/install airadar
Thesis
Treat today’s AI tooling and GitHub traction as complementary data streams for technological momentum: the stories, raises, and features that command attention and the repos whose star graphs are climbing fastest together reveal where value, community trust, and experimentation are accelerating. The purpose of this skill is to keep that thesis front and center—every summary should answer “why does this tool/repo matter now?” and “what does its trajectory say about the broader AI ecosystem?”
Workflow
- Collect the canonical signals: prioritize AI-only tools or apps with news hooks (big raises, novel features, product launches, or widespread hype). For GitHub, retrieve trending lists or star history (
GitHub Explore,octoverse, etc.) to identify repos showing rapid-star growth or new surges in contributions. - Evaluate momentum vs. noise: for each item, note the concrete trigger (e.g., funding round, major feature, notable integration, release notes) and pair it with a metric (funding amount, feature scope, star velocity, ecosystem mentions). Highlight why the story feels like a game changer or an inflection point.
- Frame the insight: weave a short thesis paragraph (~1-2 sentences) that links the tool/app news to the repo signal—e.g., “As
project Xreceives €XXM, its GitHub repo moved into the top trending slot, suggesting the community is rallying behind that capability.” - Structure the output: separate sections for “Tools & Apps” and “GitHub Radar,” each listing 3–5 items with bullets for the what/why/metric. End with a “What to Watch” note that flags one emerging pattern or repo to revisit soon.
- Source transparently: cite URLs or data (news links, GitHub URLs, star counts) next to each bullet so follow-up research is straightforward.
Style and Tone
- Be analytical, not just descriptive. Use verbs like “signals,” “reinforces,” “propels,” and “tests” to keep the prose active.
- Keep each entry concise (2–3 sentences) but layered: mention the news, what changed, and the broader implication.
- If a tool or repo contradicts the thesis (e.g., hype without traction), note that tension rather than ignoring it.
When to Trigger
Invoke this skill whenever a user wants an update on AI tools, apps, or GitHub movements, especially if they ask for “interesting” or “fast-growing” innovations, big raises, or “game changing” features. It also applies when they request analytical summaries that connect product moves with developer momentum.
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install airadar - After installation, invoke the skill by name or use
/airadar - Provide required inputs per the skill's parameter spec and get structured output
What is ai-github-radar?
Tracks and analyzes AI-native tools and GitHub repos with fast growth or major updates to reveal emerging trends in AI workflows and ecosystems. It is an AI Agent Skill for Claude Code / OpenClaw, with 1259 downloads so far.
How do I install ai-github-radar?
Run "/install airadar" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is ai-github-radar free?
Yes, ai-github-radar is completely free (open-source). You can download, install and use it at no cost.
Which platforms does ai-github-radar support?
ai-github-radar is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created ai-github-radar?
It is built and maintained by Yuri (@lopushok9); the current version is v1.0.0.