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lopushok9

ai-github-radar

by Yuri · GitHub ↗ · v1.0.0
cross-platform ✓ Security Clean
1259
Downloads
2
Stars
7
Active Installs
1
Versions
Install in OpenClaw
/install airadar
Description
Tracks and analyzes AI-native tools and GitHub repos with fast growth or major updates to reveal emerging trends in AI workflows and ecosystems.
README (SKILL.md)

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

  1. 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.
  2. 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.
  3. 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 X receives €XXM, its GitHub repo moved into the top trending slot, suggesting the community is rallying behind that capability.”
  4. 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.
  5. 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.

Usage Guidance
This skill appears coherent and low-risk because it only uses public web/GitHub signals and asks for no secrets. Before installing, consider: 1) the agent will need network access to fetch news and GitHub pages — expect rate limits and possible gaps without a GitHub token; 2) LLMs can fabricate or misquote URLs, star counts, or funding amounts — always verify cited links and metrics manually; 3) if you do not want background web crawling, restrict autonomous invocation or audit runs interactively; and 4) if you need high-confidence historical star graphs or API-level data, provide a scoped GitHub token and document why that credential is needed.
Capability Analysis
Type: OpenClaw Skill Name: airadar Version: 1.0.0 The skill's `SKILL.md` provides clear instructions for an AI agent to gather and synthesize information about AI tools and GitHub trends from public sources. It instructs the agent to collect data from 'GitHub Explore' and 'octoverse' and to cite URLs in its output, which is consistent with its stated purpose. There are no instructions for accessing sensitive data, executing arbitrary commands, exfiltrating information, or engaging in prompt injection against the agent itself. The `_meta.json` file contains standard metadata without any suspicious elements.
Capability Assessment
Purpose & Capability
Name and description match the instructions: the skill focuses on AI tools and GitHub repo momentum and requires only public data (trending lists, star history, news links). No credentials, binaries, or unusual filesystem access are requested, which is proportionate for the stated purpose.
Instruction Scope
SKILL.md instructs the agent to fetch trending GitHub lists and news links and to cite URLs and metrics. This stays within scope, but the instructions are somewhat open-ended about selection/prioritization and rely on external web queries — so the agent could produce incorrect or hallucinated URLs/metrics if not actually verifying sources.
Install Mechanism
Instruction-only skill with no install spec and no code files, which is the lowest-risk model: nothing is written to disk by an installer.
Credentials
No environment variables, credentials, or config paths are requested. For public GitHub and news data this is appropriate; note that lack of a GitHub token limits access to authenticated rate-limited APIs but is not a security concern.
Persistence & Privilege
always:false and default autonomous invocation are set; the skill does not request elevated or cross-skill privileges and does not modify other skills or system configs.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install airadar
  3. After installation, invoke the skill by name or use /airadar
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
ai-tools-github-radar v1.0.0 – Initial Release - Introduces a structured approach for tracking fast-growing or emerging AI tools and their GitHub repositories. - Summarizes major signals such as funding rounds, product launches, and rapid star growth, distinguishing true momentum from hype. - Separates reporting into “Tools & Apps” and “GitHub Radar,” each with concise, analytical entries. - Connects product news with open source community traction to highlight ecosystem impact. - Ends each summary with a “What to Watch” section to flag notable trends or projects. - Prioritizes transparent sourcing and actionable insights for users seeking focused AI tooling updates.
Metadata
Slug airadar
Version 1.0.0
License
All-time Installs 8
Active Installs 7
Total Versions 1
Frequently Asked Questions

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.

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