analyze-article

name	analyze-article
version	2.0.0
description	Analyze news articles using LLM to extract insights, categorize content, identify key entities, and assess importance. Stores analysis in memory with links to source articles.
license	MIT
allowed-tools	["use_llm","memory/get","memory/add","memory/link"]
metadata	{"domain":"news","category":"analysis","requires-approval":false,"confidence":0.9,"mcp-servers":[]}

Analyze Article

Analyze news articles using LLM to extract insights and assess importance.

When to Use

Use this skill when you need to:

• Generate concise summaries of news articles
• Categorize articles by topic (research, business, product, security, policy)
• Extract key entities (companies, people, technologies, models)
• Assess article importance on a 1-10 scale
• Detect breaking news that requires immediate notification

Instructions

Step 1: Retrieve Article from Memory

Use memory/get to retrieve the article to analyze by its ID.

The article should contain:

• title: Article title
• url: Source URL
• source: Publication name
• summary or content: Article text

Step 2: Analyze with LLM

Use the use_llm tool to analyze the article with this prompt structure:

Analysis prompt:

Analyze the following news article and provide:

1. **Summary**: A concise 2-3 sentence summary highlighting the key points.
2. **Category**: One of: research, business, product, security, policy, general
3. **Entities**: List of key entities mentioned (companies, people, technologies, models)
4. **Importance Score**: 1-10 rating where:
   - 1-3: Minor news, incremental updates
   - 4-6: Notable news, meaningful developments
   - 7-8: Important news, significant impact
   - 9-10: Major news, industry-changing announcements
5. **Is Breaking**: True if this is major breaking news
6. **Breaking Reason**: If breaking, explain why

Article:
Title: {title}
Source: {source}
Content: {content}

Respond in JSON format.

Importance scoring factors:

• Source credibility and significance
• Novelty of the information
• Potential industry impact
• Whether from an official company announcement
• Security implications

Step 3: Store Analysis in Memory

Use memory/add to store the analysis results:

• type: "analysis"
• namespace: "news/analyses"
• data: {summary, category, entities, importance_score, is_breaking, breaking_reason}
• metadata: {source_article_id, analyzed_at}

Step 4: Link Analysis to Source

Use memory/link to create a relationship:

• source_id: analysis ID
• target_id: original article ID
• relation_type: "ANALYZED_FROM"

Step 5: Return Results

Return the analysis including:

• Analysis ID for reference
• AI-generated summary
• Category classification
• Extracted entities
• Importance score
• Breaking news flag

Tool Usage Guidance

use_llm tool

• Use for structured content analysis
• Request JSON output format
• Use temperature 0.0 for consistency

memory/get

• Retrieve article by ID
• Returns full article data

memory/add

• Store analysis as type "analysis"
• Include source article ID in metadata

memory/link

• Create ANALYZED_FROM relationship
• Links analysis to source article

Importance Scoring Guidelines

Score 1-3: Minor News

• Incremental product updates
• Minor bug fixes or patches
• Routine announcements

Score 4-6: Notable News

• New features or capabilities
• Meaningful partnerships
• Research paper publications

Score 7-8: Important News

• Major product launches
• Significant research breakthroughs
• Important policy changes

Score 9-10: Major News

• Industry-changing announcements
• Major security vulnerabilities
• Breakthrough research results

Breaking News Criteria

Mark as breaking if ANY of these apply:

• Major model release from leading AI companies (OpenAI, Anthropic, Google, Meta)
• Critical security vulnerability affecting widely-used AI systems
• Regulatory action with immediate industry impact
• Breakthrough research that changes fundamental understanding

Analysis Data Schema

{
  "id": "analysis-abc123",
  "summary": "OpenAI has released GPT-5 with significant improvements...",
  "category": "product",
  "entities": ["OpenAI", "GPT-5", "Sam Altman"],
  "importance_score": 9,
  "is_breaking": true,
  "breaking_reason": "Major model release from leading AI company",
  "source_article_id": "article-xyz789",
  "analyzed_at": "2026-01-31T12:00:00Z"
}

Error Handling

• If article retrieval fails, log error and skip
• If LLM returns malformed JSON, retry with clearer prompt
• If memory operations fail, log but return analysis results

Success Criteria

• All articles have valid summaries and categories
• Importance scores are calibrated and consistent
• Entities are accurately extracted
• Analysis is linked to source article