A powerful analytical tool that identifies high-velocity GitHub repositories by calculating growth rates before they reach the official trending charts.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install github-rising-stars
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install github-rising-stars using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is GitHub Rising Stars?
GitHub Rising Stars is a sophisticated discovery tool designed to surface high-potential open-source projects in their infancy. By querying the GitHub Search API across multiple dimensions and calculating a specific velocity metric (stars per day), this Openclaw Skills contribution allows developers and researchers to find hidden gems and emerging technologies 1-7 days before they appear on the global Trending page.
This skill is particularly effective for tech enthusiasts and developers who want to stay ahead of the curve. It goes beyond simple star counts by incorporating spam filtering for low-quality signals—such as crypto bots and follower farms—ensuring that the results presented are genuine projects with real momentum.
GitHub Rising Stars Use Cases
- Identifying emerging libraries or frameworks for early adoption.
- Spotting viral open-source projects before they become mainstream.
- Monitoring specific programming language ecosystems for high-growth repos.
- Performing competitive intelligence by tracking fast-rising projects in specific niches.
- Discovering early-stage repositories that have not yet hit the official GitHub Trending radar.
How GitHub Rising Stars Works
- Multi-query search: The skill executes three distinct GitHub API queries with overlapping star and time ranges to ensure maximum coverage.
- Deduplication: Results from various queries are merged using unique repository IDs to ensure a clean dataset.
- Spam filtering: Sophisticated logic removes known low-quality signals, including game hacks and follower farms.
- Rate calculation: The system computes the velocity metric by dividing total stars by the repository age in days.
- Ranked output: Results are sorted in descending order by star rate, highlighting the projects with the highest momentum.
GitHub Rising Stars Setup
To start using this feature within your environment, ensure you have Python 3 installed. Run the bundled script with the following command:
# Basic execution with default settings
python3 scripts/find_rising.py
# Advanced usage with custom filters
python3 scripts/find_rising.py --days 5 --lang Python --min-stars 50
For higher rate limits, consider adding a GitHub Personal Access Token (PAT) to the script headers to avoid the 10 requests per minute unauthenticated limit.
GitHub Rising Stars Data Schema & Taxonomy
The skill processes and returns data structured to highlight project velocity. When the --json flag is used, the machine-readable output follows this organizational logic:
| Attribute | Description |
|---|---|
| Repo Name | The full name of the repository (user/repo). |
| Rate | Stars gained per day (The primary ranking metric). |
| Stars | Total current star count. |
| Language | The primary programming language detected. |
| Description | The project summary (absence of this may trigger a spam warning). |
| Age | Number of days since the repository was created. |
GitHub Rising Stars Advanced Features
- Custom Look-back Windows: Filter results based on a 1-7 day creation window to find ultra-fresh or slightly established repos.
- Language-Specific Filtering: Narrow your search to specific ecosystems like TypeScript, Go, or Python.
- Machine-Readable JSON Output: Export data for integration into other automated dashboards or multi-agent workflows.
- Velocity Threshold Interpretation: Categorizes projects as Viral (>200 stars/day), Fast-rising (50-200/day), or Early Signal (<50/day).
SKILL.md
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