rss-article-retriever

This skill retrieves articles from a local RSS service (WeWe-RSS) and outputs clean, structured content ready for downstream processing (e.g., video script generation).

Mandatory Execution Steps

Follow these steps EXACTLY when executing this skill:

Step 1: Determine Operation Mode

Analyze user input to determine mode:

• list → Return article list without full content
• search → Filter by keyword, return list
• get → Fetch full article content (must use Playwright/curl method)

Step 2: Fetch from RSS API

Step 2: Fetch from RSS API

Use the provided python script to fetch RSS articles:

python3 app/agentic/skills/rss-article-retriever/scripts/fetch_rss.py --limit {limit} --title_include "{keyword}"

Step 3: For Full Content (get mode)

Use the provided python script which uses Playwright:

python3 app/agentic/skills/rss-article-retriever/scripts/fetch_content.py "{article_url}"

If these scripts are available as tools, use the tool directly. Otherwise execute via shell.

Step 4: Clean Content

Apply cleaning rules from [CLEANINGRULES.md](references/CLEANINGRULES.md):

• Remove author profile sections
• Remove QR codes
• Remove subscription prompts
• Preserve main article text and legal sections

Step 5: Identify Sections

Look for common legal article headings:

• 案情简介
• 法院审理
• 法院判决
• 法官说法

Step 6: Output Structured Result

Wrap output in <skill-output> markers with proper JSON format.

Step 7: CRITICAL - Auto-Chain to video-script-generator

MANDATORY: After outputting the RSS article result, check the original user request:

If the user's request contains ANY of these phrases:

• "制作视频脚本"
• "生成口播脚本"
• "生成视频脚本"
• "make video script"
• "create video script"
• "generate script"
• "to make script"

You MUST IMMEDIATELY call the video-script-generator skill with the article content.

DO NOT ask the user "现在需要用这篇文章来制作视频脚本吗？" or similar questions.

DO NOT stop after outputting the article.

Execute this command:

Skill(video-script-generator)

With the article's plain_text content as input.

Example of correct behavior:

User: "请找一篇关于'民间借贷合同'的文章制作视频脚本"

Agent actions:
1. Search RSS for articles about "民间借贷合同"
2. Get full content using Python Playwright
3. Output <skill-output type="rss-article">...</skill-output>
4. IMMEDIATELY call: Skill(video-script-generator) with the article content
5. Output <skill-output type="video-script">...</skill-output>

Important: DO NOT Do This

❌ DO NOT use Python requests library - WeChat pages are dynamically rendered with JavaScript ❌ DO NOT use import requests or from bs4 import BeautifulSoup - These won't work for dynamic content ❌ DO NOT say "Playwright is unavailable" or "tool usage limit reached" - The Python Playwright method above works ❌ DO NOT give up and ask user to provide content - Always use the Python Playwright method first ❌ DO NOT use curl to parse WeChat article content - WeChat articles are JavaScript-rendered

✅ USE Python Playwright via Bash python3 -c - This is the ONLY reliable method for WeChat articles ✅ USE asyncio and playwright.asyncapi - These handle async browser operations ✅ USE page.evaluate() to extract content - This gets the rendered text content ✅ USE waituntil='networkidle' - This ensures the page is fully loaded before extraction

Workflow Overview

1. Parse User Intent - Determine operation mode (list/search/get)
2. Call RSS API - Fetch article metadata from http://localhost:4000/feeds/all.atom
3. Read Full Content - Use Playwright to access WeChat article pages
4. Clean Content - Remove ads, author info, QR codes per cleaning rules
5. Output Structured Result - Return JSON with <skill-output> boundary markers

Operation Modes

RSS API Reference

Endpoint: http://localhost:4000/feeds/all.atom

Parameters:

Article URL Pattern: https://mp.weixin.qq.com/s/{article.id}

Example Calls:

# Get latest 5 articles
curl "http://localhost:4000/feeds/all.atom?limit=5"

# Search articles containing "民间借贷"
curl "http://localhost:4000/feeds/all.atom?title_include=民间借贷"

Playwright Usage Strategy

When fetching full article content, use one of these methods:

Method 1: Python Playwright (Recommended - WORKS FOR WeChat)

python3 -c "
import asyncio
from playwright.async_api import async_playwright

async def main():
    async with async_playwright() as p:
        browser = await p.chromium.launch(headless=True)
        page = await browser.new_page()
        await page.goto('https://mp.weixin.qq.com/s/{article_id}', wait_until='networkidle', timeout=30000)
        try:
            await page.wait_for_selector('#js_content', timeout=15000)
        except:
            pass
        content = await page.evaluate('() => { const el = document.getElementById(\"js_content\"); return el ? el.innerText : \"\"; }')
        print(content)
        await browser.close()

asyncio.run(main())
" 2>&1

Method 2: Direct curl with HTML parsing (Fallback - MAY NOT WORK for WeChat)

# For WeChat articles, try direct fetch first
curl -s "https://mp.weixin.qq.com/s/{article_id}" -H "User-Agent: Mozilla/5.0" | \
  grep -A 1000 'id="js_content"' | grep -B 1000 'id="sg_' | \
  sed 's/<[^>]*>//g' | sed 's/&nbsp;/ /g' | sed 's/&lt;/</g' | sed 's/&gt;/>/g' | tr -s '\n'

Method 3: Read from cached file

If previous methods fail, check if content was saved and read from temp file.

Content Extraction Steps:

1. Navigate to article page or fetch via curl
2. Extract content from #js_content div
3. Remove HTML tags and decode entities
4. Apply cleaning rules (see [CLEANINGRULES.md](references/CLEANINGRULES.md))
5. Structure output as plain text with identified sections

Content Cleaning Rules

See [CLEANINGRULES.md](references/CLEANINGRULES.md) for detailed rules.

Quick Reference

Remove these elements:

• Author profile cards (#jsprofileqrcode, .profile_container)
• QR codes (#jspcqrcode, .qrcode_pc)
• Share buttons (.richmediatool)
• Ad areas (#jssponsorad_area)
• Subscription prompts (#jsprofilearticle)
• Image alt texts and captions that are ads

Preserve these elements:

• Main text paragraphs (p, section with text)
• Headings (strong, b, h1-h6)
• Lists (ul, ol, li)

Section Recognition (Legal Articles): Common headings to identify sections:

• 案情简介
• 法院审理
• 法院判决
• 法官说法

Output Protocol

All structured output MUST be wrapped in boundary markers:

<skill-output type="rss-article" schema-version="1.0.0">
{JSON conforming to schema/output-schema-v1.json}
</skill-output>

Marker Attributes:

Parsing Rules (For Main Agent/Applications)

┌─────────────────────────────────────────────────────┐
│  Skill Complete Output                              │
├─────────────────────────────────────────────────────┤
│  [Optional] Explanatory text (should be ignored)   │
│                                                     │
│  <skill-output type="rss-article" schema-version="1.0.0">
│  {                                                  │
│    "version": "1.0.0",                             │
│    "operation": "get",                             │
│    "result": { ... }  ← Structured result          │
│  }                                                  │
│  </skill-output>                                    │
│                                                     │
│  [Optional] Additional notes (should be ignored)   │
└─────────────────────────────────────────────────────┘

Main Agent Parsing Logic:

1. Extract content between <skill-output ...> and </skill-output>
2. Parse as JSON
3. Validate against schema (optional but recommended)
4. All content outside markers should be ignored

Output Structure

For list and search Operations

{
  "version": "1.0.0",
  "operation": "list",
  "result": {
    "type": "summary",
    "total_count": 5,
    "articles": [
      {
        "id": "article-id-123",
        "title": "Article Title",
        "author": "公众号名称",
        "published_at": "2026-01-20T10:30:00Z",
        "url": "https://mp.weixin.qq.com/s/article-id-123",
        "summary": "RSS 摘要内容..."
      }
    ],
    "query": {
      "limit": 5,
      "page": 1,
      "title_include": null,
      "title_exclude": null
    }
  }
}

For get Operation

{
  "version": "1.0.0",
  "operation": "get",
  "result": {
    "type": "full",
    "article": {
      "id": "article-id-123",
      "title": "Article Title",
      "author": "公众号名称",
      "published_at": "2026-01-20T10:30:00Z",
      "url": "https://mp.weixin.qq.com/s/article-id-123",
      "content": {
        "plain_text": "清洗后的纯文本，可直接传递给 video-script-generator",
        "sections": [
          { "heading": "案情简介", "text": "..." },
          { "heading": "法院审理", "text": "..." }
        ],
        "source": "playwright"
      },
      "word_count": 1245,
      "cleaning_notes": ["Removed author profile", "Removed QR codes"]
    }
  }
}

Output Modes

Pre-Execution Checklist

Before generating output, complete these checks:

1. Read Schema File

- Read schema/output-schema-v1.json - Confirm all required fields - Note format requirements

1. Reference Examples

- Read references/EXAMPLES.md - Match standard JSON structure

1. Format Requirements

- version must use semantic versioning: "1.0.0" - publishedat must use ISO 8601 format - wordcount must be calculated from cleaned content

Error Handling

RSS API Errors

{
  "version": "1.0.0",
  "operation": "list",
  "error": {
    "code": "RSS_CONNECTION_ERROR",
    "message": "Cannot connect to RSS service at localhost:4000",
    "suggestion": "Ensure WeWe-RSS service is running"
  }
}

Playwright Errors

{
  "version": "1.0.0",
  "operation": "get",
  "result": {
    "type": "full",
    "article": {
      "id": "...",
      "content": {
        "plain_text": "RSS 摘要作为回退内容...",
        "sections": [],
        "source": "rss_fallback"
      },
      "cleaning_notes": ["Playwright timeout - using RSS summary as fallback"]
    }
  }
}

Error Codes

Integration with video-script-generator

When user requests "用RSS文章制作视频脚本":

1. Execute rss-article-retriever with get operation
2. Extract content: result.article.content.plain_text
3. Call video-script-generator with extracted content

Example Chain:

User: "请用最新一篇RSS文章来制作视频脚本"

Step 1: rss-article-retriever (get latest)
  → Outputs: <skill-output type="rss-article">...</skill-output>

Step 2: Extract plain_text from result

Step 3: video-script-generator (with plain_text as input)
  → Outputs: <skill-output type="video-script">...</skill-output>

Usage Examples

List Recent Articles

User input: "获取最近5篇文章列表"

Output:

<skill-output type="rss-article" schema-version="1.0.0">
{
  "version": "1.0.0",
  "operation": "list",
  "result": {
    "type": "summary",
    "total_count": 5,
    "articles": [
      {
        "id": "abc123",
        "title": "民间借贷纠纷案例分析",
        "author": "法律公众号",
        "published_at": "2026-01-20T10:30:00Z",
        "url": "https://mp.weixin.qq.com/s/abc123",
        "summary": "本案涉及民间借贷..."
      },
      ...
    ],
    "query": { "limit": 5, "page": 1 }
  }
}
</skill-output>

Search by Keyword

User input: "找一篇关于'民间借贷'的文章"

Output:

<skill-output type="rss-article" schema-version="1.0.0">
{
  "version": "1.0.0",
  "operation": "search",
  "result": {
    "type": "summary",
    "total_count": 2,
    "articles": [...],
    "query": { "title_include": "民间借贷" }
  }
}
</skill-output>

Get Full Content

User input: "获取最新一篇文章的完整内容"

Output:

<skill-output type="rss-article" schema-version="1.0.0">
{
  "version": "1.0.0",
  "operation": "get",
  "result": {
    "type": "full",
    "article": {
      "id": "abc123",
      "title": "民间借贷纠纷案例分析",
      "author": "法律公众号",
      "published_at": "2026-01-20T10:30:00Z",
      "url": "https://mp.weixin.qq.com/s/abc123",
      "content": {
        "plain_text": "案情简介\n\n张某与李某系朋友关系...",
        "sections": [
          { "heading": "案情简介", "text": "张某与李某系朋友关系..." },
          { "heading": "法院审理", "text": "法院经审理认为..." },
          { "heading": "法院判决", "text": "判决被告李某..." }
        ],
        "source": "playwright"
      },
      "word_count": 1245,
      "cleaning_notes": ["Removed author profile", "Removed QR codes", "Removed subscription prompt"]
    }
  }
}
</skill-output>

Reference Materials

• [output-schema-v1.json](schema/output-schema-v1.json) - JSON Schema for output validation
• [CLEANINGRULES.md](references/CLEANINGRULES.md) - Detailed content cleaning rules
• [EXAMPLES.md](references/EXAMPLES.md) - Input/output examples