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

skills/article-deep-summary/SKILL.md

@@ -0,0 +1,113 @@
+---
+name: article-deep-summary
+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.
+---
+
+# Article Deep Summary
+
+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.
+
+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).
+
+## What This Skill Does
+
+- Reads an extracted article JSON containing `plain_text`
+- Calls the LLM with the dedicated article-summary prompt
+- Validates the LLM output against the `ArticleSummaryResult` schema
+- Renders a structured Markdown knowledge note in Chinese
+
+## Inputs
+
+Typical files:
+
+- Extracted article JSON: `outputs/freshrss/rerun/<run_id>/extracted/item-XX.extracted.json` (single-item) or a batch file with a `results` array
+- Prompt template: `outputs/prompts/article-summary-prompt.txt`
+
+## Workflow
+
+1. Identify the target extracted file and the item IDs to summarize.
+
+2. Run the article-summary workflow via CLI:
+
+```bash
+python -m summary_mcp.workflows.article_summary \
+  --extracted <extracted_path> \
+  --selected-ids <item_id> \
+  --output-dir <output_dir>
+```
+
+Or call the MCP tool `generate_article_summaries` with:
+
+- `extracted_path`: path to the extracted JSON file
+- `selected_ids`: array of item ID strings (pass empty array to summarize all)
+- `output_dir`: (optional) directory for Markdown output
+
+3. The workflow internally:
+   - Resolves LLM settings (`ARTICLE_SUMMARY_*` env vars, falling back to `LLM_*`)
+   - Calls `run_loop_payload` with the article-summary prompt and validator
+   - Retries up to 2 times on validation failure
+
+4. Check the output Markdown files in the specified output directory.
+
+## Output Schema
+
+The LLM returns a JSON matching `ArticleSummaryResult`:
+
+| Field | Type | Description |
+|---|---|---|
+| `title` | string | Article title |
+| `url` | HttpUrl | Article URL |
+| `core_conclusion` | string | Author's core conclusion, 1-2 sentences |
+| `main_argument` | string | Main argument or thesis, can be multi-sentence |
+| `key_methods` | string[] | Key methods, mechanisms, or techniques |
+| `important_details` | string[] | Noteworthy details, data points, or cases |
+| `reusable_insights` | string[] | Reusable insights transferable to other contexts |
+| `keywords` | string[] | Specific entities — tool names, frameworks, methods |
+| `topics` | string[] | Higher-level topic labels |
+| `category` | enum | One of: `资讯` `方法论` `工具实践` `观点评论` |
+| `worth_keeping` | bool | Whether the article is worth long-term retention |
+| `reason` | string | One-sentence justification for retention |
+
+Constraints enforced by the validator:
+
+- `keywords` and `topics` must not overlap
+- `keywords` focuses on concrete entities; `topics` focuses on abstract themes
+- `category` must be one of the four allowed values
+
+## Markdown Output
+
+Each article produces one `.md` file with sections:
+
+- 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
+
+## LLM Configuration
+
+Dedicated environment variables (fallback to main `LLM_*` if unset):
+
+- `ARTICLE_SUMMARY_LLM_API_URL`
+- `ARTICLE_SUMMARY_LLM_API_KEY`
+- `ARTICLE_SUMMARY_LLM_MODEL`
+
+## Repository Implementation
+
+Relevant code:
+
+- Workflow: `src/summary_mcp/workflows/article_summary.py`
+- Validator: `src/summary_mcp/validators/article_summary.py`
+- Data model: `src/summary_mcp/models/article_summary_result.py`
+- Prompt: `outputs/prompts/article-summary-prompt.txt`
+- MCP tool: `generate_article_summaries` in `src/summary_mcp/server.py`
+
+## When To Stop
+
+Stop when one of these is true:
+
+- Markdown knowledge notes have been generated for all requested items
+- The workflow reports repeated validation failures and the user should decide how to proceed
+- The user asks to inspect intermediate results manually

skills/article-deep-summary/agents/openai.yaml

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+display_name: Article Deep Summary
+short_description: Generate deep knowledge notes from a single extracted article.
+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.