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
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zhuyongxin
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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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