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🔄 git-trend-sync

Sync AI trends to your project

GitHub Stars License Python Claude


Detects what's actually trending — not just popular — in the AI agent ecosystem

Activity-based scoring + Project-specific recommendations + Claude Code slash commands

📊 Latest Report · 🔧 Setup Guide

Why This Exists

The AI ecosystem moves fast. New frameworks, tools, and patterns emerge weekly. Yesterday's best practice is today's legacy code.

git-trend-sync keeps your project in sync with what matters. It scans 12 categories of AI repositories, ranks them by real development activity, and tells you — specifically for YOUR codebase — what's worth adopting and what's noise.

Think of it as a daily briefing: "Here's what's actively being built in the AI world, and here's what actually applies to your project."

  • 12 categories tracked — from AI agents to MCP servers, browser automation to voice AI
  • Activity-ranked — sorted by 30-day commits, not just stars
  • Project-aware — scans your code and only recommends what fits your stack
  • Design docs on demand — /trend-apply generates integration plans, not just links

Stop manually browsing GitHub. Let the trends sync to you.

Today's Top Trending (2026-03-29)

# Repository Category Score Signal Detail
1 ghost-os Computer Use Agent 10.0 🔥 surge x1.0 this week
2 Understand-Anything Knowledge Management 9.4 🆕 newcomer 14d, 494.3/day
3 cli AI Agent Framework 9.2 🆕 newcomer 26d, 883.7/day
4 opencli AI Agent Framework 9.2 🆕 newcomer 14d, 610.3/day
5 claude-skills AI Observability 9.2 📈 momentum 79 commits/7d
6 antigravity-awesome-skills Coding Assistant 8.9 🆕 newcomer 73d, 386.9/day
7 voicebox Voice Agent 8.8 🔥 surge x1.0 this week
8 awesome-openclaw-usecases-zh AI Agent Framework 8.6 🔥 surge x1.0 this week
9 claude-code-ultimate-guide Coding Assistant 8.6 🆕 newcomer 78d, 31.5/day
10 goclaw Multi-Agent 8.5 📈 momentum 196 commits/7d

Last updated: 2026-03-29 — Full Report

📋 What is this?

An automated system that tracks development activity across 12 AI agent categories and recommends trending tools that are relevant to your project.

Why activity, not just stars?

Metric What it finds Problem
⭐ Total stars Established projects Misses actively developed new tools
🔥 30-day commits Tools being actively built right now Catches real momentum

Three Pipelines

Pipeline When What
🔍 Trend Collection Daily (automated) Collects repos, computes activity scores, generates report
🎯 Recommendations After collection Scans your project, matches with trends, suggests what's relevant
🔬 Deep Analysis On-demand Detailed integration design doc for a specific trending repo

Categories

Category Description
🧠 AI Agent Framework General-purpose agent frameworks
🔍 RAG Framework Retrieval-augmented generation
🤝 Multi-Agent Multi-agent orchestration
💻 Coding Assistant AI coding tools
⚙️ AI Infrastructure LLM serving, gateways, inference
🌐 Browser Agent Web browser automation agents
🔌 MCP Model Context Protocol ecosystem
🔄 AI Workflow Visual AI workflow builders
🎙️ Voice Agent Voice/realtime AI agents
🧩 Knowledge Management Knowledge graphs, vector DBs, memory
📊 AI Observability LLM monitoring, evaluation, prompts
🖥️ Computer Use Agent Desktop/OS automation agents

🚀 Quick Start

Prerequisites

  • Python 3.10+
  • GitHub Personal Access Token

Setup

git clone https://github.com/JSLEEKR/git-trend-sync.git
cd git-trend-sync
pip install -r requirements.txt
echo "GITHUB_TOKEN=ghp_your_token_here" > .env

Run

# Full pipeline (collect → trending → analysis → report → recommendations)
python run.py

# Skip analysis
python run.py --skip-analysis

# Recommendations for a specific project
python run.py --project /path/to/my/project

# Skip recommendations
python run.py --no-recommend

# Regenerate report from existing data
python run.py --report-only

# Deep analysis for a specific repo
python src/apply.py --repo ragflow --project /path/to/my/project

⚡ Claude Code Slash Commands

Install slash commands by placing this repo's .claude/commands/ in your project:

/trend — View today's trends

Shows a summary of trending repos: new entries, rising repos, top performers.

/trend-apply — Smart project recommendations

Scans your current project's code, matches against today's trending data, and:

  • If relevant trends found → generates a design document with integration plan
  • If nothing relevant → tells you "nothing today" with explanation

Design docs are saved to docs/trend-apply/YYYY-MM-DD-<repo>.md with:

  • Why the trending repo matters to your project
  • Which files would be affected
  • Migration path with code examples
  • Risks, effort estimate, and verdict (adopt/wait/skip)

📊 How Trending Works

Repos are ranked by development activity — the number of commits in the last 30 days. This surfaces tools that are actively being built and improved, not just popular repos that stopped evolving.

Filters

  • Stars > 1,000 — eliminates noise from toy projects
  • Pushed within 7 days — must be recently active
  • 30-day commit count — primary ranking signal

Report Format

# Repository Activity Stars Commits (30d) Last Push Age Status
1 some-repo 🔥 9.2 5,230 347 1d ago 85d NEW ENTRY
2 other-repo 📈 6.8 45,000 189 3d ago 2y ACTIVE

🎯 Project Recommendations

Add git-trend-sync.yaml to your project root for targeted recommendations:

project:
  name: "My AI App"
  description: "A conversational AI assistant with RAG pipeline"
  tech_stack: ["python", "fastapi", "langchain"]
  interests: ["better RAG", "agent orchestration", "code generation"]
  exclude: ["java", "go"]

Without config, the scanner auto-detects your stack from requirements.txt, package.json, etc.

📁 Project Structure

git-trend-sync/
├── src/
│   ├── collect.py        # GitHub data collection (stars>1000, 7d active)
│   ├── trending.py       # Activity-based trend scoring
│   ├── metrics.py        # Legacy quantitative metrics
│   ├── analyze.py        # Qualitative analysis engine
│   ├── analyze.sh        # Shell-based analysis runner
│   ├── report.py         # Trend report generation
│   ├── scan_project.py   # Project context scanner
│   ├── recommend.py      # Project-trend matcher
│   ├── apply.py          # Deep integration analysis
│   ├── history.py        # Trend history tracking
│   ├── badge.py          # Shields.io badge generation
│   ├── readme_update.py  # README trend table updater
│   ├── star_history.py   # Star growth visualization
│   └── publish.sh        # Git commit & push
├── config/
│   ├── categories.yaml   # Category & topic config
│   └── prompts/          # Analysis prompt templates
├── data/
│   └── YYYY-MM-DD/       # Daily analysis data
├── reports/              # Generated reports
├── .claude/commands/     # /trend and /trend-apply
├── run.py                # Main orchestrator
└── git-trend-sync.yaml.example

Architecture

Data Flow

GitHub Topics API
      |
      v
  collect.py ──────> data/{date}/raw.json
      |
      v
  trending.py ─────> data/{date}/trending.json
      |
      v
  metrics.py ──────> data/{date}/metrics.json
      |
      v
  analyze.py ──────> data/{date}/analysis/{category}.json
      |
      v
  report.py ───────> reports/{date}.md
      |
      v
  recommend.py ────> reports/{date}-recommendations.md
      |
      v
  apply.py ────────> reports/apply-{repo}-{date}.md

Scoring Algorithm

Each repository receives an activity score (0-10) based on 30-day commit count, normalized within its category:

score = (repo_commits - category_min) / (category_max - category_min) * 10

Repos with identical commit counts are ranked by star count as tiebreaker.

Recommendation Matching

When scanning your project, git-trend-sync builds a compatibility profile:

Signal Points How Detected
Stack match +2 Your language/ecosystem matches repo's primary language
Interest match +2 Keywords from your config or dependencies overlap with repo topics
Dependency overlap +1 Repo name appears in your current dependencies
New entry +1 Repository is less than 6 months old

Repos scoring 4+ are High Relevance, 2+ are Worth Watching.

Supported Stack Detection

git-trend-sync auto-detects your project's tech stack by scanning dependency manifests:

Language Manifest Files
Python requirements.txt, pyproject.toml, setup.py
JavaScript/TypeScript package.json
Go go.mod
Rust Cargo.toml
Java pom.xml, build.gradle
Ruby Gemfile
PHP composer.json
Elixir mix.exs

Framework detection maps 50+ known packages to labels (e.g., langchain -> LLM Framework, fastapi -> Web Framework).

Reports Generated

Each daily run produces multiple reports:

Report File Content
Main trend report reports/{date}.md Per-category trending tables with analysis
Activity history reports/{date}-history.md Sparkline charts showing repo momentum
Star growth reports/{date}-star-history.md 30-day star growth visualization
Badges reports/{date}-badges.md Copy-paste shields.io badges for trending repos
Recommendations reports/{date}-recommendations.md Project-specific tool suggestions
Deep analysis reports/apply-{repo}-{date}.md Integration design doc for a specific repo

Example Activity Sparkline

langchain  ▅▆▇▇█▇▆▅▅▆▆▇▇█████▇▆▆▇▇█▇▆▅▅▆▇█  9.2
browser-use ▁▂▃▃▅▇▇██████▇▇▆▅▅▆▇▇████████▇▇█  8.7
vllm        ▃▃▅▆▇▇█▇▆▅▄▃▃▅▆▇▇█▇▆▅▅▆▇▇█▇▆▅▅▆  7.4

⚙️ Automation

GitHub Actions (Recommended)

The included workflow (.github/workflows/daily-trend.yml) runs daily at 09:00 UTC:

  1. Collects trending data from GitHub API
  2. Generates reports and updates README
  3. Commits results and pushes automatically
  4. Posts a notification comment on a pinned GitHub issue

To enable:

  1. Go to Settings > Secrets and variables > Actions
  2. Add GH_PAT with a GitHub Personal Access Token (read:repo scope)
  3. The workflow triggers daily or via Actions > Daily Trend Sync > Run workflow

Windows Task Scheduler

  1. Open Task Scheduler
  2. Create Basic Task -> Name: git-trend-sync
  3. Trigger: Daily at your preferred time
  4. Action: Start a program
    • Program: python
    • Arguments: C:\path\to\git-trend-sync\run.py
  5. Done

cron (Linux/macOS)

# Run daily at 6 PM
0 18 * * * cd /path/to/git-trend-sync && python run.py --skip-analysis >> /var/log/git-trend-sync.log 2>&1

API Reference

Core Functions

from src.collect import collect_all
from src.trending import run_trending
from src.metrics import run_metrics
from src.report import generate_reports
from src.recommend import run_recommendations
from src.scan_project import scan_project, recommend_categories

# Collect trending data
data = collect_all()                              # -> data/{date}/raw.json

# Compute activity scores
trending = run_trending("2026-03-28")             # -> data/{date}/trending.json

# Compute quantitative metrics
metrics = run_metrics("2026-03-28")               # -> data/{date}/metrics.json

# Generate markdown report
report_path = generate_reports("2026-03-28")      # -> reports/{date}.md

# Scan a project's tech stack
profile = scan_project("/path/to/project")        # -> dict with stack, deps, frameworks

# Get recommendations
rec_path = run_recommendations("2026-03-28", "/path/to/project")

Configuration

Create git-trend-sync.yaml in your project root:

project:
  name: "My AI App"
  description: "A conversational AI assistant with RAG pipeline"
  tech_stack: ["python", "fastapi", "langchain"]
  interests:
    - "better RAG performance"
    - "agent orchestration"
    - "code generation"
    - "MCP integration"
  exclude: ["java", "go"]  # Exclude entire ecosystems

Environment Variables

Variable Required Description
GITHUB_TOKEN Yes GitHub Personal Access Token for API access
CLAUDE_API_KEY No Required only for analysis mode (enabled by default, skip with --skip-analysis)

Testing

# Install test dependencies
pip install -r requirements.txt pytest

# Run all tests
python -m pytest tests/ -v

# Run specific module tests
python -m pytest tests/test_trending.py -v

# Run with coverage
python -m pytest tests/ --cov=src --cov-report=term-missing

Contributing

  1. Fork the repository
  2. Create your feature branch (git checkout -b feat/amazing-feature)
  3. Run tests (python -m pytest tests/ -v)
  4. Commit your changes (git commit -m "feat: add amazing feature")
  5. Push to the branch (git push origin feat/amazing-feature)
  6. Open a Pull Request

License

MIT -- see LICENSE for details.

Built with Claude Code

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About

Sync AI trends to your project — auto-discovers trending GitHub repos and matches them to your codebase

Topics

open-source automation ai mcp multi-agent trending developer-tools rag active-project ai-tools ai-agent github-trends claude-code

Resources

Readme

License

MIT license
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Releases 1

v1.0.0 — Multi-Signal Trending Latest
Mar 28, 2026

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