GitHub Radar is an advanced open-source intelligence engine designed for AI Product Managers to extract high-level paradigm insights and ecosystem trends from GitHub data.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install github-trend-observer
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-trend-observer 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 Radar?
GitHub Radar serves as a sophisticated intelligence layer atop the GitHub CLI, specifically tailored for the needs of AI Product Managers and researchers. It moves beyond basic repository metrics to provide PM-grade paradigm insights, helping users understand not just what is trending, but why it matters within the broader technology stack.
By leveraging a unique Layer analysis framework, GitHub Radar categorizes projects from low-level models (L1) to high-level wrappers (L5). This allows users to filter through the noise of simple demos and focus on projects with high strategic value, making it an essential addition to the Openclaw Skills library for professional developer advocates and product strategists.
GitHub Radar Use Cases
- Proactive exploration of weekly trending projects to identify shifting technical stacks.
- Conducting deep-dive ecosystem analysis around a specific repository to understand competitive landscapes.
- Monitoring anomalous growth signals to catch breakout projects in their early acceleration phase.
- Strategic keyword expansion and search to map out new technical directions or categories.
How GitHub Radar Works
- Authentication and Quota Check: The system validates GitHub CLI (gh) authentication and checks API rate limits to determine if it should run in full, degraded, or minimal mode.
- Data Acquisition: Depending on the selected mode, the tool fetches repository metadata, star history, and contributor activity via Python scripts.
- Layer Classification: The AI agent applies a specialized Layer Model to categorize repositories based on their position in the AI stack (e.g., Infrastructure vs. Application).
- Insight Synthesis: An AI analyzer processes the raw data to generate qualitative PM insights, including paradigm shifts and innovation assessments.
- Report Generation: The system outputs comprehensive HTML or Markdown reports featuring growth curves, ASCII ecosystem maps, and strategic recommendations.
GitHub Radar Setup
Ensure you have the GitHub CLI installed and authenticated before using this skill.
# 1. Verify GitHub CLI authentication
gh auth status
# 2. Check your current GitHub API rate limit
python scripts/check_rate_limit.py
# 3. Run a proactive pulse check (Mode 1)
python scripts/radar_pulse.py --days 7
# 4. Monitor specific domains for anomalies (Mode 3)
python scripts/watch_signals.py --domain ai-agent
GitHub Radar Data Schema & Taxonomy
GitHub Radar organizes intelligence using a structured taxonomy and local configuration files:
| Component | Description |
|---|---|
| Layer Model | A classification standard (L1-L5) defining a project's depth in the AI ecosystem. |
| Seed List | config/seed_list.json contains a curated list of high-signal AI builders and organizations. |
| Growth Metrics | Tracks avg_daily_7d, acceleration, burst_ratio, and fork_ratio for anomaly detection. |
| Reports | Generates localized (EN/CN) HTML/MD files in the output/ directory with standardized CSS variables. |
GitHub Radar Advanced Features
- Multilingual Intelligence: Automatically matches user language (English/Chinese) for reports and agent prompts.
- Dynamic Search Strategy: Heuristic-based keyword expansion that adapts when search recall is low.
- Anomaly Detection Framework: Specialized growth pattern classification (Sustained, Accelerating, Spike+Decay, Step).
- Deep Link Ecosystem Mapping: Generates ASCII diagrams to visualize the relationship between a project and its competitors.
- Customizable Seed Lists: Allows power users to follow specific high-impact developers for tailored trend discovery within Openclaw Skills.
SKILL.md
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