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BytesAgain › Skills › Github Rising Stars
🦀 ClawHub

Github Rising Stars

by @humor200

Discover fast-growing GitHub repositories before they appear on the official Trending page. Use when users ask to find emerging/rising GitHub projects, spot...

Versionv1.0.0
Downloads585
#clawhub
TERMINAL
clawhub install github-rising-stars

📖 About This Skill

name: github-rising-stars description: Discover fast-growing GitHub repositories before they appear on the official Trending page. Use when users ask to find emerging/rising GitHub projects, spot early-stage repos gaining momentum, identify hidden gems not yet on Trending, or discover the hottest new repos in the last 1-7 days. Triggers on phrases like "find rising GitHub repos", "GitHub trending before trending", "fast growing repos", "GitHub黑马项目", "增速快的GitHub项目", "还没上trending的项目", "发现早期项目".

GitHub Rising Stars

Discovers fast-growing GitHub repositories that haven't made the official Trending page yet, by querying the GitHub Search API across multiple dimensions and ranking by stars per day.

Quick Start

Run the bundled script directly:

python3 scripts/find_rising.py

Default: repos created in the last 3 days, min 20 stars, top 15 results.

Script Options

python3 scripts/find_rising.py [options]
--days N        Look-back window in days (default: 3, range: 1-7)
--min-stars N   Minimum star count threshold (default: 20)
--top N         Number of results to return (default: 15)
--lang LANG     Filter by programming language (e.g. Python, TypeScript, Go)
--json          Output machine-readable JSON

Examples

# Find Python repos rising fast in the last 5 days
python3 scripts/find_rising.py --days 5 --lang Python
Find top 20 repos with at least 50 stars

python3 scripts/find_rising.py --min-stars 50 --top 20
JSON output for further processing

python3 scripts/find_rising.py --json

How It Works

1. Multi-query search — runs 3 GitHub API queries with overlapping time/star ranges to maximize coverage 2. Deduplication — merges results by repo ID 3. Spam filtering — removes known low-quality signals (crypto bots, game hacks, follower farms, etc.) 4. Rate calculation — computes stars / age_in_days for each repo 5. Ranked output — sorts by rate descending, shows top N

Interpreting Results

  • Rate > 200/day: Viral — likely hits Trending within 24h
  • Rate 50–200/day: Fast-rising — worth watching
  • Rate < 50/day: Early signal — may accelerate

    • Repos with no description or no language tag may be spam or private mirrors — apply extra scrutiny.

    Agent Workflow

    When a user asks to find rising repos:

    1. Run python3 scripts/find_rising.py with appropriate flags 2. Present results in a clean table, grouped by theme/category if patterns emerge 3. Highlight repos worth deeper investigation (high rate + meaningful description + known author) 4. Optionally open top repos in browser for quick review

    Limitations

  • GitHub Search API is unauthenticated by default — rate limit: 10 requests/min. Add a Authorization: token header in the script for higher limits.
  • Results reflect repos *created* recently; established repos that suddenly spike are not captured (use pushed filter variant for that).
  • Stars can be artificially inflated; cross-reference with Hacker News / Reddit for validation.

    • 💡 Examples

    # Find Python repos rising fast in the last 5 days
python3 scripts/find_rising.py --days 5 --lang Python
    Find top 20 repos with at least 50 stars
    
python3 scripts/find_rising.py --min-stars 50 --top 20
    JSON output for further processing
    
python3 scripts/find_rising.py --json