Github Rising Stars

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

Security Scan

The skill's code and instructions match its stated purpose (finding fast‑rising GitHub repos), request no unrelated credentials, and make only expected calls to the GitHub API.

Purpose & Capability

Name/description, SKILL.md and the included Python script align: the script queries the GitHub Search API, filters/deduplicates, computes stars/day, and ranks repos. No unrelated binaries, hosts, or credentials are requested.

Instruction Scope

SKILL.md instructs the agent to run the bundled script and present results. It also mentions optionally adding an Authorization header for a PAT to raise rate limits; the distributed script, however, does not read environment variables for a token (so adding a PAT would require editing the script or providing it some other way). Apart from that, instructions stay within the stated purpose and do not ask the agent to read arbitrary local files or transmit unrelated data.

Install Mechanism

No install spec and only a small Python script are provided. Nothing is downloaded from external URLs or written to disk by an installer — lowest-risk install model for a skill with a local script.

Credentials

The skill declares no required env vars or credentials and the script runs unauthenticated against api.github.com (as documented). SKILL.md mentions that adding a PAT would increase rate limits, but the script doesn't automatically consume any token from the environment — so no secret access is requested by default.

Persistence & Privilege

always is false and the skill doesn't request persistent system-wide configuration or modify other skills. Autonomous invocation is allowed (platform default) but not combined with elevated privileges or secret access.

Assessment

This skill appears coherent and limited to its stated task. Before installing/running: (1) review the included script (already small and readable) and run it in a safe environment; (2) be aware it performs network calls to api.github.com (expected) and is subject to GitHub API rate limits — SKILL.md suggests using a Personal Access Token for higher limits but the script does not read a token from the environment, so supplying a PAT would require modifying the script or your runtime headers; if you do add a PAT, create a token with minimal scopes and store it securely; (3) results may include spammy/automated repos despite filters — manually vet high‑rate entries before acting; (4) if you plan to let an autonomous agent run this skill, note it will make outbound requests to GitHub on invocation (normal for this purpose). Overall, nothing in the package requests unrelated credentials or hidden network endpoints.

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 <PAT> 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.