Add article-deep-summary skill and enrich article summary prompt
- Create skills/article-deep-summary/SKILL.md and agents/openai.yaml - Relax prompt constraints to allow 2-4 sentence elaboration per field - Add validation output for reference article - Update plan checklist as completed
This commit is contained in:
zhuyongxin
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{
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{
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"title": "文章标题（与原文一致）",
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"title": "文章标题（与原文一致）",
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"url": "文章 URL（与原文一致）",
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"url": "文章 URL（与原文一致）",
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"core_conclusion": "作者最核心的结论，1-2 句，精准概括",
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"core_conclusion": "作者最核心的结论，2-4 句，精准概括，包含核心判断和支撑依据",
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"main_argument": "文章的主要论点或主张，可展开，允许多句",
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"main_argument": "文章的主要论点或主张，充分展开，可包含推理链条和论证结构",
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"key_methods": [
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"key_methods": [
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"关键方法、机制或技术手段，每条一句，3-6 条；无相关内容时返回空数组"
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"关键方法、机制或技术手段，每条 2-4 句展开说明原理或运作方式，3-6 条；无相关内容时返回空数组"
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],
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],
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"important_details": [
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"important_details": [
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"值得记录的细节、数据或案例，每条一句，3-6 条；无相关内容时返回空数组"
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"值得记录的细节、数据或案例，每条 2-4 句补充背景和意义，3-6 条；无相关内容时返回空数组"
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],
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],
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"reusable_insights": [
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"reusable_insights": [
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"可复用于其他场景的启发或观点，2-4 条"
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"可复用于其他场景的启发或观点，每条 2-3 句说明适用场景和具体做法，2-4 条"
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],
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],
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"keywords": [
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"keywords": [
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"具体实体、工具名、方法名，5-8 个"
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"具体实体、工具名、方法名，5-8 个"
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],
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],
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"category": "内容分类，从以下选项中选择一个：资讯 / 方法论 / 工具实践 / 观点评论",
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"category": "内容分类，从以下选项中选择一个：资讯 / 方法论 / 工具实践 / 观点评论",
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"worth_keeping": true,
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"worth_keeping": true,
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"reason": "沉淀理由，一句话说明为什么值得长期保留"
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"reason": "沉淀理由，2-3 句说明为什么值得长期保留，以及未来什么场景下可以回看"
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}
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}
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注意：
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注意：
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# 信息过载时代，我的漏斗式阅读工作流
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Source: https://shawnxie.top/blogs/tools/read-flow-2026.html
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Category: 方法论
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## 核心结论
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在信息过载时代，个人信息处理的核心目标不是获取更多信息，而是通过分层过滤和沉淀机制，稳定地吸收、判断和沉淀真正有价值的内容。作者认为，一个有效的个人信息系统应该是一个“漏斗”，而非“桶”，其关键在于将分散的处理环节串联成闭环，让信息在向下流动的过程中不断收窄，最终只留下值得进入长期记忆的部分，并能通过沉淀内容反向优化筛选逻辑。
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## 主要论点
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文章主张，应对信息过载不应依赖单一工具或追求全自动处理，而应构建一套分层、闭环的“漏斗式”个人信息系统。该系统以RSS等可控信息源为起点，通过聚合、预处理、AI精选、人工精读、知识沉淀和轻量反馈等多个层级，逐步过滤噪音、提炼精华，并将沉淀的高价值内容转化为个性化信号，反向优化上游筛选。其核心论点是：信息处理的难点在于串联分散环节，真正的价值在于实现“上游宽广、中游稳定、下游精准、回流轻柔”的认知加工流程，使人从被动接收者转变为拥有个性化认知处理系统的主人。
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## 关键方法 / 机制
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- 信息源归一化：以RSS为主信息基础设施，利用RSSHub、wewe-rss、nitter等工具或自定义脚本，将公众号、社区、社交平台等多样信息源统一转换为RSS或类feed格式，确保下游处理环节输入格式的稳定和统一。
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- 分层处理流程：设计清晰的多层处理架构。1) 用FreshRSS作为统一聚合池，管理所有订阅源，充当缓冲层。2) 用Digest模块进行预处理，完成URL去重、相似内容去重、正文抓取、质量检查、噪音过滤、摘要生成等任务，将原始信息整理为可判断对象。3) 用Daily Review进行AI精选，由LLM将预处理后的内容按固定栏目（如今日大事、变更与实践等）结构化，生成重点突出的日报。4) 设置人工精读环节（Human in the loop），由人最终判断内容的长期价值。5) 用Lumina知识库进行长期沉淀。6) 基于沉淀内容构建轻量兴趣画像，作为辅助信号轻微影响上游排序，形成闭环。
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- 自动化编排与集成：使用OpenClaw作为自动化编排层，通过自然语言描述诉求，串联定时触发、外部工具调用（如FreshRSS API）、与飞书/Lumina的交互、AI技能调用等任务，实现整个工作流的自动化运行和快速迭代，降低了构建完整系统的初期门槛。
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## 重要细节
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- FreshRSS的定位与作用：作者强调FreshRSS并非日常阅读工具，而是作为“中间水库”的聚合池。其核心作用是统一管理信息源格式、为下游环节提供稳定且无需直接联网的内容池、以及确保信息处理的时间连续性，避免了碎片化处理，是实现“有边界的信息处理”的关键缓冲层。
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- Digest预处理的具体任务：Digest模块承担了将“海量候选内容转化为可判断对象”的重任。其具体任务包括URL精确去重、相似内容去重、正文抓取、质量检查、噪音过滤、摘要生成和初步排序。这一环节大幅减少了标题党、重复报道和无效数据，显著降低了后续人工筛选的认知成本，是提升整个系统效率的基础。
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- Daily Review的栏目化设计：AI精选环节（Daily Review）并非简单聚合摘要，而是通过预设固定栏目进行结构化编辑。栏目包括“今日大事”、“变更与实践”、“安全与风险”、“开源与工具”、“洞察与数据点”、“主题深挖”等，将信息分配到不同的“认知槽位”，使最终产出更像一份重点突出的个性化日报，而非信息堆砌。
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- 轻量反馈与兴趣画像设计：为避免系统演变为封闭的“信息茧房”或过度迎合的推荐引擎，作者刻意设计了“轻量”的兴趣画像逻辑。它仅从长期精读主题、高价值信息源、偏好内容格式、Lumina存入内容类型等维度学习，作为辅助信号轻微影响Digest和Daily Review的排序，目标是减少无效判断，同时保留关注公共重要性和探索未知的空间。
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- 沉淀价值的拓展方向：信息沉淀的终点不仅是存储，还包括价值再生产。作者实践了两个方向：1) 周刊生成：基于一周的Digest、Daily Review和Lumina内容，生成带有个人筛选痕迹和深度分析的周刊，识别长期信号与短期噪音。2) 主题文章生成：系统识别一段时间内反复出现并被多次沉淀的主题（如AI Agent趋势），将其发展为可深入输出的长文主题，将离散信息流转化为结构化知识资产。
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## 可复用启发
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- 构建分层过滤的认知加工流程：在处理任何海量输入（如邮件、任务、学习资料）时，都可以借鉴“漏斗”思维。设计从“广泛捕获”到“逐步收窄”的多层处理机制，明确每一层的职责（如聚合、粗筛、精筛、决策、沉淀），而非试图一次性处理所有信息，这能大幅降低认知负担并提升处理质量。
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- 坚持“人在回路”的核心价值判断：在自动化系统中，尤其是在知识管理领域，最关键的长期价值判断（如“什么值得长期留存”、“什么对未来有深远影响”）不应完全外包给算法。应像本文一样，在流程的关键节点（如进入知识库前）设置人工确认环节，确保系统的输出最终服务于人的深度思考和决策。
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- 利用轻量反馈构建良性闭环系统：在设计个性化系统时，应避免构建强反馈、易导致信息茧房的推荐引擎。可以借鉴本文的“轻量引导”思路，仅从最核心、最长期的行为数据（如最终沉淀内容）中提取少量信号，温和地优化上游流程，在提升效率的同时保持系统的开放性和探索性。
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- 以“沉淀和再生产”作为流程终点：信息或知识管理的目标不应止步于“读完”或“保存”。应思考如何将处理后的高价值内容进一步转化为可复用的资产，例如定期生成汇总报告（如周刊）、识别并发展跨时间维度的核心主题、或将内化知识用于指导实践和输出，从而实现知识的增值和循环。
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## 关键词
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RSS、FreshRSS、OpenClaw、Lumina、RSSHub、wewe-rss、nitter、Digest
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## 主题
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知识管理、个人信息处理、工作流设计、自动化、阅读方法
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File diff suppressed because one or more lines are too long
@@ -34,14 +34,8 @@ python -m summary_mcp.workflows.article_summary \
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- 数据模型：src/summary_mcp/models/article_summary_result.py
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- 数据模型：src/summary_mcp/models/article_summary_result.py
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- Prompt：outputs/prompts/article-summary-prompt.txt
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- Prompt：outputs/prompts/article-summary-prompt.txt
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## Skill 文件结构（待创建）
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skills/article-deep-summary/
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SKILL.md
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agents/openai.yaml
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## 待办
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## 待办
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- [ ] 创建 skills/article-deep-summary/SKILL.md
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- [x] 创建 skills/article-deep-summary/SKILL.md
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- [ ] 创建 skills/article-deep-summary/agents/openai.yaml
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- [x] 创建 skills/article-deep-summary/agents/openai.yaml
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- [ ] 用实际 extracted 文件跑一次验证输出
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- [x] 用实际 extracted 文件跑一次验证输出（outputs/reference/article-summaries/信息过载时代，我的漏斗式阅读工作流.md）
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---
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name: article-deep-summary
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description: Generate a deep structured knowledge note from a single extracted article. Use when the user wants to deeply summarize, distill, or create a knowledge note from an article's full plain_text content stored in an *.extracted.json file.
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---
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# Article Deep Summary
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Use this skill when the user has an extracted article file and wants to generate a deep knowledge note — not a brief summary card, but a structured distillation with core conclusion, arguments, methods, details, and reusable insights.
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This skill is **not** for validating or repairing existing summaries (use `llm-summary-review` for that), nor for keyword index cleanup (use `keyword-cleanup-review` for that).
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## What This Skill Does
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- Reads an extracted article JSON containing `plain_text`
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- Calls the LLM with the dedicated article-summary prompt
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- Validates the LLM output against the `ArticleSummaryResult` schema
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- Renders a structured Markdown knowledge note in Chinese
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## Inputs
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Typical files:
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- Extracted article JSON: `outputs/freshrss/rerun/<run_id>/extracted/item-XX.extracted.json` (single-item) or a batch file with a `results` array
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- Prompt template: `outputs/prompts/article-summary-prompt.txt`
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## Workflow
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1. Identify the target extracted file and the item IDs to summarize.
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2. Run the article-summary workflow via CLI:
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```bash
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python -m summary_mcp.workflows.article_summary \
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--extracted <extracted_path> \
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--selected-ids <item_id> \
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--output-dir <output_dir>
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```
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Or call the MCP tool `generate_article_summaries` with:
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- `extracted_path`: path to the extracted JSON file
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- `selected_ids`: array of item ID strings (pass empty array to summarize all)
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- `output_dir`: (optional) directory for Markdown output
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3. The workflow internally:
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- Resolves LLM settings (`ARTICLE_SUMMARY_*` env vars, falling back to `LLM_*`)
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- Calls `run_loop_payload` with the article-summary prompt and validator
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- Retries up to 2 times on validation failure
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4. Check the output Markdown files in the specified output directory.
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## Output Schema
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The LLM returns a JSON matching `ArticleSummaryResult`:
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| Field | Type | Description |
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|---|---|---|
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| `title` | string | Article title |
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| `url` | HttpUrl | Article URL |
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| `core_conclusion` | string | Author's core conclusion, 1-2 sentences |
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| `main_argument` | string | Main argument or thesis, can be multi-sentence |
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| `key_methods` | string[] | Key methods, mechanisms, or techniques |
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| `important_details` | string[] | Noteworthy details, data points, or cases |
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| `reusable_insights` | string[] | Reusable insights transferable to other contexts |
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| `keywords` | string[] | Specific entities — tool names, frameworks, methods |
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| `topics` | string[] | Higher-level topic labels |
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| `category` | enum | One of: `资讯` `方法论` `工具实践` `观点评论` |
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| `worth_keeping` | bool | Whether the article is worth long-term retention |
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| `reason` | string | One-sentence justification for retention |
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Constraints enforced by the validator:
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- `keywords` and `topics` must not overlap
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- `keywords` focuses on concrete entities; `topics` focuses on abstract themes
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- `category` must be one of the four allowed values
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## Markdown Output
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Each article produces one `.md` file with sections:
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- 核心结论
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- 主要论点
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- 关键方法 / 机制
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- 重要细节
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- 可复用启发
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- 关键词
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- 主题
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## LLM Configuration
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Dedicated environment variables (fallback to main `LLM_*` if unset):
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- `ARTICLE_SUMMARY_LLM_API_URL`
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- `ARTICLE_SUMMARY_LLM_API_KEY`
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- `ARTICLE_SUMMARY_LLM_MODEL`
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## Repository Implementation
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Relevant code:
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- Workflow: `src/summary_mcp/workflows/article_summary.py`
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- Validator: `src/summary_mcp/validators/article_summary.py`
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- Data model: `src/summary_mcp/models/article_summary_result.py`
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- Prompt: `outputs/prompts/article-summary-prompt.txt`
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- MCP tool: `generate_article_summaries` in `src/summary_mcp/server.py`
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## When To Stop
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Stop when one of these is true:
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- Markdown knowledge notes have been generated for all requested items
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- The workflow reports repeated validation failures and the user should decide how to proceed
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- The user asks to inspect intermediate results manually
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display_name: Article Deep Summary
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short_description: Generate deep knowledge notes from a single extracted article.
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default_prompt: Given an extracted article JSON with plain_text content, run the article-summary workflow to produce a structured Markdown knowledge note with core conclusion, main arguments, key methods, important details, and reusable insights.
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