reader/skills/article-deep-summary/SKILL.md

name, description

name	description
article-deep-summary	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:

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