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fetch-github-trending

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Fetch trending AI/ML repositories from GitHub and store them in memory. Uses HTTP requests for GitHub API and memory MCP for storage and deduplication.

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Source SKILL.md: https://github.com/majiayu000/claude-skill-registry/blob/HEAD/skills/analysis/fetch-github-trending/SKILL.md

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Fetch GitHub Trending

Fetch and store trending AI/ML repositories from GitHub with deduplication.

When to Use

Use this skill when you need to:

  • Discover trending AI/ML tools and libraries
  • Find new repositories gaining traction
  • Collect repos for tool spotlight sections in digests

Instructions

Step 1: Define Search Topics

Target these GitHub topics for AI/ML repos:

  • machine-learning
  • deep-learning
  • llm
  • artificial-intelligence
  • nlp
  • transformers
  • computer-vision

Step 2: Build GitHub Search Query

Construct a GitHub search API query:

Query pattern:

topic:machine-learning OR topic:llm language:python stars:>100 pushed:>2026-01-25

Date calculation based on time range:

  • daily: pushed in last 1 day
  • weekly: pushed in last 7 days
  • monthly: pushed in last 30 days

Step 3: Fetch from GitHub API

Use the http_request tool to query GitHub Search API.

API endpoint:

  • URL: https://api.github.com/search/repositories
  • Method: GET
  • Parameters: q (query), sort (stars), order (desc), per_page (20)
  • Headers: Accept: application/vnd.github.v3+json

For each API response:

  1. Extract: full_name, description, html_url, stargazers_count, forks_count, language, topics
  2. Parse created_at and pushed_at timestamps

Step 4: Check for Duplicates

For each repository:

  1. Check if already seen:

    • Call memory/check_seen with key=full_name (e.g., "owner/repo"), namespace="news/repos"
    • If seen=true, skip this repo

  2. Validate AI relevance:

    • Repo must have at least one AI-related topic OR
    • Description mentions AI/ML keywords
    • Skip repos that don't appear AI-related

Step 5: Store New Repositories

For each new (unseen) repository:

  1. Store in memory:

    • Call memory/add with:
      • type: "document"
      • namespace: "news/repos"
      • data: {full_name, name, description, url, stars, forks, language, topics, created_at, pushed_at}
      • metadata: {fetched_at, search_topic}

  2. Mark as seen:

    • Call memory/mark_seen with:
      • key: full_name
      • namespace: "news/repos"
      • ttl_seconds: 604800 (7 days)

Step 6: Return Results

Return a summary including:

  • Number of repos stored
  • Number of duplicates skipped
  • Topics searched
  • Total matching repos found

Tool Usage Guidance

http_request tool

  • Use for GitHub API calls
  • Set appropriate headers for API version
  • Handle rate limiting (60/hour unauthenticated, 5000/hour authenticated)

memory/check_seen

  • Key should be the full repo name (owner/repo format)
  • Namespace: "news/repos"

memory/add

  • Store each new repo as type "document"
  • Include star count for ranking

memory/mark_seen

  • Use 7-day TTL (repos trend changes weekly)

Repository Data Schema

{
  "full_name": "owner/repo-name",
  "name": "repo-name",
  "description": "A powerful LLM inference library",
  "url": "https://github.com/owner/repo-name",
  "stars": 15234,
  "forks": 1523,
  "language": "Python",
  "topics": ["llm", "inference", "machine-learning"],
  "created_at": "2025-06-15",
  "pushed_at": "2026-01-26"
}

Error Handling

  • If GitHub API rate limits, wait and retry or return cached results
  • If API request fails, log error and continue
  • Return partial results if some queries succeed

Success Criteria

  • At least one topic query succeeds
  • Repos are sorted by star count
  • No duplicate repos in output
  • AI-relevance filter applied