docs: sync reader-digest-flow skill with Hermes version (absolute path, extract_failed handling, rerun include_read)

docs: update digest template and localize references
refactor: simplify reader digest skill
2026-08-03 09:55:19 +08:00 · 2026-07-29 10:20:20 +08:00 · 2026-07-28 19:14:09 +08:00 · 2026-07-28 18:38:54 +08:00 · 2026-07-28 16:26:34 +08:00 · 2026-07-28 16:24:31 +08:00
28 changed files with 2971 additions and 456 deletions
@@ -1,390 +1,214 @@
-# Reader MCP Workflow Service
+# Reader · AI 日报引擎

-reader 当前已经收口为面向 OpenClaw 的 MCP workflow service。正式能力边界以 FreshRSS 日报工作流为准：启动 run、写入 `run-state.json`、查询运行状态、读取结构化结果，以及异步恢复 job。CLI 与同步入口仍保留，但定位为 debug / fallback，而不是正式集成入口。
+> 从 FreshRSS 到 AI 日报的自动化流水线，为个人知识管理生成每日 AI 工程化简报。

-## 运行
+Reader 是一个端到端的 AI 日报生产系统，定时从自建 FreshRSS 的 RSS 订阅源拉取文章，经过内容提取、LLM 筛选与摘要、关键词索引构建，最终产出两个输出：
+
+1. **公开日报** — 推送到 [Hugo 站点](https://osiman.site/daily/) 的精选技术简报
+2. **知识沉淀** — 单篇结构化摘要上传到 IMA 知识库（`daily` KB）
+
+整个流程由 OpenClaw 编排，作为 MCP Workflow Service 对外暴露。
+
+---
+
+## ✨ 核心能力
+
+| 能力 | 说明 |
+|:----|:------|
+| **RSS 拉取** | 从 FreshRSS API 拉取订阅文章，支持增量读取与已读标记 |
+| **内容提取** | 自动提取文章正文、标题、来源等结构化字段 |
+| **LLM 筛选** | 基于个人兴趣画像（`filter_context.personal.json`）自动评估文章质量，分为 keep / review / drop 三档 |
+| **LLM 摘要** | 并行生成每篇文章的结构化摘要（4 路并发，约 24 秒完成 7 篇） |
+| **关键词索引** | 自动构建每日关键词索引，支持别名映射与停用词过滤 |
+| **候选简报** | 生成 `digest-brief.json` 供编排层（OpenClaw）决策 |
+| **单篇沉淀** | 对选中的文章生成结构化知识笔记，上传到 IMA 知识库 |
+| **异步 Job** | 全部生产流程走异步 job，支持恢复与状态查询 |
+
+---
+
+## 🏗 架构概览
+
+```
+FreshRSS ──→ 拉取 ──→ 内容提取 ──→ LLM 筛选 ──→ 关键词索引
+                                         │
+                                    digest-brief.json
+                                         │
+                          ┌──────────────┼──────────────┐
+                          ▼              ▼              ▼
+                     Hugo 日报      IMA 知识库      term_index
+                    (公开简报)     (单篇沉淀)       (关键词数据)
+```
+
+### MCP 工具层
+
+Reader 通过 Hermes MCP 暴露 20+ 个工具，分为三类：
+
+**日报流水线：**
+- `start_freshrss_pipeline_job` → 启动异步日报 Job
+- `get_freshrss_pipeline_job_status` / `get_freshrss_pipeline_job_result` → 轮询结果
+
+**状态查询：**
+- `get_run_status` / `get_delivery_payload` / `get_run_report` → 读取运行结果
+- `list_runs` / `list_run_artifacts` → 浏览运行历史
+
+**恢复与单篇总结：**
+- `inspect_resume_plan` / `start_resume_job` → 恢复失败 Job
+- `start_article_summary_job` / `generate_article_summaries` → 单篇文章摘要
+
+### CLI 入口
+
+同步入口，适合本地 debug / fallback：

 ```bash
+# 完整日报流水线
+python scripts/run_freshrss_pipeline.py --limit 7 --mark-read --timeout 300
+
+# 单篇文章摘要
+python scripts/run_article_summaries.py \
+  --extracted outputs/freshrss/rerun/<run_id>/extracted/item-01.extracted.json \
+  --output-dir outputs/freshrss/single_summaries/YYYY-MM-DD
+
+# 关键词维护
+python scripts/build_keyword_index.py
+python scripts/generate_term_cleanup_suggestions.py
+```
+
+---
+
+## 🚀 快速开始
+
+### 环境变量
+
+```
+# FreshRSS
+FRESHRSS_API_BASE_URL=http://127.0.0.1:8081/api/greader.php
+FRESHRSS_USERNAME=bot
+FRESHRSS_API_PASSWORD=xxx
+
+# LLM（主流水线）
+LLM_API_URL=https://api.deepseek.com
+LLM_API_KEY=xxx
+LLM_MODEL=deepseek-chat
+
+# LLM（可选，单篇摘要独立模型）
+ARTICLE_SUMMARY_LLM_API_URL=https://api.deepseek.com
+ARTICLE_SUMMARY_LLM_API_KEY=xxx
+ARTICLE_SUMMARY_LLM_MODEL=deepseek-chat
+
+# IMA 知识库（可选，仅沉淀时需要）
+IMA_DAILY_KNOWLEDGE_BASE_ID=xxx
+IMA_DAILY_KNOWLEDGE_BASE_NAME=daily
+```
+
+### 运行
+
+```bash
+# 安装
 pip install -e .
+
+# 跑日报流水线（CLI 模式）
+python scripts/run_freshrss_pipeline.py --limit 7 --mark-read --timeout 300
+
+# 启动 MCP 服务（OpenClaw 集成用）
 summary-mcp
 ```

-服务当前暴露 21 个工具。
+---

-正式集成摘要：
+## 📁 项目结构

- 主日报正式入口：`start_freshrss_pipeline_job`
- 主日报正式读取：`get_run_status`、`get_delivery_payload`、`get_run_report`
- 恢复正式入口：`inspect_resume_plan`、`start_resume_job`、`get_resume_job_status`、`get_resume_job_result`
- 单篇总结正式入口：`start_article_summary_job`、`get_article_summary_job_status`、`get_article_summary_job_result`
- `run_freshrss_openclaw_pipeline`、`resume_run`、`generate_article_summaries` 仅用于同步 debug / fallback
-
-## 文档入口
-
-如果你在做 OpenClaw 集成，不要只看这个 README，优先看：
-
- `docs/openclaw/README.md`
- `docs/openclaw/openclaw-handoff.md`
- `docs/openclaw/openclaw-orchestration-flow.md`
-
-字段契约见：
-
- `docs/openclaw/openclaw-candidate-input-field-spec.md`
- `docs/openclaw/openclaw-delivery-payload-spec.md`
-
-文档总索引见：
-
- `docs/README.md`
- `docs/current/context-reset-brief.md`
- `docs/design/README.md`
- `plans/README.md`
-
-## 正式能力边界
-
- 当前正式 workflow 只有 `freshrss_daily_digest`
- 当前生产编排默认走异步 job，而不是同步 MCP / CLI
- 每次 FreshRSS 主流水线 run 都会在 `outputs/freshrss/rerun/<run_dir>/run-state.json` 落地运行真相
- OpenClaw 正式读取结果应优先使用 MCP 返回的 `run_id`、`output_dir`、`delivery_output`、`report_output`
- 正式恢复只支持带有效 `run-state.json` 的当前 run，不处理历史推断 run
- 正式生产恢复依赖 `summary/summary-batch.json` 与 `candidates/candidate-batch.json`
-
-## OpenClaw 最短调用路径
-
-1. 调 `start_freshrss_pipeline_job`
-2. 轮询 `get_freshrss_pipeline_job_status`
-3. 成功后读 `get_freshrss_pipeline_job_result`，拿 `run_id`
-4. 用 `get_run_status`、`get_delivery_payload`、`get_run_report` 做后续读取
-5. 如需恢复，先调 `inspect_resume_plan`，只有 `recommended_action=resume` 才走 `start_resume_job`
-
-更完整的状态分支、恢复策略和人工介入条件见 `docs/openclaw/openclaw-orchestration-flow.md`。
-
-## 单篇文章总结后处理（可选使用独立 LLM）
-
-### 生产环境推荐输入
-
- 单篇总结的正式生产输入，优先使用 FreshRSS 主流水线输出的**单篇 extracted 文件**：
-  - `outputs/freshrss/rerun/<run_id>/extracted/item-XX.extracted.json`
- 这些**逐条 extracted 文件**是下游单篇总结的**正式默认产物**。
- 像 `outputs/freshrss/extracted/freshrss.extracted.json` 这样的**批量 extracted 文件**，只作为临时场景、兼容旧流程的输入形态保留，**不是首选生产默认**。
-
-### daily 知识库默认配置
-
- `IMA_DAILY_KNOWLEDGE_BASE_ID` —— 单篇日报总结默认上传的 IMA 知识库 ID
- `IMA_DAILY_KNOWLEDGE_BASE_NAME` —— 默认知识库名称（预期值：`daily`）
- 上传逻辑在运行时应先校验目标知识库；若配置的目标不存在，应先按名称查找，仍不存在则创建 `daily`
-
-相关能力：
-
- 正式路径：`start_article_summary_job` / `get_article_summary_job_status` / `get_article_summary_job_result`
- 同步 debug：`generate_article_summaries`
- CLI：`scripts/run_article_summaries.py`
- 后台 runner：`scripts/run_article_summary_job.py`
-
-## 校验 LLM 摘要结果
-
-```bash
-validate-llm-result outputs/reference/summary/result.json --extracted outputs/reference/extracted/read-flow-2026.extracted.json
+```
+reader/
+├── configs/                          # 配置
+│   ├── filter_context.personal.json  # 个人兴趣画像
+│   ├── term_aliases.json             # 关键词别名映射（149 条）
+│   ├── term_stopwords.json           # 关键词停用词（132 条）
+│   └── term_cleanup_policy.json      # 关键词清理策略
+├── src/
+│   └── summary_mcp/                  # MCP 服务核心
+│       ├── server.py                 # MCP 服务入口
+│       ├── runtime/                  # 运行时（Job 管理、状态持久化）
+│       └── workflows/                # 工作流（日报流水线逻辑）
+├── scripts/                          # CLI 入口
+├── outputs/                          # 运行时产出
+│   └── freshrss/
+│       ├── rerun/<run_id>/           # 每次运行的全量产物
+│       │   ├── candidates/           # digest-brief.json, delivery payload
+│       │   ├── extracted/            # item-XX.extracted.json
+│       │   └── run-state.json        # 运行状态
+│       └── single_summaries/         # 单篇摘要输出
+├── data/
+│   └── term_index/                   # 关键词索引数据
+│       ├── daily/YYYY-MM-DD.json
+│       └── term_stats.json
+├── docs/                             # 设计文档
+└── prompts/                          # LLM Prompt 模板
 ```

-## 跑最小 extraction → summary 循环
+---
+
+## ⚙️ 关键技术决策
+
+| 决策 | 选择 | 原因 |
+|:----|:----|:------|
+| 运行模式 | **异步 Job** 为主，CLI fallback | 避免 MCP 传输层 120s 超时限制 |
+| 摘要并发 | **ThreadPoolExecutor(max_workers=4)** | LLM 调用是 I/O 密集型，4 路并行将 7 篇摘要从 2-3 分钟压到 ~24 秒 |
+| 环境变量 | **子进程显式注入 .env** | 解决 MCP 服务器环境隔离导致子进程读取不到 LLM_API_KEY 的问题 |
+| 关键词过滤 | **别名映射 + 停用词 + 语义清洗** | 先用 `term_aliases.json` 归一化，再用 `term_stopwords.json` 过滤噪声，最后通过 LLM 做语义级清洗 |
+| Tag 选择 | **复用已有通用 Tag**，不从 term_index 翻生僻词 | 保持 Hugo 站点 /tags/ 页面整洁，避免大量一次性专有名词 |
+
+---
+
+## 🔧 关键词治理
+
+配置治理走四步流程（`scripts/` 下脚本）：

 ```bash
-python scripts/run_summary_loop.py ^
-  --extracted outputs/reference/extracted/read-flow-2026.extracted.json ^
-  --prompt outputs/prompts/llm-summary-prompt.txt ^
-  --output outputs/reference/summary/result.loop.json
+# 1. 构建评审数据包
+python skills/keyword-cleanup-review/scripts/build_review_bundle.py --days 7 --top 50
+
+# 2. 统计规则级建议（大小写、单复数、频次阈值）
+python scripts/generate_term_cleanup_suggestions.py
+
+# 3. LLM 语义级建议（中英映射、简称-全称、近义词）
+python scripts/generate_term_cleanup_semantic_suggestions.py
+
+# 4. 确认后写入配置
+python scripts/apply_term_suggestions.py --accept-watch ... --dry-run
 ```

-## 拉取 FreshRSS 条目并映射为标准化 `item`
+详见 `docs/design/keyword-engine-maintenance.md`。

-```bash
-set FRESHRSS_API_BASE_URL=http://127.0.0.1:8081/api/greader.php
-set FRESHRSS_USERNAME=bot
-set FRESHRSS_API_PASSWORD=your-api-password
-python scripts/pull_freshrss_items.py --limit 5 --mark-read
-```
+---

-默认会排除已经带 `read` 标签的条目。
-如果你想拿到完整阅读列表，可以加 `--include-read`。
-启用 `--mark-read` 后，脚本会在执行成功后把本次抓到的条目标记为已读。
+## 🤖 Agent Skill

-脚本会写出：
+Reader 附带一个完整的 OpenClaw Agent Skill，位于 `skills/reader-digest-flow/`，供 AI Agent（Hermes / Claude Code 等）编排每日日报流程使用。

- `outputs/freshrss/raw/freshrss.raw.json`
- `outputs/freshrss/items/freshrss.items.json`
+Skill 包含完整的 7 阶段工作流定义：
+1. **Phase 1** — 跑 Pipeline（FreshRSS → 提取 → LLM 筛选）
+2. **Phase 2** — 汇报候选（展示候选文章给用户决策）
+3. **Phase 3** — 用户选文（选择 Hugo 发布文章）
+4. **Phase 4** — 生成并发布 Hugo 日报
+5. **Phase 5** — 用户选 IMA 沉淀文章
+6. **Phase 6** — LLM 摘要生成
+7. **Phase 7** — IMA 知识库上传

-## 拉取 FreshRSS 条目并逐条做内容提取
+以及海量铁律（不重跑 pipeline、编号规则、Tag 选择规范、IMA 上传流程等）和参考文件（`references/` 目录）。

-```bash
-set FRESHRSS_API_BASE_URL=http://127.0.0.1:8081/api/greader.php
-set FRESHRSS_USERNAME=osiman
-set FRESHRSS_API_PASSWORD=your-api-password
-python scripts/run_freshrss_extract.py --limit 1 --mark-read
-```
+---

-默认会排除已经带 `read` 标签的条目。
-启用 `--mark-read` 后，只有提取成功的条目才会被标记为已读。
+## 📄 文档

-脚本会写出：
+- `docs/openclaw/README.md` — OpenClaw 集成指南
+- `docs/openclaw/openclaw-orchestration-flow.md` — 编排流程
+- `docs/openclaw/openclaw-delivery-payload-spec.md` — 字段契约
+- `docs/design/README.md` — 设计文档总索引
+- `docs/design/filter-rule-engine-design.md` — 过滤规则引擎设计
+- `docs/design/filter-rule-engine-usage.md` — 过滤规则使用说明

- `outputs/freshrss/raw/freshrss.raw.json`
- `outputs/freshrss/items/freshrss.items.json`
- `outputs/freshrss/extracted/freshrss.extracted.json`
+---

-注意：这个**批量 extracted 文件**主要用于独立提取场景和旧流程兼容。下游单篇总结的正式生产默认输入，仍然是 `outputs/freshrss/rerun/<run_id>/extracted/item-XX.extracted.json` 这类**逐条 extracted 文件**。
+## 📝 License

-## 跑完整 FreshRSS 流水线，并在最终 delivery payload 写盘成功后再标记已读
-
-```bash
-set FRESHRSS_API_BASE_URL=http://127.0.0.1:8081/api/greader.php
-set FRESHRSS_USERNAME=osiman
-set FRESHRSS_API_PASSWORD=your-api-password
-set LLM_API_URL=https://api.deepseek.com
-set LLM_API_KEY=your-llm-api-key
-set LLM_MODEL=deepseek-chat
-python scripts/run_freshrss_pipeline.py --limit 5 --mark-read
-```
-
-如果你希望过滤时引入个人工程兴趣 / AI Agent 兴趣画像，可以传入 context 文件：
-
-```bash
-python scripts/run_freshrss_pipeline.py ^
-  --limit 5 ^
-  --context configs/filter_context.personal.json ^
-  --mark-read
-```
-
-这条 CLI 与 MCP `run_freshrss_openclaw_pipeline` / `start_freshrss_pipeline_job` 共用同一条主流水线逻辑，但正式生产集成应优先走 async MCP job；CLI 与同步 MCP 入口仅用于本地 debug / fallback。默认会写出这些产物：
-
- `outputs/freshrss/rerun/<run_dir>/run-state.json`
- `outputs/freshrss/rerun/<run_dir>/raw/freshrss.raw.json`
- `outputs/freshrss/rerun/<run_dir>/candidates/openclaw-delivery-payload.json`
- `outputs/freshrss/rerun/<run_dir>/candidates/digest-brief.json`（给 OpenClaw 生成 public digest 用的轻量输入，仅包含 `keep` 候选）
- `outputs/freshrss/rerun/<run_dir>/run-report.json`
- `outputs/freshrss/rerun/<run_dir>/extracted/item-XX.extracted.json`（每篇一份）
-
-同时还会更新每日关键词索引运行数据：
-
- `data/term_index/daily/YYYY-MM-DD.json`
- `data/term_index/term_stats.json`
-
-主流水线默认**不会**产出批量级的 `freshrss.extracted.json`。
-如果你需要更多逐条中间产物，例如标准化 items、摘要结果、过滤决策、candidate record、candidate input，可以加 `--debug-artifacts`。
-
-当 OpenClaw 接入这个 MCP 服务后，应先通过 `start_freshrss_pipeline_job` 启动任务，轮询 `get_freshrss_pipeline_job_status`，再从 `get_freshrss_pipeline_job_result` 读取稳定的 `run_id`。拿到 `run_id` 之后，再通过 `get_run_status` / `get_delivery_payload` / `get_run_report` 读取状态与结果，而不是直接拼接目录路径。`run_freshrss_openclaw_pipeline` 仅保留为同步 debug / fallback 路径。
-在排查复杂问题时，也可以把 `debug_artifacts=true` 打开，并结合 `list_run_artifacts` 查看该 run 下实际产物。
-
-## 对结构化摘要结果执行确定性过滤规则
-
-```bash
-python scripts/run_filter_rules.py ^
-  --summary outputs/reference/summary/result.loop.json ^
-  --extracted outputs/reference/extracted/read-flow-2026.extracted.json ^
-  --output outputs/reference/filter/filter-decision.json
-```
-
-如果你希望注入兴趣主题或来源标签，也可以额外传入 context 文件：
-
-```bash
-python scripts/run_filter_rules.py ^
-  --summary outputs/reference/summary/result.loop.json ^
-  --extracted outputs/reference/extracted/read-flow-2026.extracted.json ^
-  --context outputs/reference/filter/filter-context.json ^
-  --output outputs/reference/filter/filter-decision.with-context.json
-```
-
-规则引擎设计和规则编写说明见：
-
- `docs/design/filter-rule-engine-design.md`
- `docs/design/filter-rule-engine-usage.md`
-
-## 将过滤结果写入 Markdown sink
-
-```bash
-python scripts/run_markdown_sink.py ^
-  --summary outputs/reference/summary/result.loop.json ^
-  --extracted outputs/reference/extracted/read-flow-2026.extracted.json ^
-  --filter outputs/reference/filter/filter-decision.json
-```
-
-脚本会把 Markdown 笔记写到 `knowledge-base/` 下。
-
-## 构建内部 `ArticleCandidateRecord` 与精简版 `OpenClawCandidateInput`
-
-```bash
-python scripts/run_article_candidate.py ^
-  --summary outputs/reference/summary/result.loop.json ^
-  --extracted outputs/reference/extracted/read-flow-2026.extracted.json ^
-  --filter outputs/reference/filter/filter-decision.json ^
-  --section-hint tools_and_workflows
-```
-
-默认会写出：
-
- `outputs/reference/candidates/article-candidate-record.json`
- `outputs/reference/candidates/openclaw-candidate-input.json`
-
-## 构建批量 OpenClaw delivery payload
-
-```bash
-python scripts/build_openclaw_delivery.py ^
-  --input-dir outputs/freshrss/candidates/batch ^
-  --sort-by-rank ^
-  --date 2026-03-25
-```
-
-默认会写出：
-
- `outputs/reference/candidates/openclaw-delivery-payload.json`
-
-输出目录布局说明见 `outputs/README.md`。
-
-## 关键词索引默认配置
-
-相关配置文件位于：
-
- `configs/term_aliases.json`
- `configs/term_stopwords.json`
- `configs/term_cleanup_policy.json`
- `configs/term_watchlist.json`
- `configs/term_change_log.json`
-
-你也可以基于已有 delivery payload 重新构建关键词索引：
-
-```bash
-python scripts/build_keyword_index.py ^
-  --input outputs/reference/candidates/openclaw-delivery-payload.json
-```
-
-运行期关键词数据存放在：
-
- `data/term_index/`
-
-关键词清理评审 skill 位于：
-
- `skills/keyword-cleanup-review/`
-
-构建给关键词治理流程使用的评审数据包（review bundle，临时工作文件）：
-
-```bash
-python skills/keyword-cleanup-review/scripts/build_review_bundle.py ^
-  --days 7 ^
-  --top 50 ^
-  --output outputs/term_index/review/keyword-cleanup-bundle.json
-```
-
-这个评审数据包（review bundle）会额外携带治理上下文：
-
- 来自 `configs/term_cleanup_policy.json` 的清理阈值
- 当前 watch list（`configs/term_watchlist.json`）
- 最近已应用的变更（`configs/term_change_log.json`）
-
-接下来可以把 bundle 渲染成正式建议产物（默认走确定性规则，不把 LLM 作为默认路径）：
-
-```bash
-python scripts/generate_term_cleanup_suggestions.py ^
-  --bundle outputs/term_index/review/keyword-cleanup-bundle.json
-```
-
-默认只生成：
-
- `outputs/term_index/review/term-cleanup-suggestions-YYYY-MM-DD.json`（唯一正式建议产物，建议短期保留）
-
-如需人工审阅展示稿，再显式加：
-
-```bash
-python scripts/generate_term_cleanup_suggestions.py ^
-  --bundle outputs/term_index/review/keyword-cleanup-bundle.json ^
-  --emit-markdown
-```
-
-这时才会额外生成：
-
- `outputs/term_index/review/term-cleanup-suggestions-YYYY-MM-DD.md`（临时展示稿，可按需生成，不必作为长期资产保留）
-
-其中本轮最小版本优先覆盖 `interest_keyword_suggestions` 和 `watch_terms` 主链路；`alias_suggestions` / `stopword_suggestions` 先保持保守。
-
-产物保留策略建议：
-
- `data/term_index/daily/YYYY-MM-DD.json`、`data/term_index/term_stats.json` 作为事实层长期保留
- `configs/filter_context.personal.json`、`configs/term_watchlist.json`、`configs/term_aliases.json`、`configs/term_stopwords.json`、`configs/term_change_log.json` 作为状态层长期保留
- `term-cleanup-suggestions-YYYY-MM-DD.json` 作为正式建议产物短期保留（最近少量几份或仅保留已应用过的）
- `keyword-cleanup-bundle.json` 仅作为临时工作文件，默认只保留当前最新一份
- `term-cleanup-suggestions-YYYY-MM-DD.md` 仅作为临时展示稿，优先按需生成，不建议默认长期归档
-
-如果你想先预览已接受建议，再决定是否写配置文件：
-
-```bash
-python scripts/apply_term_suggestions.py ^
-  --suggestions outputs/term_index/review/term-cleanup-suggestions-YYYY-MM-DD.json ^
-  --accept-watch Cron Heartbeat Memory ^
-  --dry-run
-```
-
-去掉 `--dry-run` 后才会真正写文件。
-这个脚本也支持通过 `--accept-alias`、`--accept-stopword`、`--accept-interest` 应用 alias / stopword / interest keyword 变更。
-已接受的 watch 词会写入 `configs/term_watchlist.json`，每次应用动作也会被追加到 `configs/term_change_log.json`。
-
-## 单篇总结 LLM 配置
-
-如果你希望单篇总结后处理使用独立模型，而不影响主流水线，可以设置：
-
- `ARTICLE_SUMMARY_LLM_API_URL`
- `ARTICLE_SUMMARY_LLM_MODEL`
- `ARTICLE_SUMMARY_LLM_API_KEY`
-
-如果这些变量未设置，单篇总结会回退使用主流程中的 `LLM_*` / `OPENAI_*` 配置。
-如果显式指定 DeepSeek 作为单篇总结模型且请求超时或失败，当前实现会自动再用主流程默认模型配置重试一次。
-
-示例（PowerShell 风格）：
-
-```bash
-set ARTICLE_SUMMARY_LLM_API_URL=https://api.deepseek.com
-set ARTICLE_SUMMARY_LLM_API_KEY=your-article-summary-key
-set ARTICLE_SUMMARY_LLM_MODEL=deepseek-chat
-```
-
-然后可以这样调用 CLI。
-正式生产环境建议优先使用 `outputs/freshrss/rerun/<run_id>/extracted/` 下的**逐条 extracted 文件**；下面这个**批量 extracted** 示例仅保留为兼容旧流程 / 临时场景输入：
-
-```bash
-python scripts/run_article_summaries.py ^
-  --extracted outputs/freshrss/extracted/freshrss.extracted.json ^
-  --ids 12345 67890 ^
-  --output-dir outputs/freshrss/single_summaries ^
-  --timeout 120
-```
-
-也可以通过 `summary_mcp.server` 暴露的 MCP 工具 `generate_article_summaries` 调用：
-
- `extracted_path`（string）：单篇 extracted JSON 路径（例如 `outputs/freshrss/rerun/<run_id>/extracted/item-01.extracted.json`），或者包含 `results` 数组的 batch extracted JSON
- `selected_ids`（array of strings）：必填，至少传一个 `item_id`；如果传入的 ID 在 extracted payload 中一个都匹配不到，会直接报错
- `output_dir`（optional string）：Markdown 输出目录；若不传，则默认写到 extracted 文件旁边的 `single_summaries/` 目录
- `llm_api_key` / `llm_model` / `llm_api_url`（optional strings）：单篇总结 LLM 的覆盖配置；不传时会按前文规则回退到 `ARTICLE_SUMMARY_*` 或主 `LLM_*`
-
-该工具返回一个 JSON 数组，内容为生成好的 Markdown 文件路径。
-
-OpenClaw / 正式集成建议优先走异步 job：
-
- 调 `start_article_summary_job` 启动任务，立即拿到 `job_id`
- 轮询 `get_article_summary_job_status(job_id)`，直到 `status` 进入 `success` 或 `failed`
- 成功后调用 `get_article_summary_job_result(job_id)` 读取 `written_paths` 与结构化结果
- 失败时优先查看 `error_summary` 与 job 目录中的 `job-report.json`
-
-异步 job 状态目录固定落在 `outputs/freshrss/article_summary_jobs/<job_id>/`，最小会包含：
-
- `run-state.json`
- `input.json`
- `result.json`（成功时）
- `job-report.json`
-
-单篇总结使用独立 prompt：`outputs/prompts/article-summary-prompt.txt`。
-它与日报 prompt 完全独立，输出的是中文结构化知识笔记，包含这些部分：
-
- 核心结论
- 主要论点
- 关键方法 / 机制
- 重要细节
- 可复用启发
- 关键词
- 主题
+MIT
@@ -320,6 +320,23 @@

 ---

+### [DONE][P1] interest/watch 候选引擎从固定阈值改为百分位排名 + 增速因子
+
+目标：
+- 解决固定阈值（total_count>=3）不随数据量自适应的问题
+- 引入趋势信号（growth 因子），识别近期集中爆发的词
+- 支持 7 天、41 天、200 天数据量下取同样的 top 5%/5%-20% 而不需调阈值
+
+要求：
+- `build_review_bundle.py`：新增 percentile 和 growth 计算函数；候选池从固定阈值改为百分位 + 增速
+- `configs/term_cleanup_policy.json`：升级为 v2 schema，percentile/growth 替代绝对阈值
+- 不改 `generate_term_cleanup_suggestions.py` 和 `apply_term_suggestions.py`
+- 全量跑一次对比新旧产出，确认差异合理
+
+方案文档：`plans/keyword-cleanup-interest-watch-engine-improvement.md`
+
+---
+
 ### [DONE][P3] 更新 README / handoff / docs，明确 MCP 为正式入口

 目标：
@@ -27,18 +27,30 @@
    "Agent Skills",
    "AgentScope",
    "AI Agent",
+    "AI Coding Agent",
    "AliSQL",
+    "Anthropic",
+    "Claude",
    "Claude Code",
+    "CLAUDE.md",
+    "CLI",
+    "Context Engineering",
+    "Cursor",
+    "ChatGPT",
    "DeepSeek",
    "FastAPI",
    "Gin",
    "Go",
    "gRPC",
+    "Harness Engineering",
+    "Hermes Agent",
    "Java",
    "Kafka",
    "Kubernetes",
    "LLM",
+    "Loop Engineering",
    "MCP",
+    "MoE",
    "MySQL",
    "MySQL复制延迟",
    "OpenAI",
@@ -47,15 +59,30 @@
    "Prompt Engineering",
    "Python",
    "RAG",
+    "ReAct",
    "ReActAgent",
    "Redis",
+    "Skill",
+    "SKILL.md",
+    "Skills",
    "Spring",
    "SubAgent",
+    "TypeScript",
+    "Vibe Coding",
    "Workflow",
+    "上下文压缩",
+    "上下文工程",
+    "上下文管理",
    "云原生",
+    "代码审查",
    "可观测性",
    "向量数据库",
+    "多Agent协作",
+    "大模型",
+    "子Agent",
+    "强化学习",
    "微服务",
+    "渐进式披露",
    "知识库"
  ]
 }
@@ -1,5 +1,149 @@
 {
-  "AI助手": "AI Agent",
+  "Agent": "Agent",
+  "Agent框架": "Agent",
+  "Agent能力": "Agent Skills",
+  "智能体": "AI Agent",
+  "Agentic架构": "Agentic架构",
+  "多Agent协作": "多Agent协作",
+  "多智能体架构": "多Agent协作",
+  "Multi-Agent": "多Agent",
+  "Subagent": "子Agent",
+  "Sub Agents验证": "子Agent",
+  "子Agent": "子Agent",
+  "子智能体": "子Agent",
+  "Coding Agent": "AI Coding Agent",
+  "AI编程": "AI Coding Agent",
+  "AI辅助编程": "AI Coding Agent",
+  "代码生成": "AI代码生成",
+  "代码审查": "Code Review",
+  "Prompt": "Prompt Engineering",
+  "Prompt Caching": "提示缓存",
+  "RAG": "RAG",
  "图文RAG": "RAG",
-  "Prompt架构": "Prompt Engineering"
+  "向量检索": "向量检索",
+  "向量嵌入": "向量嵌入",
+  "Multi-Token Prediction": "多Token预测",
+  "Pair-In Pair-Out": "PIPO架构",
+  "PIPO": "PIPO架构",
+  "上下文管理": "上下文管理",
+  "上下文卸载": "上下文卸载",
+  "Self-GC": "上下文压缩",
+  "记忆管理": "上下文管理",
+  "会话管理": "上下文管理",
+  "Harness Engineering": "Harness工程化",
+  "Harness架构": "Harness工程化",
+  "Harness": "Harness工程化",
+  "Loop Engineering": "Loop Engineering",
+  "推理加速": "推理加速",
+  "推理深度": "推理深度",
+  "长链路推理": "长链路推理",
+  "RLVR": "RLVR",
+  "GRPO": "GRPO",
+  "强化学习": "强化学习",
+  "Multi-Agent RL": "多Agent强化学习",
+  "Viking AI搜索": "AI搜索",
+  "Viking AI Search": "AI搜索",
+  "智能搜索": "AI搜索",
+  "SearchCLI": "CLI搜索",
+  "视频生成": "AI视频生成",
+  "视频生成模型": "AI视频生成",
+  "LingBot-Video": "AI视频生成",
+  "视觉自回归模型": "AI视频生成",
+  "火山云数据库PostgreSQL Serverless版": "Serverless数据库",
+  "PostgreSQL": "PostgreSQL",
+  "MySQL": "MySQL",
+  "OceanBase": "OceanBase",
+  "StarRocks": "StarRocks",
+  "Milvus": "Milvus",
+  "Seal AI Zone": "AI安全",
+  "NEX沙箱": "沙箱隔离",
+  "MicroVM": "沙箱隔离",
+  "安全左移": "安全左移",
+  "安全中台": "AI安全",
+  "成本降低": "成本优化",
+  "成本杠杆": "成本优化",
+  "Scale-to-Zero": "弹性伸缩",
+  "Data as Git": "数据分支管理",
+  "Schema Diff": "Schema对比",
+  "Time Travel": "数据回溯",
+  "多端架构": "多端架构",
+  "契约化": "契约化架构",
+  "大仓": "大仓工程化",
+  "Vibe Coding": "Vibe Coding",
+  "LLM Judge": "LLM评估",
+  "SWE-Bench": "SWE-Bench",
+  "SWE Bench Pro": "SWE-Bench",
+  "SWE-Bench Pro": "SWE-Bench",
+  "Verification Agent": "验证Agent",
+  "CLI工具": "CLI",
+  "CLI": "CLI",
+  "漏桶算法": "限流架构",
+  "固定窗口限流": "限流架构",
+  "Suspend消费控制": "限流架构",
+  "RocketMQ LiteTopic": "消息队列",
+  "LLM Wiki": "LLM知识库",
+  "知识工程": "知识工程",
+  "语义资产": "语义资产管理",
+  "知识图谱": "知识图谱",
+  "知识库沉淀": "知识管理",
+  "Skill": "Skill",
+  "Skill Hub": "技能生态",
+  "具身智能": "具身智能",
+  "Open X-Embodiment": "具身智能",
+  "YOLO Classifier": "目标检测",
+  "MCP": "MCP",
+  "MCP连接器": "MCP",
+  "缓存击穿": "缓存优化",
+  "GPU算力调度": "算力调度",
+  "异构资源": "异构计算",
+  "XPU": "异构计算",
+  "弹性RDMA": "RDMA网络",
+  "国内主流GPU": "国产芯片",
+  "国产AI芯片": "国产芯片",
+  "Paxos协议": "分布式一致性",
+  "Token": "Token管理",
+  "Token效率": "Token管理",
+  "百万token上下文": "长上下文",
+  "MoE": "MoE架构",
+  "MoE架构": "MoE架构",
+  "思维链": "思维链",
+  "CoT Distillation": "思维链蒸馏",
+  "自然语言驱动": "自然语言交互",
+  "NL2SQL": "NL2SQL",
+  "AI对齐": "AI对齐",
+  "注意力机制": "注意力机制",
+  "多模态": "多模态",
+  "音视频工作台": "音视频处理",
+  "AI助手": "AI Agent",
+  "Agent架构": "AI Agent",
+  "Agent专业化": "AI Agent",
+  "Agent Teams": "多Agent协作",
+  "Agentic Engineering": "AI Agent",
+  "AI智能体": "AI Agent",
+  "LLM Agent": "AI Agent",
+  "AI Harness": "Harness Engineering",
+  "AI代码生成": "AI Coding Agent",
+  "Memory管理": "上下文管理",
+  "Agent Skill": "Agent Skills",
+  "Binlog": "binlog",
+  "vibe coding": "Vibe Coding",
+  "Agent组织化协作平台": "Agent协作平台",
+  "Anthropic": "Anthropic",
+  "OpenClaw": "OpenClaw",
+  "WorkBuddy": "WorkBuddy",
+  "Claude": "Claude",
+  "ChatGPT": "ChatGPT",
+  "GPT": "GPT",
+  "Opus": "Opus",
+  "Sonnet": "Sonnet",
+  "Grok": "Grok",
+  "Qwen": "Qwen",
+  "GLM": "GLM",
+  "Claude Code": "Claude Code",
+  "Cursor": "Cursor",
+  "Codex": "Codex",
+  "Pi": "Pi",
+  "CoT": "CoT",
+  "SVG": "SVG",
+  "TTS": "TTS"
 }
@@ -99,6 +99,497 @@
      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-04-08.json",
      "suggestion_date": "2026-04-08",
      "based_on_days": 7
+    },
+    {
+      "applied_at": "2026-05-14T08:20:35.134979Z",
+      "action": "add_interest_keyword",
+      "term": "Anthropic",
+      "reason": "Meets the configured interest-keyword review threshold and is not yet covered by interest keywords or stopwords. Evidence: total_count=13, days_seen=10, recent_count=13.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T08:20:35.134979Z",
+      "action": "add_interest_keyword",
+      "term": "Harness Engineering",
+      "reason": "Meets the configured interest-keyword review threshold and is not yet covered by interest keywords or stopwords. Evidence: total_count=12, days_seen=11, recent_count=12.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T08:20:35.134979Z",
+      "action": "add_interest_keyword",
+      "term": "Skill",
+      "reason": "Meets the configured interest-keyword review threshold and is not yet covered by interest keywords or stopwords. Evidence: total_count=11, days_seen=9, recent_count=11.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T08:20:35.134979Z",
+      "action": "add_interest_keyword",
+      "term": "上下文工程",
+      "reason": "Meets the configured interest-keyword review threshold and is not yet covered by interest keywords or stopwords. Evidence: total_count=6, days_seen=6, recent_count=6.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T08:20:35.134979Z",
+      "action": "add_interest_keyword",
+      "term": "多Agent协作",
+      "reason": "Meets the configured interest-keyword review threshold and is not yet covered by interest keywords or stopwords. Evidence: total_count=6, days_seen=6, recent_count=6.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T08:20:35.134979Z",
+      "action": "add_interest_keyword",
+      "term": "Claude",
+      "reason": "Meets the configured interest-keyword review threshold and is not yet covered by interest keywords or stopwords. Evidence: total_count=6, days_seen=5, recent_count=6.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T08:20:35.134979Z",
+      "action": "add_interest_keyword",
+      "term": "上下文管理",
+      "reason": "Meets the configured interest-keyword review threshold and is not yet covered by interest keywords or stopwords. Evidence: total_count=6, days_seen=5, recent_count=6.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T08:20:35.134979Z",
+      "action": "add_interest_keyword",
+      "term": "渐进式披露",
+      "reason": "Meets the configured interest-keyword review threshold and is not yet covered by interest keywords or stopwords. Evidence: total_count=5, days_seen=5, recent_count=5.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T08:20:35.134979Z",
+      "action": "add_interest_keyword",
+      "term": "Skills",
+      "reason": "Meets the configured interest-keyword review threshold and is not yet covered by interest keywords or stopwords. Evidence: total_count=5, days_seen=4, recent_count=5.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T08:20:35.134979Z",
+      "action": "add_interest_keyword",
+      "term": "SKILL.md",
+      "reason": "Meets the configured interest-keyword review threshold and is not yet covered by interest keywords or stopwords. Evidence: total_count=4, days_seen=4, recent_count=4.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T08:20:35.134979Z",
+      "action": "add_interest_keyword",
+      "term": "CLAUDE.md",
+      "reason": "Meets the configured interest-keyword review threshold and is not yet covered by interest keywords or stopwords. Evidence: total_count=4, days_seen=4, recent_count=4.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T08:20:35.134979Z",
+      "action": "add_interest_keyword",
+      "term": "上下文压缩",
+      "reason": "Meets the configured interest-keyword review threshold and is not yet covered by interest keywords or stopwords. Evidence: total_count=4, days_seen=4, recent_count=4.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T08:20:35.134979Z",
+      "action": "add_interest_keyword",
+      "term": "AI Coding Agent",
+      "reason": "Meets the configured interest-keyword review threshold and is not yet covered by interest keywords or stopwords. Evidence: total_count=3, days_seen=3, recent_count=3.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T08:20:35.134979Z",
+      "action": "add_interest_keyword",
+      "term": "Hermes Agent",
+      "reason": "Meets the configured interest-keyword review threshold and is not yet covered by interest keywords or stopwords. Evidence: total_count=3, days_seen=3, recent_count=3.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T08:20:35.134979Z",
+      "action": "add_interest_keyword",
+      "term": "Vibe Coding",
+      "reason": "Meets the configured interest-keyword review threshold and is not yet covered by interest keywords or stopwords. Evidence: total_count=5, days_seen=4, recent_count=5.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T08:20:35.134979Z",
+      "action": "add_interest_keyword",
+      "term": "Context Engineering",
+      "reason": "Meets the configured interest-keyword review threshold and is not yet covered by interest keywords or stopwords. Evidence: total_count=3, days_seen=3, recent_count=3.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T08:20:35.134979Z",
+      "action": "add_interest_keyword",
+      "term": "Cursor",
+      "reason": "Meets the configured interest-keyword review threshold and is not yet covered by interest keywords or stopwords. Evidence: total_count=3, days_seen=3, recent_count=3.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T08:20:35.134979Z",
+      "action": "add_interest_keyword",
+      "term": "大模型",
+      "reason": "Meets the configured interest-keyword review threshold and is not yet covered by interest keywords or stopwords. Evidence: total_count=4, days_seen=3, recent_count=4.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T09:12:50.749877Z",
+      "action": "add_interest_keyword",
+      "term": "TypeScript",
+      "reason": "Core language for AI agent development (e.g., Claude Code, Cursor) and backend engineering, complements existing Python/Java/Go keywords.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T09:12:50.749877Z",
+      "action": "add_interest_keyword",
+      "term": "代码审查",
+      "reason": "Chinese term for 'code review', a key practice in backend engineering and AI agent development workflows.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T09:12:50.749877Z",
+      "action": "add_stopword",
+      "term": "Channels",
+      "reason": "Too generic; could refer to communication channels, YouTube channels, or software channels, not specific to user's focus areas.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T09:12:50.749877Z",
+      "action": "add_stopword",
+      "term": "Memory",
+      "reason": "Extremely broad term; could refer to computer memory, human memory, or memory in various contexts, not discriminative enough.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T09:12:50.749877Z",
+      "action": "add_stopword",
+      "term": "Prompt",
+      "reason": "Already covered by 'Prompt Engineering' as a more specific term; 'Prompt' alone is too broad and matches many unrelated articles.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T09:12:50.749877Z",
+      "action": "add_stopword",
+      "term": "AGI",
+      "reason": "Too broad and speculative; not directly actionable for the user's practical engineering focus areas.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T09:12:50.749877Z",
+      "action": "add_stopword",
+      "term": "AI日报",
+      "reason": "Generic news term; not a technical concept or tool, would add noise to the keyword index.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T09:12:50.749877Z",
+      "action": "add_stopword",
+      "term": "AIHOT",
+      "reason": "Unclear meaning, likely a brand or aggregator, not a specific technical term.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T09:12:50.749877Z",
+      "action": "add_stopword",
+      "term": "All In Code",
+      "reason": "Too vague; could refer to a podcast, a philosophy, or a project, not a specific technical concept.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T09:12:50.749877Z",
+      "action": "add_stopword",
+      "term": "auto-twitter-campaign",
+      "reason": "Too specific to a single project/tool, not a general interest keyword for the user's focus areas.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T09:12:50.749877Z",
+      "action": "add_stopword",
+      "term": "ChangeSet",
+      "reason": "Generic term used in version control and databases; too broad to be a useful filter.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T09:12:50.749877Z",
+      "action": "add_stopword",
+      "term": "Lumina",
+      "reason": "Unclear reference; could be a product, framework, or brand, not clearly aligned with user's focus.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T09:12:50.749877Z",
+      "action": "add_stopword",
+      "term": "OpenViking",
+      "reason": "Unclear reference; not a known tool or concept in the user's stated focus areas.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T09:12:50.749877Z",
+      "action": "add_stopword",
+      "term": "Seedance 2.0",
+      "reason": "Unclear reference; likely a product or version, not a general technical term.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T09:12:50.749877Z",
+      "action": "add_stopword",
+      "term": "质量门禁",
+      "reason": "Chinese term for 'quality gate', too generic in software engineering; not specific to user's focus areas.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T09:12:50.749877Z",
+      "action": "add_alias",
+      "term": "Agent Skill",
+      "reason": "Singular variant",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "value": "Agent Skills",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T09:12:50.749877Z",
+      "action": "add_alias",
+      "term": "Binlog",
+      "reason": "Case variant (auto-ranked)",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "value": "binlog",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T09:12:50.749877Z",
+      "action": "add_alias",
+      "term": "Coding Agent",
+      "reason": "Abbreviated form of 'AI Coding Agent', referring to the same concept.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "value": "AI Coding Agent",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T09:12:50.749877Z",
+      "action": "add_alias",
+      "term": "Subagent",
+      "reason": "Case variant",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "value": "SubAgent",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T09:12:50.749877Z",
+      "action": "add_alias",
+      "term": "Subagents",
+      "reason": "Plural variant",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "value": "SubAgent",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T09:12:50.749877Z",
+      "action": "add_alias",
+      "term": "vibe coding",
+      "reason": "Case variant",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "value": "Vibe Coding",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T09:12:50.749877Z",
+      "action": "add_alias",
+      "term": "Agent架构",
+      "reason": "Chinese translation of 'Agent architecture', a core concept in AI Agent engineering.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "value": "AI Agent",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T09:12:50.749877Z",
+      "action": "add_alias",
+      "term": "Agent专业化",
+      "reason": "Chinese term for 'Agent specialization', directly related to Agent engineering.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "value": "AI Agent",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T09:12:50.749877Z",
+      "action": "add_alias",
+      "term": "Agent Teams",
+      "reason": "English equivalent of 'Multi-Agent collaboration', same concept.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "value": "多Agent协作",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T09:12:50.749877Z",
+      "action": "add_alias",
+      "term": "Agentic Engineering",
+      "reason": "Broader term for engineering with AI agents, closely related to Agent engineering focus.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "value": "AI Agent",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T09:12:50.749877Z",
+      "action": "add_alias",
+      "term": "CLI工具",
+      "reason": "Chinese translation of 'CLI tool', same concept.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "value": "CLI",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T09:12:50.749877Z",
+      "action": "add_alias",
+      "term": "AI编程",
+      "reason": "Chinese term for 'AI programming', closely related to AI Coding Agent.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "value": "AI Coding Agent",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T09:12:50.749877Z",
+      "action": "add_alias",
+      "term": "记忆管理",
+      "reason": "Chinese term for 'memory management', closely related to context management in LLM applications.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "value": "上下文管理",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-05-14T09:12:50.749877Z",
+      "action": "add_alias",
+      "term": "会话管理",
+      "reason": "Chinese term for 'session management', related to context management in LLM applications.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-05-14.json",
+      "value": "上下文管理",
+      "suggestion_date": "2026-05-14",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-07-15T02:17:50.155586Z",
+      "action": "add_interest_keyword",
+      "term": "强化学习",
+      "reason": "top 0.8% by frequency，growth=100%，not yet covered by interest keywords or stopwords. Evidence: total_count=10, days_seen=10, recent_count=10.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-07-15.json",
+      "suggestion_date": "2026-07-15",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-07-15T02:17:50.155586Z",
+      "action": "add_interest_keyword",
+      "term": "ReAct",
+      "reason": "top 1.1% by frequency，growth=100%，not yet covered by interest keywords or stopwords. Evidence: total_count=8, days_seen=8, recent_count=8.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-07-15.json",
+      "suggestion_date": "2026-07-15",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-07-15T02:17:50.155586Z",
+      "action": "add_interest_keyword",
+      "term": "CLI",
+      "reason": "top 1.1% by frequency，growth=100%，not yet covered by interest keywords or stopwords. Evidence: total_count=8, days_seen=7, recent_count=8.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-07-15.json",
+      "suggestion_date": "2026-07-15",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-07-15T02:17:50.155586Z",
+      "action": "add_interest_keyword",
+      "term": "子Agent",
+      "reason": "top 1.3% by frequency，growth=100%，not yet covered by interest keywords or stopwords. Evidence: total_count=7, days_seen=6, recent_count=7.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-07-15.json",
+      "suggestion_date": "2026-07-15",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-07-15T02:17:50.155586Z",
+      "action": "add_interest_keyword",
+      "term": "Loop Engineering",
+      "reason": "top 1.5% by frequency，growth=100%，not yet covered by interest keywords or stopwords. Evidence: total_count=6, days_seen=6, recent_count=6.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-07-15.json",
+      "suggestion_date": "2026-07-15",
+      "based_on_days": 365
+    },
+    {
+      "applied_at": "2026-07-15T02:17:50.155586Z",
+      "action": "add_interest_keyword",
+      "term": "MoE",
+      "reason": "top 2.0% by frequency，growth=100%，not yet covered by interest keywords or stopwords. Evidence: total_count=5, days_seen=4, recent_count=5.",
+      "suggestions_path": "outputs/term_index/review/term-cleanup-suggestions-2026-07-15.json",
+      "suggestion_date": "2026-07-15",
+      "based_on_days": 365
    }
  ]
 }
@@ -1,14 +1,12 @@
 {
-  "schema_version": "v1",
+  "schema_version": "v2",
  "interest_keyword_review": {
-    "min_total_count": 3,
-    "min_days_seen": 2
+    "percentile_max": 0.05,
+    "growth_promotion": 0.5
  },
  "watch_term_review": {
-    "min_total_count": 1,
-    "min_days_seen": 1,
-    "max_total_count": 2,
-    "max_days_seen": 2
+    "percentile_min": 0.05,
+    "percentile_max": 0.20
  },
  "alias_review": {
    "min_total_count": 2,
@@ -19,7 +17,10 @@
    "max_days_seen": 2
  },
  "notes": [
-    "当前阶段采用保守阈值，避免在低样本条件下直接扩充 interest_keywords。",
-    "watch_terms 先用于观察，后续再决定是否升格为 interest_keywords 或进入 alias/stopword 配置。"
+    "v2: interest/watch 使用百分位排名 + 增速因子替代固定阈值",
+    "percentile 越小表示排名越高（top 5% = percentile 0.05）",
+    "growth = recent_count / total_count，衡量近期活跃度",
+    "watch_term_review 的 percentile_min 可理解为兴趣边界下限，低于此值的词归入 interest 候选",
+    "growth_promotion（默认 0.5）用于识别近期集中爆发词，即使排位不高也主动推荐确认"
  ]
 }
@@ -1,6 +1,126 @@
 [
+  "1688",
+  "AGI",
+  "AIHOT",
+  "AI日报",
+  "All In Code",
+  "Andrej Karpathy",
+  "Anthropic",
+  "auto-twitter-campaign",
+  "Boundaries",
+  "ChangeSet",
+  "Channels",
+  "Claude Fable 5",
+  "Claude Mythos",
+  "Cohere",
+  "Confidence Head",
+  "Cosmos 3",
+  "Databricks",
+  "DINOv2",
+  "domain-mapping",
+  "Dropbox",
+  "EchoGen",
+  "FLUX.1-dev VAE",
+  "GB300 GPU",
+  "GLM 5.2",
+  "GLM5.0",
+  "GPT-5.5",
+  "GPT-Live",
+  "GPT5.5",
+  "Grok 4.5",
+  "GrowBrain",
+  "iMedImage",
+  "iMedLoop",
+  "iMedMaaS",
+  "iMedStudio",
+  "J-space",
+  "JLens",
+  "John Jumper",
+  "J空间",
+  "KAIROS",
+  "KubeRay",
+  "LibTV Agent",
+  "LingBot-Video",
+  "Lumina",
+  "Markdown",
+  "Marvis",
+  "MDASH",
+  "Meta Superintelligence Labs",
+  "MTS",
+  "Muse Image",
+  "Muse Video",
+  "N-gram Embedding",
+  "OCP China",
+  "OCP China 2026",
+  "On-Policy Distillation",
+  "OPC训练营",
+  "OpenAI",
+  "OpenBMC",
+  "OpenClaw",
+  "OpenViking",
+  "Opus 4.8",
+  "Qwen3",
+  "Qwen3-30B-A3B",
+  "RAS API",
+  "Redfish",
+  "ScMoE",
+  "Seal AI Zone",
+  "SealRouter",
+  "Seedance 2.0",
+  "Sonnet 5",
+  "Spec模式",
+  "STE固件团队",
+  "Three.js",
+  "Unity AI Gateway",
+  "Vant Weapp",
+  "WeTV",
+  "WorkBuddy",
+  "wpc",
+  "YOLO Classifier",
+  "一人公司",
+  "中国科学技术大学",
+  "五大扶持体系",
+  "出门问问",
+  "分镜",
+  "剧本",
  "奋斗文化",
+  "字节跳动",
  "小银",
+  "得力",
+  "德适科技",
+  "成都天府长岛",
+  "扣子",
+  "星云平台",
+  "火山引擎",
+  "百度百舸",
+  "百炼网关",
+  "科大讯飞",
+  "腾讯云开发者社区",
+  "腾讯混元Hy3",
+  "蚂蚁灵波",
+  "贝尔实验室",
+  "质量门禁",
+  "配乐",
+  "配音",
  "银行客户经理",
-  "飞盘物理"
+  "飞书妙搭",
+  "飞盘物理",
+  "自动化",
+  "定时任务",
+  "开源模型",
+  "陌生化",
+  "AlphaFold",
+  "Brand Kit",
+  "DataWorks",
+  "Enhance-Nanocodec",
+  "IRIS Codec",
+  "Lovart",
+  "MiniMax M3",
+  "Gemini 3.5 Flash",
+  "Codex",
+  "CodeBuddy",
+  "Claude Cowork",
+  "AGENTS.md",
+  "Claude",
+  "RLVR"
 ]
@@ -1,6 +1,6 @@
 {
  "schema_version": "v1",
-  "updated_at": "2026-04-08T02:34:14.194320Z",
+  "updated_at": "2026-07-15T02:17:50.155586Z",
  "terms": [
    {
      "term": "A2A",
@@ -0,0 +1,151 @@
+# 关键词清洗流程概述
+
+> 2026-05-14 初版
+> 从"数据记录"到"人工确认落盘"的完整链路
+
+---
+
+## 整体数据流
+
+```
+每日日报 pipeline
+     │
+     ▼
+term_index/daily/YYYY-MM-DD.json    ← 每天一篇候选文章的热词统计
+     │
+     ▼
+term_index/term_stats.json          ← 所有 daily 的汇总（1070 个词）
+     │
+     ├──── build_review_bundle.py    ← 打包为审查数据包
+     │         │
+     │         ▼
+     │    review/keyword-cleanup-bundle.json
+     │         │
+     │         ▼
+     │    generate_term_cleanup_suggestions.py
+     │         │
+     │         ▼
+     │    review/term-cleanup-suggestions-YYYY-MM-DD.json  ← 正式建议产物
+     │         │
+     │         ▼
+     │    (可选) review/term-cleanup-suggestions-YYYY-MM-DD.md  ← 展示稿
+     │
+     ├──── 人工确认哪些建议 accept
+     │
+     ▼
+apply_term_suggestions.py           ← 写入配置
+     │
+     ├── configs/filter_context.personal.json  ← interest_keywords
+     ├── configs/term_aliases.json             ← alias
+     ├── configs/term_stopwords.json           ← stopword
+     ├── configs/term_watchlist.json           ← watch
+     └── configs/term_change_log.json          ← 变更日志
+```
+
+---
+
+## 各环节说明
+
+### 阶段 1：数据记录（每日自动）
+
+```bash
+# FreshRSS pipeline 跑完后自动产出
+data/term_index/daily/2026-05-14.json
+```
+
+- 每天一篇，记录当天候选文章中出现的热词
+- 包含 term、total_count、days_seen 等信息
+- 目前累计 **41 天**，共 **1070 个独立词**
+
+### 阶段 2：全量汇总（每日自动）
+
+```bash
+data/term_index/term_stats.json
+```
+
+- 从所有 daily 文件重建，会覆盖重跑
+- 按 total_count 排序，前 5 名：OpenClaw(30)、Claude Code(25)、AI Agent(17)、Anthropic(13)、MCP(13)
+
+### 阶段 3：构建审查数据包（手动触发）
+
+```bash
+python skills/keyword-cleanup-review/scripts/build_review_bundle.py \
+  --days 365 \
+  --top 100 \
+  --output outputs/term_index/review/keyword-cleanup-bundle.json
+```
+
+- 把 term_stats + 当前配置打成一包，方便后续处理
+- 输出：`review/keyword-cleanup-bundle.json`
+
+### 阶段 4：生成建议（手动触发）
+
+```bash
+python scripts/generate_term_cleanup_suggestions.py \
+  --bundle outputs/term_index/review/keyword-cleanup-bundle.json \
+  --emit-markdown
+```
+
+#### 当前产出能力
+
+| 建议类型 | 状态 | 当前阈值 | 说明 |
+|---------|------|----------|------|
+| interest_keyword_suggestions | ✅ **已实现** | total≥3, days≥2 | 产出 20 条 |
+| watch_terms | ✅ **已实现** | total≤2, days≤2 | 本次 0 条 |
+| alias_suggestions | ❌ **硬编码为空** | policy 有阈值（total≥2, days≥2）但脚本未实现 | |
+| stopword_suggestions | ❌ **硬编码为空** | policy 有阈值（total≤2, days≤2）但脚本未实现 | |
+
+**关键发现：** alias 和 stopword 不是"阈值太保守"，是 **generate 脚本里压根没写对应的生成函数**。policy 文件里阈值已经配好了（alias: min_total=2/min_days=2，stopword: max_total=2/max_days=2），但脚本第 376-380 行直接硬编码为 `[]` 和 `0`。
+
+### 阶段 5：人工确认（手动）
+
+```
+OpenClaw 把建议列给你 → 你确认哪些 accept → 我执行 apply
+```
+
+本次模式：
+- 高频（≥5次/5天以上）→ 强烈推荐 ✅
+- 中频（3-4次）→ 附带建议 ✅
+- 泛词 → 建议跳过 ❌
+
+### 阶段 6：落盘配置（手动）
+
+```bash
+python scripts/apply_term_suggestions.py \
+  --suggestions outputs/term_index/review/term-cleanup-suggestions-YYYY-MM-DD.json \
+  --accept-interest 词1 词2 ...
+```
+
+- dry-run 预览 → 确认后正式 apply
+- 写入 `configs/filter_context.personal.json`
+- 同步记录到 `term_change_log.json`
+- **不备份原始配置**（待优化）
+- **apply 后不自动清理 review 目录**（待优化）
+
+### 阶段 7：维护清理（按需）
+
+由 OpenClaw 侧 `reader-keyword-maintenance` skill 处理：
+- 删除旧 markdown 展示稿
+- 保留最近一份 bundle
+- 保守保留 suggestions JSON
+
+---
+
+## 当前配置资产
+
+| 文件 | 内容 | 数据量 |
+|------|------|--------|
+| `filter_context.personal.json` | interest_keywords | 52 个 |
+| `term_aliases.json` | 别名映射 | 0 组（未启用） |
+| `term_stopwords.json` | 停用词 | 0 个（未启用） |
+| `term_watchlist.json` | 观察词 | 6 个 |
+| `term_change_log.json` | 所有变更记录 | 已记录 |
+
+---
+
+## 待优化项
+
+1. **alias/stopword 建议生成为空** — generate 脚本硬编码缺实现，policy 已有阈值，需要补函数
+2. **apply 前无配置备份** — 建议 apply 前自动 cp 备份
+3. **apply 后无自动收尾** — 建议 apply 后自动删旧 markdown 和 bundle
+4. **alias 识别依赖规则而非 LLM** — 当前全靠统计阈值，无法做语义级判断（如中英文映射、缩写展开）。如果需要高级 alias 识别，可以用 LLM 生成候选，规则脚本做 apply
@@ -0,0 +1,290 @@
+# interest/watch 候选引擎改进方案
+
+> 从固定阈值到自适应排位 + 趋势因子的演进
+
+## 1. 背景
+
+### 1.1 当前实现
+
+`build_review_bundle.py` 使用固定的绝对阈值将未覆盖词（uncovered terms）划分为两个候选池：
+
+| 候选池 | 判断条件 | 依据 |
+|--------|---------|------|
+| `interest_review_candidates` | `total_count >= 3 AND days_seen >= 2` | `policy.interest_keyword_review` |
+| `watch_review_candidates` | `total_count <= 2 AND days_seen <= 2` | `policy.watch_term_review` |
+
+`generate_term_cleanup_suggestions.py` 则直接从这两个候选池过滤、去重、排序后输出。
+
+### 1.2 当前方案的问题
+
+**问题一：固定阈值不随数据量自适应**
+
+```
+场景                   total_count=3 意味着什么
+─────────────────────────────────────────────
+7 天数据（~200 词）     top 15%，有一定区分度  ✅
+41 天数据（1070 词）    top 5%，区分度更高      ✅ 但阈值没变
+未来 200 天             仍然用 3 次，区分度稀释  ❌
+```
+
+同一个绝对次数，在不同数据规模下的语义完全不同。手工调阈值不可持续。
+
+**问题二：固定阈值忽略趋势信号**
+
+- "Anthropic"：total=13, recent=7 — 近期高活跃，上升趋势
+- "Channels"：total=3, recent=0 — 早期出现但近期消失
+- 当前引擎认为这两个词"都过了 3 次阈值"，同等对待。实际一个是强烈买入信号，一个是过气词。
+
+**问题三：interest 和 watch 的分界线是硬的**
+
+total=3 → interest，total=2 → watch。一个词从 2 次变成 3 次就自动"升级"，没有过渡、没有缓冲。
+
+### 1.3 讨论结论
+
+与老大讨论后确认：
+
+1. interest/watch 是**统计判断**，不需要大模型介入，纯算法可以解决
+2. 当前引擎缺的不是大模型，而是**算法本身没写完**——自适应维度（排位、趋势）还没实现
+3. alias 和 stopword 需要语义判断，与 interest/watch 分属不同阶段，不在本方案范围内
+4. 修改量小，可以在 1 小时内落地
+
+---
+
+## 2. 设计方案
+
+### 2.1 核心思路
+
+引入两个互补维度替代固定阈值：
+
+```
+判定维度                   含义                     数据来源
+────────────────────────────────────────────────────────────
+percentile（百分位排名）    该词 total_count 在所有词     term_stats
+                            中的排位占比
+growth（增速因子）          近期集中度 = recent_count      daily 近 N 天
+                                                    / total_count
+```
+
+两个维度配合：
+
+- **percentile** 衡量"这个词在当前数据集里有多突出"——消除数据量变化的影响
+- **growth** 衡量"这个词是持续出现还是近期爆发"——识别趋势信号
+
+### 2.2 候选池划分逻辑
+
+```
+           percentile
+              │
+    ┌─────────────────────┐
+    │  top 5%             │
+    │  → 建议 interest    │ ← 高频稳定词
+    ├─────────────────────┤
+    │  top 5%-20%         │
+    │  → 建议 watch       │ ← 有信号但未达 threshold
+    ├─────────────────────┤
+    │  bottom 80%          │
+    │  → 暂不处理          │ ← 噪声/低频
+    └─────────────────────┘
+
+额外规则：
+  如果词在 top 20% 之外，但 growth > 0.5（近期集中度高）
+  → 主动提升到 watch / 主动推 confirm
+```
+
+这样就不需要关心"total_count 是 3 还是 5"，只看数据自己说话。
+
+### 2.3 接口变化
+
+**`configs/term_cleanup_policy.json`**：
+
+```json
+{
+  "schema_version": "v2",
+  "interest_keyword_review": {
+    "percentile_max": 0.05,
+    "growth_promotion": 0.5
+  },
+  "watch_term_review": {
+    "percentile_min": 0.05,
+    "percentile_max": 0.20
+  }
+}
+```
+
+`v1` 的 `min_total_count`/`min_days_seen` 等绝对阈值字段不再使用。
+
+**`build_review_bundle.py` 输出的候选项**：
+
+```json
+{
+  "term": "Anthropic",
+  "total_count": 13,
+  "days_seen": 10,
+  "percentile": 0.012,
+  "growth": 0.54,
+  "reason": "top 1.2% by frequency, 54% of occurrences in recent window — strong signal."
+}
+```
+
+### 2.4 不需要改动的部分
+
+- `generate_term_cleanup_suggestions.py` — 它只消费候选池，不用改
+- `apply_term_suggestions.py` — 消费 suggestions JSON，不用改
+- `keyword-cleanup-bundle.json` 结构 — 向后兼容，新增 percentile/growth 字段
+
+---
+
+## 3. 实施计划
+
+### 3.1 改动范围
+
+| 文件 | 改动量 | 内容 |
+|------|--------|------|
+| `skills/keyword-cleanup-review/scripts/build_review_bundle.py` | ~40 行 | 新增 `_compute_percentile()` 和 `_compute_growth()` 函数；修改候选池生成逻辑；候选项中增加 percentile/growth |
+| `configs/term_cleanup_policy.json` | ~10 行 | schema v2：percentile/growth 替代绝对阈值 |
+
+### 3.2 实施步骤
+
+1. **build_review_bundle.py**：在 `top_global_terms` 生成后，增加 percentile 计算函数和 growth 计算函数
+2. **build_review_bundle.py**：修改 `interest_review_candidates` 和 `watch_review_candidates` 的生成逻辑，从固定阈值改为 percentile + growth
+3. **build_review_bundle.py**：候选项增加 `percentile` 和 `growth` 字段，更新 `reason` 文案
+4. **term_cleanup_policy.json**：更新为 v2 schema
+5. **验证**：全量跑一次（`--days 365 --top 100`），对比新旧两份输出的差异
+
+### 3.3 验证方法
+
+```bash
+# 1. 用旧版生成 baseline
+cd /home/ubuntu/zhu/github/reader
+python3.11 skills/keyword-cleanup-review/scripts/build_review_bundle.py \
+  --days 365 --top 100 \
+  --output /tmp/bundle-baseline.json
+
+# 2. 改代码后用新版生成
+python3.11 skills/keyword-cleanup-review/scripts/build_review_bundle.py \
+  --days 365 --top 100 \
+  --output /tmp/bundle-new.json
+
+# 3. 对比 governance_hints
+python3 -c "
+import json
+a = json.load(open('/tmp/bundle-baseline.json'))
+b = json.load(open('/tmp/bundle-new.json'))
+for key in ['interest_review_candidates', 'watch_review_candidates']:
+    old = set(i['term'] for i in a['governance_hints'][key])
+    new = set(i['term'] for i in b['governance_hints'][key])
+    print(f'{key}: 新增={new-old}, 减少={old-new}')
+"
+```
+
+### 3.4 风险
+
+| 风险 | 概率 | 应对 |
+|------|------|------|
+| 百分位阈值对特小数据集（如只有 1 天数据）不适用 | 低 | 不足 7 天时降级回绝对阈值 |
+| growth 因子对低频词的偏差（total=1, recent=1 → growth=1） | 低 | growth 只对 total>=3 的词计算 |
+| 排位突变导致推荐漂移 | 低 | percentil 天然平滑，新增几天数据不会剧烈改变已有词的排位 |
+
+---
+
+## 4. alias/stopword 设计方案
+
+### 4.1 核心判断
+
+alias 和 stopword 需要语义理解，与 interest/watch（纯统计）性质不同。
+
+| 类型 | 需要什么 | 判断方式 |
+|------|---------|----------|
+| 大小写变体 | 表层 | 规则：casefold 去重 |
+| 单复数 | 表层 | 规则：去/加 s 后缀匹配 |
+| 分词变体（空格/连字符） | 表层 | 规则：去空格归一 |
+| 简写全称（MCP→Model Context Protocol） | **语义** | LLM |
+| 中英文（上下文工程→Context Engineering） | **语义** | LLM |
+| 同义不同名（Rush→猿辅导 Rush 平台） | **语义** | LLM |
+| stopword（大模型、AI 太泛） | **语义** | LLM |
+
+### 4.2 分层方案
+
+```
+输入：高频未覆盖词 + 已有 interest 词表
+     │
+     ├── 规则层（零成本）── 大小写归一、单复数、去空格/连字符
+     │                     输出候选 alias 对
+     │
+     └── LLM 层（每次 ~500 token）── 把候选词表整批给 LLM
+                                     做语义聚类
+                                     输出 alias 组 + stopword 标记
+```
+
+### 4.3 规则层设计
+
+在 `generate_term_cleanup_suggestions.py` 中新增 `_prepare_alias_suggestions()` 函数：
+
+```python
+def _prepare_alias_suggestions(top_terms, interest_keywords):
+    """
+    基于表层规则生成 alias 建议。
+    规则1：casefold 匹配——同一个 casefold 下有多个原文变体
+    规则2：单复数——去掉/加上末尾 s 后匹配
+    规则3：分词变体——去空格/连字符后匹配
+    """
+```
+
+优势：零成本、可复现、可审计。直接写入 suggestions JSON，随 generate 一起输出。
+
+### 4.4 LLM 层设计
+
+单独脚本，非 generate 主链路的一部分。
+
+```bash
+python scripts/generate_term_cleanup_semantic_suggestions.py \
+  --suggestions outputs/term_index/review/term-cleanup-suggestions-YYYY-MM-DD.json \
+  --output outputs/term_index/review/term-cleanup-semantic-suggestions-YYYY-MM-DD.json
+```
+
+LLM prompt 设计：
+
+```
+你是一个关键词治理助手。以下是一个用户的 interest 关键词列表和一批未覆盖的高频词。
+请做三件事：
+
+1. ALIAS：判断哪些未覆盖词是已有 interest 关键词的别名/变体
+2. STOPWORD：标记哪些词太宽泛/通用，建议排除
+3. PROMOTE：标记哪些新词与用户关注方向一致，建议加入 interest
+
+用户关注方向：AI Agent 工程化、后端工程、开源工具、大模型落地
+```
+
+LLM 层输出格式：
+
+```json
+{
+  "alias_suggestions": [
+    {"from": "Context Engineering", "to": "上下文工程", "reason": "中英文对应同一概念"}
+  ],
+  "stopword_suggestions": [
+    {"term": "大模型", "reason": "过于宽泛，高频率但低区分度"}
+  ]
+}
+```
+
+### 4.5 预期效果
+
+| 覆盖类型 | 规则层 | LLM 层 |
+|---------|--------|--------|
+| 大小写变体 | ✅ | — |
+| 单复数 | ✅ | — |
+| 分词变体 | ✅ | — |
+| 简写全称 | — | ✅ |
+| 中英文映射 | — | ✅ |
+| 同义不同名 | — | ✅ |
+| stopword 判断 | — | ✅ |
+
+---
+
+## 5. 讨论记录
+
+- 2026-05-14：与老大确认 interest/watch 不需要 LLM，纯算法可解决
+- 2026-05-14：确认百分位排名 + 增速因子方案，修改量小，优先落地
+- 2026-05-14：确认本方案不改 `generate_term_cleanup_suggestions.py` 和 `apply_term_suggestions.py`
+- 2026-05-14：确认 alias/stopword 采用规则层 + LLM 层分层方案，规则层零成本优先
@@ -0,0 +1,314 @@
+#!/usr/bin/env python3
+"""
+Generate semantic keyword suggestions using LLM.
+
+Covers what surface-form rules cannot:
+  - semantic alias (abbreviation ↔ full name, Chinese ↔ English, synonym)
+  - stopword (overly broad / low-discrimination terms)
+  - promote (new term that aligns with user's focus areas)
+
+Usage:
+  python scripts/generate_term_cleanup_semantic_suggestions.py \
+    --bundle outputs/term_index/review/keyword-cleanup-bundle.json \
+    --output outputs/term_index/review/term-cleanup-semantic-suggestions-2026-05-14.json
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import re
+import sys
+import time
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+from urllib.request import Request, urlopen
+
+
+REPO_ROOT = Path(__file__).resolve().parents[1]
+DEFAULT_BUNDLE_PATH = REPO_ROOT / "outputs" / "term_index" / "review" / "keyword-cleanup-bundle.json"
+DEFAULT_OUTPUT_DIR = REPO_ROOT / "outputs" / "term_index" / "review"
+
+
+def _load_json(path: Path) -> Any:
+    return json.loads(path.read_text(encoding="utf-8-sig"))
+
+
+def _save_json(path: Path, payload: dict[str, Any]) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text(json.dumps(payload, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
+
+
+def _load_env(path: Path) -> dict[str, str]:
+    """Load key=value pairs from .env file."""
+    env: dict[str, str] = {}
+    if not path.exists():
+        return env
+    for line in path.read_text(encoding="utf-8").splitlines():
+        line = line.strip()
+        if not line or line.startswith("#") or "=" not in line:
+            continue
+        key, _, value = line.partition("=")
+        env[key.strip()] = value.strip().strip("\"'")
+    return env
+
+
+def _build_prompt(
+    interest_keywords: list[str],
+    rule_alias_suggestions: list[dict[str, str]],
+    candidate_terms: list[dict[str, Any]],
+    relevant_watch_terms: list[dict[str, Any]],
+) -> str:
+    """Build the LLM prompt for semantic suggestions."""
+
+    interest_bullets = "\n".join(f"  - {t}" for t in sorted(interest_keywords))
+    candidate_bullets = "\n".join(
+        f"  - {t['term']} (count={t['total_count']}, days={t['days_seen']})"
+        for t in candidate_terms[:40]
+    )
+
+    # Alias from rule layer (for LLM to build on, not duplicate)
+    rule_alias_text = ""
+    if rule_alias_suggestions:
+        rule_alias_text = "\nSurface-form alias (already identified, skip these):\n" + "\n".join(
+            f"  {a['from']} → {a['to']} ({a['reason']})"
+            for a in rule_alias_suggestions
+        )
+
+    watch_text = ""
+    if relevant_watch_terms:
+        watch_text = "\nWatch terms (low-frequency but potentially relevant):\n" + "\n".join(
+            f"  {t['term']} (count={t['total_count']}, days={t['days_seen']})"
+            for t in relevant_watch_terms[:20]
+        )
+
+    return f"""You are a keyword governance assistant for an AI engineer. Your job is to analyze keyword data and produce structured suggestions.
+
+## User's focus areas
+- AI Agent engineering (Skills, Harness, MCP, Agent architecture)
+- Backend engineering (Java, Go, Kubernetes, MySQL, distributed systems)
+- Open source AI tools and practices (Claude Code, Cursor, DeepSeek, OpenClaw)
+- LLM application engineering (context engineering, RAG, prompt engineering)
+
+## Interest keywords (52 already configured)
+{interest_bullets}
+
+## Uncovered candidate terms (sorted by frequency)
+{candidate_bullets}
+{watch_text}{rule_alias_text}
+
+## Task
+Analyze the candidate terms and output a JSON object with exactly three keys:
+
+1. "semantic_alias": array of alias suggestions that SURFACE RULES CAN'T CATCH (e.g. abbreviation↔full name, Chinese↔English, different naming for the same concept).
+   Format: [{{"from": "<variant>", "to": "<canonical interest keyword>", "reason": "<why>"}}]
+
+2. "stopword": array of terms that are too broad/generic to be useful as filters. A stopword is a term that appears frequently but has LOW DISCRIMINATION — it matches too many unrelated articles and clutters the keyword index.
+   Format: [{{"term": "<term>", "reason": "<why it should be a stopword>"}}]
+
+3. "promote_to_interest": array of uncovered terms that align well with the user's focus areas and should be added as interest keywords.
+   Format: [{{"term": "<term>", "reason": "<why it fits>"}}]
+
+## Rules
+- Be conservative. When in doubt, leave it out.
+- Only suggest alias for terms that clearly refer to the SAME concept as an existing interest keyword.
+- Only suggest stopword for terms that are genuinely too broad (appear in many unrelated contexts).
+- Only suggest promote for terms that clearly match the user's stated focus areas.
+- Output valid JSON only, no markdown, no explanation outside the JSON."""
+
+
+def _call_llm(prompt: str, api_url: str, model: str, api_key: str) -> str:
+    """Call LLM API and return the response text."""
+    payload = json.dumps({
+        "model": model,
+        "messages": [{"role": "user", "content": prompt}],
+        "temperature": 0.1,
+        "max_tokens": 2048,
+    }).encode("utf-8")
+
+    req = Request(
+        api_url.rstrip("/") + "/chat/completions",
+        data=payload,
+        headers={
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {api_key}",
+        },
+    )
+
+    max_retries = 3
+    for attempt in range(max_retries):
+        try:
+            with urlopen(req, timeout=120) as resp:
+                result = json.loads(resp.read().decode("utf-8"))
+            return result["choices"][0]["message"]["content"]
+        except Exception as e:
+            if attempt < max_retries - 1:
+                wait = 2 ** attempt
+                print(f"  LLM call failed (attempt {attempt+1}/{max_retries}): {e}", file=sys.stderr)
+                print(f"  Retrying in {wait}s...", file=sys.stderr)
+                time.sleep(wait)
+            else:
+                raise
+
+
+def _parse_llm_response(text: str) -> dict[str, list[dict[str, str]]]:
+    """Extract JSON from LLM response (may contain markdown fences)."""
+    # Try to find JSON block
+    json_match = re.search(r"```(?:json)?\s*\n?(\{.*?\})\s*\n?```", text, re.DOTALL)
+    if json_match:
+        text = json_match.group(1)
+
+    # Clean up: remove any text before { or after }
+    start = text.find("{")
+    end = text.rfind("}")
+    if start >= 0 and end > start:
+        text = text[start : end + 1]
+
+    try:
+        result = json.loads(text)
+    except json.JSONDecodeError:
+        # Try partial recovery
+        print(f"  Warning: LLM response not clean JSON, attempting recovery", file=sys.stderr)
+        print(f"  Raw: {text[:500]}", file=sys.stderr)
+        return {"semantic_alias": [], "stopword": [], "promote_to_interest": []}
+
+    # Normalize keys
+    normalized = {
+        "semantic_alias": result.get("semantic_alias", result.get("alias", [])),
+        "stopword": result.get("stopword", result.get("stopword_suggestions", [])),
+        "promote_to_interest": result.get("promote_to_interest", result.get("promote", [])),
+    }
+    # Ensure each is a list
+    for key in normalized:
+        if not isinstance(normalized[key], list):
+            normalized[key] = []
+    return normalized
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Generate semantic keyword suggestions via LLM.")
+    parser.add_argument("--bundle", type=Path, default=DEFAULT_BUNDLE_PATH, help="Review bundle JSON path")
+    parser.add_argument("--suggestions", type=Path, default=None, help="Existing suggestions JSON (for rule alias context)")
+    parser.add_argument("--output", type=Path, default=None, help="Output JSON path (auto-generated if omitted)")
+    parser.add_argument("--llm-api-url", type=str, default=None, help="LLM API base URL")
+    parser.add_argument("--llm-model", type=str, default=None, help="LLM model name")
+    parser.add_argument("--llm-api-key", type=str, default=None, help="LLM API key")
+    parser.add_argument("--dry-run", action="store_true", help="Print prompt and exit without calling LLM")
+    args = parser.parse_args()
+
+    # Load config
+    env_path = REPO_ROOT / ".env"
+    env = _load_env(env_path) if env_path.exists() else {}
+
+    api_url = args.llm_api_url or os.environ.get("LLM_API_URL") or env.get("LLM_API_URL", "https://api.deepseek.com")
+    # Map OpenClaw model aliases to actual API model names
+    model_raw = args.llm_model or os.environ.get("LLM_MODEL") or env.get("LLM_MODEL", "deepseek-chat")
+    MODEL_ALIAS_MAP = {
+        "deepseek/deepseek-v4-flash": "deepseek-chat",
+        "deepseek/deepseek-chat": "deepseek-chat",
+        "deepseek-v4-flash": "deepseek-chat",
+        "deepseek-chat": "deepseek-chat",
+    }
+    model = MODEL_ALIAS_MAP.get(model_raw, model_raw)
+    api_key = args.llm_api_key or os.environ.get("LLM_API_KEY") or env.get("LLM_API_KEY", "")
+
+    if not api_key:
+        print("Error: No LLM API key found. Set LLM_API_KEY in .env or pass --llm-api-key.", file=sys.stderr)
+        sys.exit(1)
+
+    # Load bundle
+    if not args.bundle.exists():
+        print(f"Error: Bundle not found: {args.bundle}", file=sys.stderr)
+        sys.exit(1)
+
+    bundle = _load_json(args.bundle)
+    current_config = bundle.get("current_config", {})
+    interest_keywords = current_config.get("interest_keywords", [])
+    top_global_terms = bundle.get("top_global_terms", [])
+    governance_hints = bundle.get("governance_hints", {})
+
+    # Build candidate list (uncovered terms from interest + watch candidates)
+    candidate_terms = []
+    for item in governance_hints.get("interest_review_candidates", []):
+        if isinstance(item, dict):
+            candidate_terms.append({
+                "term": item.get("term", ""),
+                "total_count": item.get("total_count", 0),
+                "days_seen": item.get("days_seen", 0),
+                "percentile": item.get("percentile", 0),
+                "growth": item.get("growth", 0),
+            })
+    for item in governance_hints.get("watch_review_candidates", []):
+        if isinstance(item, dict):
+            # Avoid duplicates
+            if not any(c["term"] == item.get("term") for c in candidate_terms):
+                candidate_terms.append({
+                    "term": item.get("term", ""),
+                    "total_count": item.get("total_count", 0),
+                    "days_seen": item.get("days_seen", 0),
+                    "percentile": item.get("percentile", 0),
+                    "growth": item.get("growth", 0),
+                })
+
+    # Sort by total_count descending
+    candidate_terms.sort(key=lambda x: -x["total_count"])
+    relevant_watch_terms = governance_hints.get("watch_review_candidates", [])[:20]
+
+    # Load rule-layer alias suggestions if available
+    rule_alias = []
+    if args.suggestions and args.suggestions.exists():
+        s = _load_json(args.suggestions)
+        rule_alias = s.get("alias_suggestions", [])
+
+    # Build prompt
+    prompt = _build_prompt(
+        interest_keywords=interest_keywords,
+        rule_alias_suggestions=rule_alias,
+        candidate_terms=candidate_terms,
+        relevant_watch_terms=relevant_watch_terms,
+    )
+
+    # Determine output path
+    suggestion_date = datetime.now(timezone.utc).date().isoformat()
+    output_path = args.output or (DEFAULT_OUTPUT_DIR / f"term-cleanup-semantic-suggestions-{suggestion_date}.json")
+
+    if args.dry_run:
+        print("=== DRY RUN: Prompt ===")
+        print(prompt)
+        print("\n=== END ===")
+        print(f"\nWould write to: {output_path}")
+        return
+
+    # Call LLM
+    print(f"Calling LLM ({model})...", file=sys.stderr)
+    response = _call_llm(prompt, api_url, model, api_key)
+    print(f"LLM response received ({len(response)} chars)", file=sys.stderr)
+
+    # Parse
+    parsed = _parse_llm_response(response)
+
+    # Build output
+    output = {
+        "date": suggestion_date,
+        "source_bundle": str(args.bundle),
+        "model": model,
+        "interest_keyword_count": len(interest_keywords),
+        "candidate_count": len(candidate_terms),
+        **parsed,
+    }
+
+    _save_json(output_path, output)
+
+    summary = {
+        "output": str(output_path),
+        "semantic_alias": len(output.get("semantic_alias", [])),
+        "stopword": len(output.get("stopword", [])),
+        "promote_to_interest": len(output.get("promote_to_interest", [])),
+    }
+    print(json.dumps(summary, ensure_ascii=False, indent=2))
+
+
+if __name__ == "__main__":
+    main()
@@ -175,6 +175,121 @@ def _prepare_watch_suggestions(bundle: dict[str, Any], reserved_terms: set[str])
    return suggestions


+def _prepare_alias_suggestions(
+    bundle: dict[str, Any],
+    all_terms: list[dict[str, Any]] | None = None,
+) -> list[dict[str, Any]]:
+    """
+    Generate alias suggestions using surface-form rules (no LLM).
+
+    Rules:
+    1. casefold match — same normalized form, different original casing
+    2. trailing-s singularization — singular/plural variants
+    3. whitespace/hyphen normalization — word boundary variants
+
+    Scans all_terms (full term_stats) if provided; otherwise falls back
+    to top_global_terms from the bundle.
+    """
+    current_config = _require_dict(bundle.get("current_config"), "bundle.current_config")
+    interest_keywords = _require_list(
+        current_config.get("interest_keywords"), "bundle.current_config.interest_keywords"
+    )
+    top_global_terms = _require_list(bundle.get("top_global_terms"), "bundle.top_global_terms")
+    source_terms = all_terms if all_terms is not None else top_global_terms
+
+    interest_set = {_term_key(t) for t in interest_keywords if isinstance(t, str)}
+    interest_originals: set[str] = {t for t in interest_keywords if isinstance(t, str)}
+
+    # Build full casefold → [original forms] map
+    cf_map: dict[str, list[str]] = {}
+    for item in source_terms:
+        term = None
+        if isinstance(item, dict):
+            term = item.get("term")
+        elif isinstance(item, str):
+            term = item
+        if not isinstance(term, str) or not term.strip():
+            continue
+        key = _term_key(term)
+        if key not in cf_map:
+            cf_map[key] = []
+        if term not in cf_map[key]:
+            cf_map[key].append(term)
+
+    suggestions: list[dict[str, Any]] = []
+    seen_pairs: set[tuple[str, str]] = set()
+
+    def _add(from_term: str, to_term: str, reason: str) -> None:
+        pair = (_term_key(from_term), _term_key(to_term))
+        if pair in seen_pairs:
+            return
+        seen_pairs.add(pair)
+        suggestions.append({"from": from_term, "to": to_term, "reason": reason})
+
+    # Build a set of all term keys from source for quick lookup
+    source_keys = set(cf_map.keys())
+
+    # Rule 1: casefold match — same normalized form, different casing
+    for key, variants in cf_map.items():
+        if len(variants) < 2:
+            continue
+        canonical = None
+        alt_forms = []
+        for v in variants:
+            if v in interest_originals:
+                canonical = v
+            else:
+                alt_forms.append(v)
+        if canonical and alt_forms:
+            for alt in alt_forms:
+                _add(alt, canonical, "Case variant")
+        elif len(variants) >= 2 and not canonical:
+            # None is canonical — suggest the highest-frequency form
+            ranked = sorted(variants, key=lambda t: -(
+                next(
+                    (it.get("total_count", 0) for it in top_global_terms if it.get("term") == t),
+                    0,
+                )
+            ))
+            for alt in ranked[1:]:
+                _add(alt, ranked[0], "Case variant (auto-ranked)")
+
+    # Rule 2: singular/plural — trailing-s normalization
+    # Check all source terms (not just interest keys) for bidirectional matching
+    for key in source_keys:
+        if key in interest_set:
+            continue
+        if key.endswith("s") and len(key) > 2:
+            singular_key = key.rstrip("s")
+            if singular_key in interest_set and singular_key != key:
+                # Find canonical interest keyword
+                canon = next((t for t in interest_keywords if _term_key(t) == singular_key), None)
+                from_form = cf_map[key][0]
+                if canon:
+                    _add(from_form, canon, "Plural variant")
+        # singular form → interest has plural
+        plural_key = key + "s"
+        if plural_key in interest_set and plural_key != key:
+            canon = next((t for t in interest_keywords if _term_key(t) == plural_key), None)
+            from_form = cf_map[key][0]
+            if canon:
+                _add(from_form, canon, "Singular variant")
+
+    # Rule 3: whitespace/hyphen normalization
+    for key in source_keys:
+        if key in interest_set:
+            continue
+        normalized = key.replace("-", "").replace("_", "").replace(" ", "")
+        if normalized in interest_set and normalized != key:
+            canon = next((t for t in interest_keywords if _term_key(t) == normalized), None)
+            from_form = cf_map[key][0]
+            if canon:
+                _add(from_form, canon, "Whitespace/punctuation variant")
+
+    suggestions.sort(key=lambda x: (x["from"].casefold(), x["to"].casefold()))
+    return suggestions
+
+
 def _render_table(items: list[dict[str, Any]]) -> str:
    if not items:
        return "_None in this pass._\n"
@@ -361,9 +476,19 @@ def main() -> None:
        markdown_output=args.markdown_output,
    )

+    # Load full term_stats for alias scanning (bundle only has top N)
+    stats_path = REPO_ROOT / "data" / "term_index" / "term_stats.json"
+    all_stats_terms: list[str] = []
+    if stats_path.exists():
+        stats_payload = _load_json(stats_path)
+        raw_terms = stats_payload.get("terms") if isinstance(stats_payload, dict) else []
+        if isinstance(raw_terms, list):
+            all_stats_terms = [str(t["term"]) for t in raw_terms if isinstance(t, dict) and isinstance(t.get("term"), str)]
+
    interest_items = _prepare_interest_suggestions(bundle)
    reserved_terms = {_term_key(str(item.get("term") or "")) for item in interest_items}
    watch_items = _prepare_watch_suggestions(bundle, reserved_terms=reserved_terms)
+    alias_items = _prepare_alias_suggestions(bundle, all_terms=all_stats_terms)

    suggestions = {
        "date": suggestion_date,
@@ -373,10 +498,10 @@ def main() -> None:
        "summary": {
            "interest_keyword_suggestions": len(interest_items),
            "watch_terms": len(watch_items),
-            "alias_suggestions": 0,
+            "alias_suggestions": len(alias_items),
            "stopword_suggestions": 0,
        },
-        "alias_suggestions": [],
+        "alias_suggestions": alias_items,
        "stopword_suggestions": [],
        "interest_keyword_suggestions": interest_items,
        "watch_terms": watch_items,
@@ -400,7 +525,7 @@ def main() -> None:
        "markdown_output": str(markdown_output_path) if args.emit_markdown else None,
        "interest_keyword_suggestions": len(interest_items),
        "watch_terms": len(watch_items),
-        "alias_suggestions": 0,
+        "alias_suggestions": len(alias_items),
        "stopword_suggestions": 0,
        "emit_markdown": args.emit_markdown,
    }
@@ -26,7 +26,7 @@ description: 生成 reader 项目的正式关键词 review 输入。当用户需

 ## 工作流程

-1. 构建精简的审查数据包（临时工作文件）：
+### Phase 1：构建审查数据包

 ```bash
 python skills/keyword-cleanup-review/scripts/build_review_bundle.py
@@ -34,51 +34,92 @@ python skills/keyword-cleanup-review/scripts/build_review_bundle.py

 可选参数：

- `--days 7`
- `--top 50`
+- `--days 7`（默认 7，建议传 365 覆盖全量）
+- `--top 100`（考虑的词数）
 - `--output outputs/term_index/review/keyword-cleanup-bundle.json`

-2. 阅读建议模式：
+#### 候选引擎策略

- `skills/keyword-cleanup-review/references/suggestion-schema.md`
+根据 `configs/term_cleanup_policy.json` 的 `schema_version` 自动切换：

-3. 运行 suggestions 生成脚本：
+| 版本 | 策略 | 说明 |
+|------|------|------|
+| v1（旧） | 固定阈值（total≥3/days≥2 → interest） | 小数据集兼容 |
+| v2（当前默认） | 百分位排名 + 增速因子 | 自适应数据量，不需要手工调阈值 |
+
+v2 策略说明：
+- **percentile**：total_count 在所有词里的排位占比。top 5% → interest 候选，5%-20% → watch 候选
+- **growth**：recent_count / total_count，衡量近期活跃度。growth≥0.5 的排位外词也会主动推荐
+
+### Phase 2：生成建议（规则层）

 ```bash
-python scripts/generate_term_cleanup_suggestions.py ^
+python scripts/generate_term_cleanup_suggestions.py \
  --bundle outputs/term_index/review/keyword-cleanup-bundle.json
 ```

-默认生成：
-
- 一份符合模式的 JSON 建议文件（正式建议产物，也是 review / apply 之间唯一正式输入）
-
-如需人工审阅展示稿，再显式加：
+如需人工审阅展示稿：

 ```bash
-python scripts/generate_term_cleanup_suggestions.py ^
-  --bundle outputs/term_index/review/keyword-cleanup-bundle.json ^
+python scripts/generate_term_cleanup_suggestions.py \
+  --bundle outputs/term_index/review/keyword-cleanup-bundle.json \
  --emit-markdown
 ```

-这时才会额外生成：
+#### 产出能力

- 一份简短的供人工审阅的 Markdown 报告（临时展示稿）
+| 建议类型 | 状态 | 方法 |
+|---------|------|------|
+| interest 建议 | ✅ 已实现 | 百分位 top 5% + 增速促活 |
+| watch 建议 | ✅ 已实现 | 百分位 5%-20% |
+| alias 建议 | ✅ 已实现 | 规则层：大小写归一、单复数、去空格/连字符 |
+| stopword 建议 | ❌ 规则层空缺 | 见 Phase 3（LLM 层） |

-4. 严格保持边界：
+默认生成：
+- `term-cleanup-suggestions-YYYY-MM-DD.json`（正式建议产物）
+
+显式加 `--emit-markdown` 额外生成：
+- `term-cleanup-suggestions-YYYY-MM-DD.md`（临时展示稿）
+
+### Phase 3：生成建议（LLM 层，可选）
+
+规则层覆盖不了 alias（中英文对应、缩写展开、同义不同名）和 stopword 判断，需要 LLM 辅助：
+
+```bash
+python scripts/generate_term_cleanup_semantic_suggestions.py \
+  --bundle outputs/term_index/review/keyword-cleanup-bundle.json \
+  --suggestions outputs/term_index/review/term-cleanup-suggestions-YYYY-MM-DD.json \
+  --output outputs/term_index/review/term-cleanup-semantic-suggestions-YYYY-MM-DD.json
+```
+
+从 `.env` 读取 LLM 配置（`LLM_API_URL` / `LLM_MODEL` / `LLM_API_KEY`），使用 DeepSeek API。
+
+输出三部分：
+
+| 输出 | 说明 |
+|------|------|
+| `semantic_alias` | 语义级别名（中英文、缩写、同义不同名） |
+| `stopword` | 泛词过滤建议（规则层做不了的需要语义判断的） |
+| `promote_to_interest` | 与用户关注方向一致的新词，建议加入 interest |
+
+**注：LLM 层产物是候选，不应自动 apply，需要人工确认后由 OpenClaw 编排 apply。**
+
+### Phase 4：输出给 OpenClaw 编排
+
+- `suggestions JSON` = review / apply 之间唯一正式建议输入
+- `semantic-suggestions JSON` = LLM 补充建议，需要人工筛选后合并到 suggestions JSON 再 apply
+- Markdown = 临时展示层
+- 后续汇报、确认、dry-run、apply、收尾清理由 OpenClaw 编排层执行
+
+### Phase 5：严格保持边界

 - 建议 `configs/term_aliases.json` 的修改
 - 建议 `configs/term_stopwords.json` 的修改
 - 建议 `configs/filter_context.personal.json` 的新增
+- **LLM 层产出（semantic-suggestions）不自动 apply**，需人工确认后由 OpenClaw 编排层执行
 - 除非用户明确要求，否则不要直接编辑这些文件
 - 除非用户要求修改规则逻辑，否则不要建议直接编辑 `configs/filter_rules.json`

-5. 输出交接口径：
-
- 将 JSON suggestions 视为正式 review 输入
- 将 Markdown 视为可选展示层
- 后续汇报、确认、dry-run apply、正式 apply、收尾清理应由 OpenClaw 编排层继续执行
-
 ## 审查启发式规则

 优先考虑以下决策：
@@ -141,6 +182,7 @@ JSON 输出应遵循：
 短期保留：

 - `outputs/term_index/review/term-cleanup-suggestions-YYYY-MM-DD.json`
+- `outputs/term_index/review/term-cleanup-semantic-suggestions-YYYY-MM-DD.json`

 临时产物：

@@ -173,7 +215,9 @@ JSON 输出应遵循：
 ## 资源

 - 脚本：
-  - `scripts/build_review_bundle.py`
+  - `skills/keyword-cleanup-review/scripts/build_review_bundle.py`
  - `scripts/generate_term_cleanup_suggestions.py`
+  - `scripts/generate_term_cleanup_semantic_suggestions.py`（LLM 层）
 - 参考文档：
  - `references/suggestion-schema.md`
+  - `plans/keyword-cleanup-interest-watch-engine-improvement.md`（v2 引擎设计）
@@ -9,16 +9,15 @@ from typing import Any


 DEFAULT_POLICY: dict[str, Any] = {
-    "schema_version": "v1",
+    "schema_version": "v2",
    "interest_keyword_review": {
-        "min_total_count": 3,
-        "min_days_seen": 2,
+        "percentile_min": 0.0,
+        "percentile_max": 0.05,
+        "growth_promotion": 0.5,
    },
    "watch_term_review": {
-        "min_total_count": 1,
-        "min_days_seen": 1,
-        "max_total_count": 2,
-        "max_days_seen": 2,
+        "percentile_min": 0.05,
+        "percentile_max": 0.20,
    },
    "alias_review": {
        "min_total_count": 2,
@@ -28,6 +27,11 @@ DEFAULT_POLICY: dict[str, Any] = {
        "max_total_count": 2,
        "max_days_seen": 2,
    },
+    "notes": [
+        "v2: interest/watch 使用百分位排名 + 增速因子替代固定阈值",
+        "percentile 越小表示排名越高（top 5% = percentile 0.05）",
+        "growth = recent_count / total_count，衡量近期活跃度",
+    ],
 }


@@ -126,6 +130,37 @@ def _within_watch_thresholds(item: dict[str, Any], thresholds: dict[str, Any]) -
    )


+def _compute_percentile(value: int, sorted_values: list[int]) -> float:
+    """
+    Return the percentile rank of `value` in `sorted_values` (ascending).
+    0.0 = highest frequency (top rank), 1.0 = lowest frequency (bottom rank).
+    """
+    if not sorted_values:
+        return 1.0
+    # bisect_left — count of values strictly less than `value`
+    lo, hi = 0, len(sorted_values)
+    while lo < hi:
+        mid = (lo + hi) // 2
+        if sorted_values[mid] < value:
+            lo = mid + 1
+        else:
+            hi = mid
+    rank = lo
+    # invert: smallest value → rank=0 → 1.0 (bottom)
+    #         largest value → rank=len → 0.0 (top)
+    return 1.0 - (rank / len(sorted_values))
+
+
+def _compute_growth(recent_count: int, total_count: int) -> float:
+    """
+    Return growth factor: recent_count / total_count.
+    Only meaningful when total_count >= 3; returns 0.0 for small counts.
+    """
+    if total_count < 3:
+        return 0.0
+    return recent_count / total_count
+
+
 def main() -> None:
    parser = argparse.ArgumentParser(
        description="Build a compact review bundle for the keyword-cleanup-review skill."
@@ -235,6 +270,13 @@ def main() -> None:
    alias_values = _casefold_set(list(aliases.values()))
    watch_set = _casefold_set([str(item.get("term", "")) for item in watchlist])

+    # Build a sorted list of all total_counts for percentile computation
+    all_total_counts = sorted(
+        int(item.get("total_count") or 0)
+        for item in stats_terms
+        if isinstance(item, dict) and isinstance(item.get("term"), str)
+    )
+
    top_global_terms = []
    for item in stats_terms[: args.top]:
        if not isinstance(item, dict):
@@ -256,42 +298,108 @@ def main() -> None:
                "is_alias_target": folded in alias_values,
                "in_watchlist": folded in watch_set,
                "recent_count": recent_counter.get(term, 0),
+                "percentile": _compute_percentile(
+                    int(item.get("total_count") or 0), all_total_counts
+                ),
+                "growth": _compute_growth(
+                    recent_counter.get(term, 0),
+                    int(item.get("total_count") or 0),
+                ),
            }
        )

+    # Keep more uncovered terms for percentile-based selection
    uncovered_terms = [
        item for item in top_global_terms if not item["in_interest_keywords"] and not item["is_stopword"]
-    ][:20]
+    ][:100]
+
+    policy_version = (policy.get("schema_version") if isinstance(policy, dict) else None) or "v1"
    interest_thresholds = policy.get("interest_keyword_review") if isinstance(policy, dict) else {}
    watch_thresholds = policy.get("watch_term_review") if isinstance(policy, dict) else {}
+
+    if policy_version == "v2" or "percentile_max" in interest_thresholds:
+        # v2: percentile + growth based selection
+        pct_min_interest = float(interest_thresholds.get("percentile_min", 0.0))
+        pct_max_interest = float(interest_thresholds.get("percentile_max", 0.05))
+        growth_promo = float(interest_thresholds.get("growth_promotion", 0.5))
+        pct_min_watch = float(watch_thresholds.get("percentile_min", 0.05))
+        pct_max_watch = float(watch_thresholds.get("percentile_max", 0.20))
+
+        interest_candidates_raw = [
+            item for item in uncovered_terms
+            if not item["in_watchlist"]
+            and pct_min_interest <= item["percentile"] <= pct_max_interest
+        ]
+        watch_candidates_raw = [
+            item for item in uncovered_terms
+            if not item["in_watchlist"]
+            and pct_min_watch < item["percentile"] <= pct_max_watch
+        ]
+        # Growth boost: terms outside watch range but with strong growth signal
+        growth_boost_candidates = [
+            item for item in uncovered_terms
+            if not item["in_watchlist"]
+            and item["percentile"] > pct_max_watch
+            and item["growth"] >= growth_promo
+        ]
+    else:
+        # v1 fallback: fixed thresholds
+        interest_candidates_raw = [
+            item for item in uncovered_terms
+            if not item["in_watchlist"] and _meets_min_thresholds(item, interest_thresholds)
+        ]
+        watch_candidates_raw = [
+            item for item in uncovered_terms
+            if not item["in_watchlist"]
+            and not _meets_min_thresholds(item, interest_thresholds)
+            and _within_watch_thresholds(item, watch_thresholds)
+        ]
+        growth_boost_candidates = []
+
    interest_review_candidates = [
        {
            "term": item["term"],
            "total_count": item["total_count"],
            "days_seen": item["days_seen"],
+            "percentile": item["percentile"],
+            "growth": item["growth"],
            "reason": (
-                "Meets the configured interest-keyword review threshold and is not yet covered "
-                "by interest keywords or stopwords."
+                f"top {item['percentile']:.1%} by frequency，"
+                f"growth={item['growth']:.0%}，"
+                "not yet covered by interest keywords or stopwords."
            ),
        }
-        for item in uncovered_terms
-        if not item["in_watchlist"] and _meets_min_thresholds(item, interest_thresholds)
+        for item in interest_candidates_raw
    ][:20]
    watch_review_candidates = [
        {
            "term": item["term"],
            "total_count": item["total_count"],
            "days_seen": item["days_seen"],
+            "percentile": item["percentile"],
+            "growth": item["growth"],
            "reason": (
-                "Falls into the configured watch-term review range and should be observed "
-                "before promotion into interest keywords."
+                f"top {item['percentile']:.1%} by frequency，"
+                f"growth={item['growth']:.0%}，"
+                "fell into watch-review range."
            ),
        }
-        for item in uncovered_terms
-        if not item["in_watchlist"]
-        and not _meets_min_thresholds(item, interest_thresholds)
-        and _within_watch_thresholds(item, watch_thresholds)
+        for item in watch_candidates_raw
    ][:20]
+    growth_boost_review_items = [
+        {
+            "term": item["term"],
+            "total_count": item["total_count"],
+            "days_seen": item["days_seen"],
+            "percentile": item["percentile"],
+            "growth": item["growth"],
+            "reason": (
+                f"growth spike: {item['growth']:.0%} of occurrences in recent window "
+                f"(total={item['total_count']}, days={item['days_seen']})."
+            ),
+        }
+        for item in growth_boost_candidates
+    ][:5]
    recent_hot_terms = sorted(
        ({"term": term, "recent_count": count} for term, count in recent_counter.items()),
        key=lambda item: (-item["recent_count"], item["term"].casefold(), item["term"]),
@@ -330,6 +438,7 @@ def main() -> None:
        "governance_hints": {
            "interest_review_candidates": interest_review_candidates,
            "watch_review_candidates": watch_review_candidates,
+            "growth_boost_review_items": growth_boost_review_items,
        },
    }
    _save_json(args.output, bundle)
@@ -0,0 +1,186 @@
+---
+name: reader-digest-flow
+description: 编排 reader 项目的端到端 AI 日报流程。仅在用户要求运行/重跑日报、汇报候选、发布 Hugo 日报、沉淀选中文章或继续已有日报任务时使用；覆盖异步 MCP 任务、候选确认、发布、单篇摘要和 IMA 知识库上传。不要因验证、排障冲动或候选质量不佳自行重跑。
+---
+
+# Reader Digest Flow
+
+## 职责边界
+
+本 Skill 负责：
+
+- 通过 reader MCP 启动、观察和恢复日报任务；
+- 向用户展示候选并保持稳定编号；
+- 根据用户选择生成并发布 Hugo 日报；
+- 对用户选中的文章生成知识笔记并编排 IMA 上传；
+- 在每个副作用边界执行确认和结果验证。
+
+本 Skill 不负责：
+
+- 实现 reader 内部抓取、摘要、过滤或恢复逻辑；
+- 通过手拼目录推导 Run 状态或 Artifact；
+- 未经用户要求自行重跑 Pipeline；
+- 未经用户确认发布日报或写入知识库；
+- 直接维护关键词配置；关键词治理委托给 `keyword-cleanup-review`。
+
+## 核心规则
+
+1. **只按用户指令运行。** 只有用户明确要求“跑日报”“重新跑”“再跑一次”时才启动新 Pipeline。验证、解释排序和排障默认读取已有 Run。
+2. **一次对话绑定一个当前 Run。** 以异步 Job 结果返回的 `run_id` 为稳定句柄；新 Run 产生新的候选编号体系，不混用历史编号。
+3. **状态以 MCP 返回为准。** Agent 只根据顶层 `status` 和 `recommended_action` 分支；`status_source`、`state_conflict` 仅用于解释。
+4. **路径以返回值为准。** 使用 `output_dir`、`artifact.path`、`delivery_output`、`report_output` 和 `written_paths`；不要根据 `run_id` 手拼 `outputs/...`。
+5. **候选编号保持稳定。** 用户编号永远对应当前候选列表的原始顺序（1-based）；跨产物读取详情时按 URL 或完整 `item_id` 关联，不按数组位置关联。
+6. **副作用必须授权。** 用户确认 Hugo 文章后才能发布；用户确认 IMA 文章后才能生成并上传知识笔记。
+7. **内容必须有来源。** 日报和知识笔记只能基于当前 Run 的 `article.plain_text`、摘要、highlights 等 Artifact；不得使用通用知识补写原文没有的信息，也不为满足长度而扩写。
+
+## 默认生产参数
+
+用户未显式覆盖时使用：
+
+```json
+{
+  "limit": 7,
+  "include_read": false,
+  "mark_read": true,
+  "debug_artifacts": false,
+  "timeout_seconds": 60,
+  "max_retries": 2
+}
+```
+
+- 默认只处理未读文章。
+- `include_read=true` 仅在用户明确要求扩大到已读内容时使用。
+- debug/test/validation 才允许 `mark_read=false` 或 `debug_artifacts=true`。
+- 不随机生成文章数量；用户指定 `limit` 时按用户值执行。
+
+## 正式流程
+
+### Phase 1：启动并观察日报 Job
+
+正式生产入口统一为异步 MCP：
+
+1. 调用 `start_freshrss_pipeline_job`；
+2. 轮询 `get_freshrss_pipeline_job_status`；
+3. `status=success` 后调用 `get_freshrss_pipeline_job_result`；
+4. 保存返回的 `run_id` 和 Artifact 路径；
+5. 使用 `get_run_status`、`get_delivery_payload`、`get_run_report` 读取业务状态和结果。
+
+失败时：
+
+1. 使用 `get_run_status(run_id)` 读取关联 Run；
+2. 调用 `inspect_resume_plan(run_id)`；
+3. `recommended_action=resume` 时启动并轮询异步 Resume Job；
+4. `recommended_action=read_terminal_result` 时直接读取已有终态结果；
+5. `recommended_action=start_new_run` 时停止并向用户报告，不自行新建 Run。
+
+CLI 仅用于 MCP 不可用时的 fallback、debug 或人工排障，不是默认生产入口。具体调用序列见 `references/flow.md`。
+
+### Phase 2：汇报候选
+
+- 使用当前 Run 返回的 Delivery Payload 或 digest brief Artifact；
+- 按候选原始顺序从 1 编号，状态可显示为“已入选/待确认”，但不得重新分组编号；
+- 每篇提供标题、来源、2-3 句摘要和筛选理由，避免原始 JSON dump；
+- 用户质疑编号或排序时读取当前 Run 产物核对，不重新运行 Pipeline；
+- 需要跨 Artifact 取详情时按 URL 或完整 `item_id` 交叉验证。
+
+Feishu 输出不要使用 Markdown 表格，见 `references/feishu-format-notes.md`。
+
+### Phase 3：等待 Hugo 选择
+
+- 等待用户明确选择要发布的文章；
+- 用户编号映射到当前候选列表，不映射到 extracted 文件序号；
+- 用户拒绝发布时立即停止当日日报后续流程，不劝说、不自动换一批；
+- 用户明确要求重跑时才创建新 Run，并重新建立编号体系。
+
+### Phase 4：生成并发布 Hugo 日报
+
+发布前读取 `references/public-digest-example.md`，按其最终页面结构生成：
+
+- `今日概览`
+- `今日重点`
+- `趋势观察`
+
+每篇 `今日重点` 文章末尾必须添加 `来源：[来源名](原文 URL)`，来源链接跟随对应文章，不再生成独立的 `延伸阅读` 章节或重复链接。
+
+仅发布用户在 Phase 3 选中的文章。公开页面不得出现 `keep/review/drop`、候选、待确认等内部状态。
+
+写入 Hugo 后执行部署，并验证首页、日报列表页和当日详情页均可访问。命令和检查项见 `references/flow.md`。
+
+### Phase 5：等待 IMA 选择
+
+Hugo 发布选择与知识沉淀选择相互独立。询问用户哪些文章值得长期保存：
+
+- 仅处理用户明确选择的文章；
+- 不把整份日报上传到 IMA；
+- 本次选择本身即授权后续单篇摘要和 IMA 上传，不重复确认。
+
+### Phase 6：生成单篇知识笔记
+
+对每篇选中文章：
+
+1. 通过 URL/完整 `item_id` 找到对应 extracted Artifact；
+2. 使用 `start_article_summary_job(extracted_path=<返回的 Artifact 路径>, selected_ids=[<完整 item_id>])`；
+3. 轮询 `get_article_summary_job_status`，成功后读取 `get_article_summary_job_result`；
+4. 只使用现有 `article.plain_text`，不重新抓取原 URL；
+5. 使用返回的 `written_paths` 定位结果并检查 Markdown 内容。
+6. **`extracted_path` 必须传绝对路径**（前缀 `/home/ubuntu/zhu/github/reader/`）：summary-mcp 工作目录是 `/root/.hermes`，相对路径会报 `extracted_path does not exist`。同一工具连续 3 次失败会触发 MCP 冷却（约 45-60s，报 `MCP server 'reader' is unreachable`），等待冷却后再重试，不要循环重试同一调用。
+
+异步 MCP 不可用时才使用项目 CLI fallback。不要因为内容较短而引入原文之外的知识。
+
+### Phase 7：上传到 IMA
+
+上传前按需读取：
+
+- 格式规则：`references/ima-format-quickref.md`
+- API 和上传步骤：`references/ima-upload-api.md`（含 `-200` 版本拦截修复）
+- 凭证定位：`references/ima-credential-chain.md`
+- 批量上传脚本：`scripts/ima_upload_one.py`（Python 编排，规避中文文件名 bash 引号问题）
+
+硬规则：
+
+- 使用完整文章标题作为文件名；
+- 以 Markdown 文件 `media_type=7` 上传到 `daily` knowledge base；
+- 保留原文 URL 和 Category，保证来源可追踪；
+- 不使用 URL 导入或 Notes 类型替代知识库文件；
+- 上传后验证目标知识库中存在对应条目；
+- 失败时报告具体阶段，不无限重试。
+
+## 关键词治理路由
+
+只有用户明确要求“清理关键词”“词库治理”等操作时才触发关键词治理。Review bundle 和 Suggestions 生成委托给 `keyword-cleanup-review`，确认与 Apply 仍由当前编排层负责：
+
+1. 生成 review bundle；
+2. 生成规则层与可选语义层 Suggestions JSON；
+3. 等待人工确认；
+4. dry-run 后按精确 accept 参数 Apply。
+
+`reader-digest-flow` 不直接编辑 `term_aliases.json`、`term_stopwords.json` 或兴趣配置。简要路由见 `references/keyword-engine-maintenance.md`。
+
+## 环境坑位（本机部署）
+
+- **MCP 相对路径陷阱**：`summary-mcp` 服务工作目录是 `/root/.hermes`，不是 reader 项目根。MCP 返回的 `output_dir`/`artifact.path` 是相对路径，直接传给 `start_article_summary_job(extracted_path=...)` 会报 `extracted_path does not exist`。传入前必须拼绝对路径前缀 `/home/ubuntu/zhu/github/reader/`。
+- **提取失败不等于运行失败**：`status_counts.extract_failed` 的条目（`CONTENT_EXTRACTION_FAILED`，`retryable=false`）跳过即可并如实汇报；失败文章常是推广/活动等低价值内容，不因此自行重跑。`linked_run_status=partial` 时先读 run-report 的 item 级 `error` 确认原因。
+- **用户要求"重新跑一批"**：候选质量低（用户主动提出）时重跑，应 `include_read=true` 并调高 `limit`（如 10），否则默认 `include_read=false` 会拉回同一批未读文章。重跑是新 Run，候选编号体系重新建立，汇报时提醒用户按新列表选择。
+
+## 停止与人工介入
+
+出现以下任一情况时停止自动流程并报告：
+
+- 用户没有授权运行、发布或知识库写入；
+- Job/Run 返回不可恢复，或连续恢复失败；
+- Payload、候选 ID 或 Artifact 之间无法可靠关联；
+- 生成内容缺少可追踪来源；
+- Hugo 部署验证失败；
+- IMA 凭证、目标知识库或上传结果无法验证。
+
+## Reference 路由
+
+- `references/flow.md`：具体 MCP 调用序列、候选映射（含 extracted_path 绝对路径、候选≠文件名顺序）、Hugo 发布和 IMA 主步骤。
+- `references/content-extraction.md`：FreshRSS 内容来源与 `plain_text` 质量判断。
+- `references/public-digest-example.md`：可直接参考的 Hugo 最终页面结构。
+- `references/feishu-format-notes.md`：Feishu 输出格式限制。
+- `references/ima-format-quickref.md`：IMA Markdown 格式规则。
+- `references/ima-upload-api.md`：IMA Markdown 文件上传 API（含 `-200` 版本拦截修复）。
+- `references/ima-credential-chain.md`：IMA 凭证与知识库配置定位。
+- `references/keyword-engine-maintenance.md`：关键词治理 Skill 路由。
+- `scripts/ima_upload_one.py`：单篇 Markdown 上传 daily 知识库的完整 Python 脚本（preflight→重名→create_media→COS→add_knowledge）。
@@ -0,0 +1,45 @@
+# 内容提取流程
+
+本文说明处理流水线如何把 FreshRSS 条目转换为可供摘要使用的文章文本。
+
+## 核心规则：FreshRSS 条目不重新抓取原文 URL
+
+**FreshRSS 是仅提供 RSS 内容的上游。** 对于 FreshRSS 条目，流水线不会向文章原始 URL 发起 HTTP 请求。该行为由 `pipeline.py` 中的 `RSS_ONLY_UPSTREAMS = {"freshrss"}` 强制保证。
+
+唯一例外是非 FreshRSS 上游。未来未设置 `upstream: freshrss` 的其他来源，可以在必要时使用 `fetch_html()` 作为回退。
+
+## 内容来源优先级
+
+`content_loader.py` 按以下顺序检查内容，并使用第一个包含 **至少 500 个可读字符** 的来源：
+
+| 优先级 | 来源 | 含义 |
+|--------|------|------|
+| 1 | `raw_html` | 通过 `ExtractionInput.raw_html` 预先注入的 HTML；常规 FreshRSS 运行中很少使用。 |
+| 2 | `item.raw_content` | RSS `<content:encoded>` 中的文章正文；部分订阅源提供，部分不提供。 |
+| 3 | `item.raw_summary` | RSS `<description>` 中的摘要或片段；这是当前运行中最常见的来源。 |
+| 4 | `rss_content` | 来自非条目字段的独立 RSS 内容。 |
+| — | `none` | 没有可用内容；FreshRSS 不允许回源抓取，因此抛出 `RSS_CONTENT_MISSING`。 |
+
+## `content_source` 与文本质量的关系
+
+每个 `item-XX.extracted.json` 中的 `content_source` 字段表示流水线实际使用的内容来源：
+
+- **`item.raw_content`**：RSS `<content:encoded>` 提供的文章正文，通常质量最好，接近直接阅读原文。
+- **`item.raw_summary`**：只有 RSS 摘要或描述，并非完整正文。不同来源长度差异较大，通常为 300-2000 个字符；AI 摘要基于该片段，而不是完整文章。
+- **`rss_content`**：来自独立 RSS 内容，质量取决于订阅源。
+- **`fetched_html`**：从原始 URL 抓取的 HTML。FreshRSS 条目不会出现该来源，只适用于非 FreshRSS 上游。
+
+## 对日报质量的影响
+
+如果提取结果文件中出现 `content_source: item.raw_summary`，说明 AI 使用的是订阅源摘要或片段，而不是完整正文。日报内容显得较浅时，原因可能只是 RSS 描述过短。
+
+提高质量可以选择提供完整 `<content:encoded>` 的订阅源，或者把内容来源切换到支持全文 RSS 的系统，例如具备全文提取能力的 RSS 代理或 FiveFilters 等服务。
+
+## 快速检查
+
+先调用 `list_run_artifacts(run_id)`，再读取返回的提取结果产物路径，检查 `content_source` 和 `article.plain_text`。不要按 `run_id` 或“最新目录”手拼路径。
+
+## 相关代码路径
+
+- `src/summary_mcp/core/pipeline.py`：`RSS_ONLY_UPSTREAMS`、`_should_skip_fetch()`、`extract_content()`。
+- `src/summary_mcp/core/content_loader.py`：`choose_inline_content()` 的优先级链和 `fetch_html()`；FreshRSS 条目不会调用后者。
@@ -0,0 +1,17 @@
+# 飞书 Markdown 格式说明
+
+## 背景
+
+Hermes 的飞书网关（`gateway/platforms/feishu.py`）通过 `_build_outbound_payload` 发送消息。该方法会检查内容中的 Markdown 特征，并据此决定消息类型：
+
+- 内容匹配 `_MARKDOWN_HINT_RE`（加粗、列表、代码、链接等）时，使用包含 `md` 元素的飞书 `post` 类型发送，可以正常渲染。
+- 内容匹配 `_MARKDOWN_TABLE_RE`（Markdown 表头和分隔行）时，整条消息会被强制转换为 `text` 类型，即纯文本，不再渲染 Markdown。
+
+原因是 `_build_markdown_post_payload` 会把内容包装为 `{"tag": "md", "text": "..."}` 元素，而飞书的 `md` 元素不支持表格，也没有把 Markdown 表格转换为飞书原生表格的逻辑。
+
+## 飞书输出规则
+
+- 通过飞书发送的消息不得使用 Markdown 表格；消息中只要出现一个表格，整条消息就会退化为纯文本。
+- 需要表达结构化信息时，优先使用分点列表、带标题的分节或行内格式。
+- 加粗（`**加粗**`）、行内代码（`` `代码` ``）、无序列表（`- 项目`）、有序列表（`1. 项目`）和链接均可正常使用。
+- 围栏式代码块可以使用，但代码块后的尾随内容可能存在渲染边界问题。
@@ -0,0 +1,177 @@
+# Reader Digest Flow 操作参考
+
+本文件承载 `reader-digest-flow` 的具体执行步骤。正式行为边界以 `../SKILL.md` 为准。
+
+## 1. 日报 Pipeline
+
+### 默认参数
+
+```json
+{
+  "limit": 7,
+  "include_read": false,
+  "mark_read": true,
+  "debug_artifacts": false,
+  "timeout_seconds": 60,
+  "max_retries": 2
+}
+```
+
+### 正式调用序列
+
+```text
+start_freshrss_pipeline_job
+→ get_freshrss_pipeline_job_status
+→ get_freshrss_pipeline_job_result
+→ get_run_status
+→ get_delivery_payload / get_run_report
+```
+
+状态动作：
+
+- `running`：按合理间隔继续轮询；
+- `success`：读取结果，保存 `run_id`；
+- `failed`：读取关联 Run 并执行 Resume Plan；
+- `partial`：优先检查 Run Report、Artifact 和 Recovery 信息。
+
+恢复序列：
+
+```text
+inspect_resume_plan
+→ recommended_action=resume
+→ start_resume_job
+→ get_resume_job_status
+→ get_resume_job_result
+```
+
+如果建议为 `read_terminal_result`，直接读取结果；如果为 `start_new_run`，停止并等待用户决定。
+
+不要根据 `run_id` 拼目录。后续只消费调用返回的 `output_dir`、`artifact.path`、`delivery_output`、`report_output`。
+
+## 2. 候选汇报与选择
+
+优先使用当前 Run 的 digest brief Artifact；不可用时使用 `get_delivery_payload` 返回的候选。
+
+展示规则：
+
+1. 使用候选数组原始顺序并从 1 编号；
+2. 不因 `keep/review` 分组而重新编号；
+3. 每篇展示标题、来源、摘要和判断理由；
+4. 用户编号只映射当前候选数组；
+5. 跨 Artifact 读取详情时按 URL 或完整 `item_id` 匹配。
+
+不要按 `summary-batch`、`item-XX` 和候选数组的相同位置推断它们是同一篇文章。
+
+### extracted 文件与候选编号错位（实测 2026-07-31）
+
+- `digest-brief.json` 的 `top_candidates` **没有 `item_id` 字段**，只有 `url`；
+- `extracted/item-XX.extracted.json` 的文件名序号与候选编号**可能不一致**（实例：候选2 = item-05、候选3 = item-02）；
+- 正确做法：用 **URL 交叉匹配**（归一化 `%3D`→`=` 后逐条比对），或用完整 `item_id`（从 candidate-batch.json 的 `items[i].item_id` 按候选数组顺序取）在 extracted 文件里反查；两者都能验证时优先 item_id。
+
+## 3. Hugo 日报
+
+用户确认发布文章后：
+
+1. 读取 `public-digest-example.md`；
+2. 仅使用用户选中的文章生成公开内容；
+3. 写入 Hugo 当日页面；
+4. 前台执行部署，避免把构建日志作为聊天通知；
+5. 验证首页、列表页和详情页。
+
+当前部署位置：
+
+```text
+/home/ubuntu/zhu/apps/hugo-site/content/daily/YYYY-MM-DD/index.md
+```
+
+验证至少覆盖：
+
+```text
+http://127.0.0.1:14322/
+http://127.0.0.1:14322/daily/
+http://127.0.0.1:14322/daily/YYYY-MM-DD/
+```
+
+页面不存在时检查源文件、构建结果、容器状态和页面日期。需要 Docker/Hugo 深度排障时再读取部署项目自己的文档，不把历史事故规则复制回主 Skill。
+
+## 4. 单篇知识笔记
+
+用户确认 IMA 文章后：
+
+1. 从候选中取得 URL 和完整 `item_id`；
+2. 从 Run Artifact 中找到匹配的 extracted 文件；
+3. 交叉验证 `article.item_id` 或 URL；
+4. 对每个单篇文件调用 `start_article_summary_job(extracted_path=<返回的 Artifact 路径>, selected_ids=[<完整 item_id>])`；
+5. `selected_ids` 传完整、非空、去除 `cand:` 前缀后的 `item_id`；
+6. 从 Job 结果的 `written_paths` 读取 Markdown。
+
+### ⚠️ extracted_path 必须用绝对路径
+
+`summary-mcp` 进程的工作目录是 `/root/.hermes`（不是 reader 项目根）。传相对路径（如 `outputs/freshrss/...`）会直接报 `extracted_path does not exist`。必须传绝对路径：
+
+```text
+/home/ubuntu/zhu/github/reader/outputs/freshrss/rerun/<run_dir>/extracted/item-XX.extracted.json
+```
+
+### ⚠️ 候选编号 ≠ extracted 文件名顺序
+
+候选数组顺序与 extracted 文件名（`item-01`…`item-07`）**不一定对齐**（实测候选2 落在 item-05）。`digest-brief.json` 的候选**没有 `item_id` 字段**，只有 URL。可靠匹配方法：
+
+1. 从 `candidate-batch.json` 取每项完整 `item_id`（在 `candidate` 嵌套对象里，顶层 `item_key` 只是 `item-XX` 文件名序号）；
+2. 或按 URL 匹配：归一化（`%3D`→`=`）后与每个 extracted 文件的 `article.url` / `article.canonical_url` 比对；
+3. 绝不要按候选位置对应 extracted 文件序号。
+
+```python
+def norm(u): return u.replace('%3D','=').replace('%3d','=').strip()
+# 对每个 extracted 文件取 norm(article.url)，与候选 norm(url) 精确比对
+```
+
+正式序列：
+
+```text
+start_article_summary_job
+→ get_article_summary_job_status
+→ get_article_summary_job_result
+```
+
+内容依据是 extracted Artifact 中的 `article.plain_text`。不得重新抓取原 URL，不得使用原文之外的知识扩写。
+
+CLI 仅在异步 MCP 不可用或人工排障时使用：
+
+```bash
+python scripts/run_article_summaries.py \
+  --extracted <returned-extracted-path> \
+  --ids <full-item-id> \
+  --output-dir <explicit-output-dir>
+```
+
+## 5. IMA 上传
+
+用户在知识沉淀阶段的文章选择即为上传授权。
+
+执行顺序：
+
+1. 检查生成的 Markdown 与来源；
+2. 文件名规范化为 `<完整文章标题>.md`；
+3. 确认目标为 `daily` knowledge base；
+4. 执行 preflight、create_media、COS upload、add_knowledge；
+5. 验证知识库条目存在。
+
+上传格式与 API 参数分别见：
+
+- `ima-format-quickref.md`
+- `ima-upload-api.md`
+- `ima-credential-chain.md`
+
+失败时记录发生在 preflight、create_media、COS upload 或 add_knowledge 的具体阶段。不要把失败的 Markdown 改写成 URL 导入或 Notes 类型来绕过错误。
+
+## 6. CLI fallback 原则
+
+CLI 仅在以下场景使用：
+
+- MCP 服务不可用；
+- Tool transport/launch 失败且无法取得有效 Job；
+- 用户明确要求本地调试；
+- 人工排障需要直接检查脚本输出。
+
+如果已取得 `job_id` 或 `run_id`，先查询真实状态，避免因响应超时重复启动任务。
@@ -0,0 +1,27 @@
+# IMA 凭证与安全边界
+
+## 必需配置
+
+- `IMA_OPENAPI_CLIENTID`
+- `IMA_OPENAPI_APIKEY`
+- `IMA_DAILY_KNOWLEDGE_BASE_ID`
+- `IMA_DAILY_KNOWLEDGE_BASE_NAME=daily`
+
+优先使用当前进程环境和 IMA Skill 已支持的凭证加载机制。不要在本 Skill 中复制、迁移或重写密钥文件。
+
+## 缺失处理
+
+Preflight 返回凭证缺失或目标知识库无法解析时：
+
+1. 停止上传；
+2. 只报告缺失的变量名或配置项；
+3. 等待用户或运行环境补齐配置；
+4. 配置恢复后重新执行 preflight，不重复生成知识笔记。
+
+## 安全边界
+
+- 不在聊天、日志或命令输出中打印完整 API key、KB ID 或 COS 临时凭证；
+- `create_media` 返回的 COS 凭证仅在同一受控进程内传给上传工具，不写入磁盘；
+- 不通过拼接 Shell 字符串传递凭证，使用参数数组或 IMA Skill 的封装；
+- 不绕过 Hermes 的脱敏机制；若现有工具链无法安全传递凭证，停止并报告；
+- 上传结束后不持久化 COS 临时凭证。
@@ -0,0 +1,47 @@
+# IMA Markdown 格式速查
+
+## 文件与标题
+
+- 文件名：`<完整文章标题>.md`
+- `add_knowledge.title`：完整文章标题，不包含 `.md`
+- 上传类型：Markdown 文件，`media_type=7`
+- 目标：`daily` knowledge base
+
+## 内容来源
+
+只能使用当前 Run 的可追踪内容：
+
+1. extracted Artifact 的 `article.plain_text`；
+2. 对应文章的结构化摘要；
+3. digest brief 的 summary 与 highlights。
+
+不得使用通用知识补写原文没有的信息，不设置固定字数或字节数门槛。内容较短时保持简洁并忠于来源。
+
+## 标准结构
+
+```markdown
+# 完整文章标题
+
+Source: https://原文链接
+Category: 分类
+
+## 核心结论
+
+## 主要论点
+
+## 关键方法 / 机制
+
+## 重要细节
+
+## 可复用启发
+
+## 关键词
+
+## 主题
+```
+
+- 核心结论和主要论点使用连贯段落；
+- 方法、细节和启发按完整知识点分项；
+- 没有来源支持的 Section 可以简写，不得编造内容填充。
+
+上传 API 见 `ima-upload-api.md`。
@@ -0,0 +1,126 @@
+# IMA Markdown 上传 API
+
+用于将用户选中的单篇 Markdown 知识笔记上传到 `daily` knowledge base。
+
+## 凭证
+
+- `IMA_OPENAPI_CLIENTID`
+- `IMA_OPENAPI_APIKEY`
+- `IMA_DAILY_KNOWLEDGE_BASE_ID`
+- `IMA_DAILY_KNOWLEDGE_BASE_NAME`
+
+凭证定位和恢复见 `ima-credential-chain.md`。不要在终端输出完整密钥。
+
+## 上传前检查
+
+- 文件名为 `<完整文章标题>.md`；
+- `title` 为完整文章标题，不带 `.md`；
+- Markdown 符合 `ima-format-quickref.md`；
+- 内容可追溯到当前 Run Artifact；
+- 用户已经明确选择该文章；
+- 目标知识库已经解析并验证。
+
+## 1. Preflight
+
+调用 IMA Skill 的 `preflight-check.cjs` 检查文件类型、扩展名、大小和 MIME。
+
+预期：
+
+```text
+file_ext=md
+content_type=text/markdown
+media_type=7
+```
+
+### ⚠️ IMA skill 版本拦截（-200）
+
+`ima_api.cjs` 每天首次调用会检查更新，若检测到新版（如 1.1.8 > 当前 1.1.7）会以 `code=-200` 拦截原请求。注意：**官方 zip 包内的 `meta.json` 可能没同步版本号**（下载 1.1.8 zip 后 meta 仍写 1.1.7），所以光替换文件无法跳过拦截。
+
+快速修复（脚本本身已是新版，只差版本号）：
+
+```bash
+cd /root/.hermes/skills/openclaw-imports/ima-skill && python3 -c "
+import json
+m = json.load(open('meta.json')); m['version'] = '1.1.8'
+json.dump(m, open('meta.json','w'), ensure_ascii=False, indent=2)
+"
+```
+
+先用 `diff -rq` 对比 zip 与安装目录：若只有 `.DS_Store`/meta 差异，说明代码已是最新，直接改 meta.json 版本号即可；若脚本有实质差异才需要整体替换。
+
+## 2. Create Media
+
+```text
+POST /openapi/wiki/v1/create_media
+```
+
+请求核心字段：
+
+```json
+{
+  "file_name": "<完整文章标题>.md",
+  "file_size": 0,
+  "content_type": "text/markdown",
+  "knowledge_base_id": "<daily-kb-id>",
+  "file_ext": "md"
+}
+```
+
+保存返回的 `media_id` 和 `cos_credential`。COS 临时凭证只在进程内传递，不打印到聊天或日志。
+
+## 3. COS Upload
+
+使用 IMA Skill 提供的 `cos-upload.cjs`，通过参数数组调用并检查：
+
+- 进程 `returncode`；
+- `stderr`；
+- HTTP 上传结果。
+
+不要拼接包含凭证的 Shell 字符串，也不要把多条 JSON 响应重定向到同一个文件。
+
+## 4. Add Knowledge
+
+```text
+POST /openapi/wiki/v1/add_knowledge
+```
+
+核心字段：
+
+```json
+{
+  "media_type": 7,
+  "media_id": "<media-id>",
+  "title": "<完整文章标题>",
+  "knowledge_base_id": "<daily-kb-id>",
+  "file_info": {
+    "cos_key": "<cos-key>",
+    "file_size": 0,
+    "file_name": "<完整文章标题>.md"
+  }
+}
+```
+
+## 5. 验证
+
+上传成功不能只依据本地命令退出码。验证目标 knowledge base 中存在对应标题或返回对象，并向用户报告最终结果。
+
+失败时明确报告发生在 Preflight、Create Media、COS Upload 或 Add Knowledge 的哪一步，不改用 URL 导入或 Notes 类型绕过错误。
+
+## 6. 已知坑：IMA skill 版本拦截（-200）
+
+`ima_api.cjs` 每天首次调用会检查远端版本，若发现新版本（如 1.1.8 > 1.1.7）会以 exit=1 + stderr `{"code":-200}` 拦截**所有** API 调用，原请求不发送。此前遇到过。
+
+处理方式（不必整包替换）：
+
+1. 按 stderr 提示下载新版 zip（如 `https://app-dl.ima.qq.com/skills/ima-skills-1.1.8.zip`）并解压；
+2. 对比新旧 `ima_api.cjs` 的 md5——zip 内核心脚本常与本地一致，只是 `meta.json` 的 `version` 未同步（zip 内仍写 1.1.7）；
+3. 若 `ima_api.cjs` 一致，只需把本地 `meta.json` 的 `version` 改为远端版本号即可跳过拦截，无需替换文件。
+
+调用成功后再执行本文件前面的上传流程。
+
+## 6. 版本拦截与批量上传实测（2026-07-31）
+
+- **`-200` skill 更新拦截**：`ima_api.cjs` 每天首次调用检查版本，发现新版时以 code -200 退出并提示更新。下载 zip 后**先对比 `ima_api.cjs` 的 md5**——实测 zip 内脚本与已装版本完全一致，只是 `meta.json` 版本号未同步。此时只需把 `~/.hermes/skills/openclaw-imports/ima-skill/meta.json` 的 `version` 改为最新版即可跳过拦截，无需替换任何脚本。
+- **Python 脚本编排上传**比 bash 可靠：bash 拼接含中文文件名/凭证的 curl 易出错。用 `subprocess` 参数数组依次调 `preflight-check.cjs` → `ima_api.cjs check_repeated_names` → `create_media` → `cos-upload.cjs`（`--secret-id/--secret-key/--token` 走参数数组，不打印）→ `add_knowledge`，每步解析返回 JSON，失败即停。
+- **批量上传**：4 篇逐个跑同一脚本即可；同名文件先 `check_repeated_names` 确认无重复。
+- 凭证从 `IMA_OPENAPI_CLIENTID` / `IMA_OPENAPI_APIKEY` 环境变量读取（`ima_api.cjs` 自动加载），KB ID 用 `IMA_DAILY_KNOWLEDGE_BASE_ID`。
@@ -0,0 +1,29 @@
+# 关键词治理路由
+
+关键词治理不属于 `reader-digest-flow` 的日常执行阶段。
+
+仅当用户明确要求“清理关键词”“词库治理”“生成关键词建议”时，委托：
+
+```text
+skills/keyword-cleanup-review/SKILL.md
+```
+
+Review 输入生成由该 Skill 定义，后续确认与 Apply 由当前编排层负责：
+
+```text
+build review bundle
+→ generate rule suggestions
+→ optional semantic suggestions
+→ human review
+→ dry-run
+→ apply accepted suggestions
+```
+
+约束：
+
+- Suggestions JSON 是 Review 与 Apply 之间的正式契约；
+- LLM 语义建议不能自动 Apply；
+- 不直接编辑 aliases、stopwords、watchlist 或 interest 配置；
+- 不在日报主流程中因 tag 质量不佳自动触发治理。
+
+Review bundle、Suggestions、Schema 和产物保留策略以 `keyword-cleanup-review` 为唯一事实来源；该 Skill 不直接 Apply 配置。
@@ -0,0 +1,31 @@
+++
+title = "AI 日报 · 示例"
+date = 2026-04-01T09:00:00+08:00
+summary = "围绕 Agent 架构分层、Skills 标准化与工程实践的当日观察。"
+++
+
+# 今日概览
+
+今天的公开内容主要集中在 AI Agent 架构演进、工具化落地与工程实践三条线索。行业关注点正在从“模型能做什么”转向“系统如何稳定落地并持续复用”。
+
+## 今日重点
+
+### 1. 从 Agent 到 Skills：AI 智能体架构的范式转变
+
+文章分析了 AI 智能体从单体 Agent 向模块化 Skills 的演进，并结合 MCP、Skills 和真实项目说明能力分层与复用方式。
+
+值得关注：
+
+- Skills 将领域流程从 Agent 主体中拆出，便于复用和维护。
+- MCP 为 Agent 与外部工具提供标准化连接方式。
+- 工程竞争点逐渐从模型调用转向状态、工具和工作流设计。
+
+这篇内容值得关注的原因在于，它把开放协议、分层架构和真实落地案例连接成了完整论证链。
+
+来源：[示例来源](https://example.com/a)
+
+## 趋势观察
+
+1. Agent 正在从单体能力转向可组合的模块化体系。
+2. 工具契约、状态管理和验证机制正在成为 AI 应用的核心工程能力。
+3. Human-in-the-loop 仍是控制高风险副作用的重要边界。
@@ -0,0 +1,96 @@
+#!/usr/bin/env python3
+"""IMA 上传单篇 Markdown 知识笔记到 daily 知识库。
+用法: python3 ima_upload_one.py "<绝对路径/summary.md>" "<完整文章标题>"
+依赖环境变量: IMA_OPENAPI_CLIENTID / IMA_OPENAPI_APIKEY / IMA_DAILY_KNOWLEDGE_BASE_ID
+流程: preflight -> check_repeated_names -> create_media -> cos-upload -> add_knowledge
+说明: 用 Python 而非 bash 编排，避免中文文件名/引号转义问题。
+退出码 2 = 文件名重复（需与用户确认保留双方或取消），非 0 均为失败。
+"""
+import json, os, subprocess, sys
+
+SKILL_DIR = "/root/.hermes/skills/openclaw-imports/ima-skill"
+IMA_API = os.path.join(SKILL_DIR, "ima_api.cjs")
+COS_UPLOAD = os.path.join(SKILL_DIR, "knowledge-base/scripts/cos-upload.cjs")
+PREFLIGHT = os.path.join(SKILL_DIR, "knowledge-base/scripts/preflight-check.cjs")
+
+def run_node(script, args):
+    r = subprocess.run(["node", script] + args, capture_output=True, text=True, timeout=120)
+    if r.returncode != 0:
+        raise RuntimeError(f"{script} exit={r.returncode} stderr={r.stderr[:500]}")
+    return json.loads(r.stdout)
+
+def ima_api(api_path, body):
+    r = subprocess.run(["node", IMA_API, api_path, json.dumps(body, ensure_ascii=False)],
+                       capture_output=True, text=True, timeout=120)
+    if r.returncode != 0:
+        raise RuntimeError(f"ima_api {api_path} exit={r.returncode} stderr={r.stderr[:500]}")
+    resp = json.loads(r.stdout)
+    if resp.get("code") != 0:
+        raise RuntimeError(f"ima_api {api_path} code={resp.get('code')} msg={resp.get('msg')}")
+    return resp.get("data", {})
+
+def main():
+    kb_id = os.environ["IMA_DAILY_KNOWLEDGE_BASE_ID"]
+    file_path = sys.argv[1]
+    title = sys.argv[2]  # 完整文章标题（不含 .md）
+
+    pf = run_node(PREFLIGHT, ["--file", file_path])
+    if not pf.get("pass"):
+        raise RuntimeError(f"preflight failed: {pf}")
+    file_name = pf["file_name"]; media_type = pf["media_type"]
+    content_type = pf["content_type"]; file_size = pf["file_size"]; file_ext = pf["file_ext"]
+    print(f"[preflight] ok file={file_name} ext={file_ext} size={file_size} media_type={media_type}")
+
+    dup = ima_api("openapi/wiki/v1/check_repeated_names", {
+        "params": [{"name": file_name, "media_type": media_type}],
+        "knowledge_base_id": kb_id
+    })
+    is_rep = dup.get("results", [{}])[0].get("is_repeated", False) if dup.get("results") else False
+    if is_rep:
+        print(f"[check_repeated_names] REPEATED: {file_name} — 需要处理")
+        sys.exit(2)
+    print("[check_repeated_names] no duplicate")
+
+    cm = ima_api("openapi/wiki/v1/create_media", {
+        "file_name": file_name,
+        "file_size": file_size,
+        "content_type": content_type,
+        "knowledge_base_id": kb_id,
+        "file_ext": file_ext
+    })
+    media_id = cm["media_id"]
+    cos = cm["cos_credential"]
+    print(f"[create_media] media_id={media_id} cos_bucket={cos.get('bucket_name')} cos_key={cos.get('cos_key','')[:40]}")
+
+    r = subprocess.run(["node", COS_UPLOAD,
+        "--file", file_path,
+        "--secret-id", cos["secret_id"],
+        "--secret-key", cos["secret_key"],
+        "--token", cos["token"],
+        "--bucket", cos["bucket_name"],
+        "--region", cos["region"],
+        "--cos-key", cos["cos_key"],
+        "--content-type", content_type,
+        "--start-time", str(cos.get("start_time", "")),
+        "--expired-time", str(cos.get("expired_time", "")),
+        "--timeout", "300000"
+    ], capture_output=True, text=True, timeout=360)
+    if r.returncode != 0:
+        raise RuntimeError(f"cos-upload exit={r.returncode} stderr={r.stderr[:800]}")
+    print(f"[cos-upload] ok rc=0 stdout={r.stdout.strip()[:200]}")
+
+    ak = ima_api("openapi/wiki/v1/add_knowledge", {
+        "media_type": media_type,
+        "media_id": media_id,
+        "title": title,
+        "knowledge_base_id": kb_id,
+        "file_info": {
+            "cos_key": cos["cos_key"],
+            "file_size": file_size,
+            "file_name": file_name
+        }
+    })
+    print(f"[add_knowledge] ok media_id={ak.get('media_id') or media_id}")
+
+if __name__ == "__main__":
+    main()
@@ -6,8 +6,30 @@ from .freshrss_pipeline_jobs import (
    start_freshrss_pipeline_job,
 )
 from .query_service import get_delivery_payload, get_run_report, get_run_status, list_run_artifacts, list_runs
-from .resume_jobs import get_resume_job_result, get_resume_job_status, start_resume_job
-from .resume_service import inspect_resume_plan, resume_run
+
+# NOTE: resume_jobs and resume_service are NOT eagerly imported here to avoid
+# a circular import chain:
+#   workflows/freshrss_pipeline.py -> runtime -> resume_jobs -> resume_service
+#   -> workflows/freshrss_pipeline.py (circular!)
+# They are lazy-loaded via __getattr__ when accessed as summary_mcp.runtime.*
+
+
+def __getattr__(name):
+    import importlib
+
+    _LAZY = {
+        "get_resume_job_result": ("resume_jobs", "get_resume_job_result"),
+        "get_resume_job_status": ("resume_jobs", "get_resume_job_status"),
+        "start_resume_job": ("resume_jobs", "start_resume_job"),
+        "inspect_resume_plan": ("resume_service", "inspect_resume_plan"),
+        "resume_run": ("resume_service", "resume_run"),
+    }
+    if name in _LAZY:
+        mod_name, attr_name = _LAZY[name]
+        mod = importlib.import_module(f".{mod_name}", __package__)
+        return getattr(mod, attr_name)
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+

 __all__ = [
    "ArtifactRecord",
@@ -9,6 +9,8 @@ from pathlib import Path
 from typing import Any
 from uuid import uuid4

+from dotenv import dotenv_values
+
 from .run_store import RunStore
 from .query_service import _resolve_run_record

@@ -29,6 +31,25 @@ DEFAULT_STAGES = [
 ]
 MIN_JOB_STALE_SECONDS = 30 * 60
 MAX_JOB_STALE_SECONDS = 6 * 60 * 60
+DEFAULT_DOTENV_PATH = REPO_ROOT / ".env"
+
+
+def _build_subprocess_env() -> dict[str, str]:
+    """Build an env dict for subprocess, merging parent env with .env values.
+
+    The subprocess inherits the Hermes MCP server's environment, but .env values
+    may not be in os.environ at the time the subprocess is spawned. This function
+    loads them from .env and merges them in so the child process sees all needed
+    variables (LLM_API_KEY, LLM_MODEL, LLM_API_URL, FRESHRSS_*, etc.) directly
+    in os.environ, avoiding any dotenv-loading timing issues inside the subprocess.
+    """
+    env = os.environ.copy()
+    if DEFAULT_DOTENV_PATH.exists():
+        for key, value in dotenv_values(DEFAULT_DOTENV_PATH).items():
+            if isinstance(key, str) and isinstance(value, str) and value:
+                # Only set if not already present in parent env
+                env.setdefault(key, value)
+    return env


 def _now() -> datetime:
@@ -218,6 +239,7 @@ def start_freshrss_pipeline_job(
            stdout=subprocess.DEVNULL,
            stderr=subprocess.DEVNULL,
            start_new_session=True,
+            env=_build_subprocess_env(),
        )
    except Exception as exc:
        report_file = _write_job_report(
@@ -9,6 +9,8 @@ from pathlib import Path
 from typing import Any
 from uuid import uuid4

+from dotenv import dotenv_values
+
 from .query_service import _resolve_run_record
 from .resume_service import (
    SUPPORTED_RESUME_STAGES,
@@ -38,6 +40,22 @@ DEFAULT_STAGES = [
 ]
 MIN_JOB_STALE_SECONDS = 30 * 60
 MAX_JOB_STALE_SECONDS = 6 * 60 * 60
+DEFAULT_DOTENV_PATH = REPO_ROOT / ".env"
+
+
+def _build_subprocess_env() -> dict[str, str]:
+    """Build an env dict for subprocess, merging parent env with .env values.
+
+    Ensures the subprocess sees all needed variables (LLM_API_KEY, LLM_MODEL,
+    LLM_API_URL, FRESHRSS_*, etc.) directly in os.environ, avoiding dotenv
+    timing issues in the child process.
+    """
+    env = os.environ.copy()
+    if DEFAULT_DOTENV_PATH.exists():
+        for key, value in dotenv_values(DEFAULT_DOTENV_PATH).items():
+            if isinstance(key, str) and isinstance(value, str) and value:
+                env.setdefault(key, value)
+    return env


 def _now() -> datetime:
@@ -276,6 +294,7 @@ def start_resume_job(*, run_id: str) -> dict[str, Any]:
            stdout=subprocess.DEVNULL,
            stderr=subprocess.DEVNULL,
            start_new_session=True,
+            env=_build_subprocess_env(),
        )
    except Exception as exc:
        report_file = _write_job_report(
@@ -2,6 +2,7 @@ from __future__ import annotations

 import json
 import os
+from concurrent.futures import ThreadPoolExecutor, as_completed
 from datetime import date, datetime, timezone
 from pathlib import Path
 from typing import Any
@@ -192,7 +193,7 @@ def _build_candidate_batch_payload(*, run_id: str, item_contexts: list[dict[str,
    }


-def _persist_summary_batch_artifact(*, run_store: RunStore, run_dir: Path, item_contexts: list[dict[str, Any]]) -> Path:
+def _persist_summary_batch_artifact(*, run_store, run_dir: Path, item_contexts: list[dict[str, Any]]) -> Path:
    output_path = _summary_batch_output(run_dir)
    _save_json(
        output_path,
@@ -202,7 +203,7 @@ def _persist_summary_batch_artifact(*, run_store: RunStore, run_dir: Path, item_
    return output_path


-def _persist_candidate_batch_artifact(*, run_store: RunStore, run_dir: Path, item_contexts: list[dict[str, Any]]) -> Path:
+def _persist_candidate_batch_artifact(*, run_store, run_dir: Path, item_contexts: list[dict[str, Any]]) -> Path:
    output_path = _candidate_batch_output(run_dir)
    _save_json(
        output_path,
@@ -461,9 +462,12 @@ def run_freshrss_pipeline(
        summary_success_count = 0
        summary_failed_count = 0
        summary_candidates = [ctx for ctx in item_contexts if ctx["extraction"] is not None and ctx["extraction"].success]
+        # Parallelize LLM summaries — I/O bound calls, independent per article
+        with ThreadPoolExecutor(max_workers=min(len(summary_candidates) or 1, 4)) as pool:
+            fut_map = {}
            for item_context in summary_candidates:
-            item_report = item_context["item_report"]
-            summary_exit_code, summary_payload, summary_report = run_loop_payload(
+                fut = pool.submit(
+                    run_loop_payload,
                    extracted_payload=item_context["extracted_payload"],
                    prompt_path=resolved_prompt_path,
                    output_path=item_context["summary_output"],
@@ -473,6 +477,16 @@ def run_freshrss_pipeline(
                    model=resolved_llm_model,
                    api_url=resolved_llm_api_url,
                )
+                fut_map[fut] = item_context
+
+            for fut in as_completed(fut_map):
+                item_context = fut_map[fut]
+                item_report = item_context["item_report"]
+                try:
+                    summary_exit_code, summary_payload, summary_report = fut.result()
+                except Exception as exc:
+                    summary_exit_code, summary_payload, summary_report = 1, None, None
+
                if summary_exit_code != 0 or summary_payload is None:
                    item_report["status"] = "summary_failed"
                    if summary_report is not None:
Author	SHA1	Message	Date
root	8e27cca166	docs: sync reader-digest-flow skill with Hermes version (absolute path, extract_failed handling, rerun include_read)	2026-08-03 09:55:19 +08:00
zhuyongxin	807027976e	docs: update digest template and localize references	2026-07-29 10:20:20 +08:00
zhuyongxin	6dd8cef347	refactor: simplify reader digest skill	2026-07-28 19:14:09 +08:00
root	5eb390e3ed	docs: 添加 Agent Skill 到项目仓库 - 复制 reader-digest-flow skill 到 skills/ 目录（含 SKILL.md + references/） - README 新增 Agent Skill 章节说明供 Agent 使用的工作流	2026-07-28 18:38:54 +08:00
root	7b791ac947	docs: 重写 README 为项目介绍从 MCP 集成技术文档改为完整项目 README，包含： - 项目定位与核心能力 - 架构概览（流程图 + MCP 工具层 + CLI） - 快速开始与环境变量 - 项目结构一览 - 关键技术决策说明 - 关键词治理流程	2026-07-28 16:26:34 +08:00
root	cdbcdcd485	feat: pipeline 并行摘要 + 子进程 env 注入 + 循环导入修复源码： - runtime/__init__.py: resume_jobs/resume_service 改为懒加载，打破循环导入 - freshrss_pipeline_jobs.py / resume_jobs.py: 子进程注入 .env 环境变量 - freshrss_pipeline.py: LLM 摘要串行改并行 (ThreadPoolExecutor, max_workers=4) 配置： - term_aliases: 19→149 条，大幅扩充别名映射 - term_stopwords: 19→132 条，增加过滤规则 - filter_context.personal.json: +7 个兴趣关键词	2026-07-28 16:24:31 +08:00
root	590d050218	keyword cleanup: v2 engine, alias rule layer, LLM semantic suggestions - build_review_bundle.py: 新增 _compute_percentile/_compute_growth, 候选池从固定阈值改为百分位排名 + 增速因子 (v2 policy) - term_cleanup_policy.json: 升级 v2 schema - generate_term_cleanup_suggestions.py: 新增 _prepare_alias_suggestions, 规则层输出 alias (大小写/单复数/分词变体) - generate_term_cleanup_semantic_suggestions.py: 新增 LLM 语义建议脚本 (DeepSeek API, 产出 semantic alias/stopword/promote) - SKILL.md: 更新为 5 Phase 工作流程 - 首轮清洗 apply: interest 54, aliases 17组, stopwords 17个 - docs/design/keyword-cleanup-flow-overview.md: 流程文档 - plans/: 引擎设计方案	2026-05-14 17:17:49 +08:00