fetch-github-trending

name	fetch-github-trending
version	2.0.0
description	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.
license	MIT
allowed-tools	["http_request","memory/add","memory/check_seen","memory/mark_seen"]
metadata	{"domain":"news","category":"collection","requires-approval":false,"confidence":0.75,"mcp-servers":[]}

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