airadar

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name: ai-tools-github-radar
description: Distill the signal around AI-native tools/apps and their GitHub home bases: fast-growing, hyped, well-funded projects plus repos with rapidly rising stars or trending status. Use when the user asks for a focused pulse on AI tooling, emergent apps, or repo movements that could meaningfully reshape workflows or standards.
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# 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 eme