agentsclimarketplace

Radar repo watch

Skill Neetx/ai-security-research-radar/.claude/skills/radar-repo-watch

AI Security Research Radar — AI for security and security for AI

Install
npx -y skills add Neetx/ai-security-research-radar --skill radar-repo-watch

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Watch GitHub every daily run for behind-the-scenes movement — across watched REPOS (releases, merged PRs, hot issues), watched PROFILES/USERS (what a key author ships next), and FORK TREES (notable forks up to depth 3, scored by a notability metric). Releases and merges are citable artifacts; profile/issue/fork movement is a queue signal until it lands. Default to GitHub's public Atom feeds + Tavily; the REST API is optional. Use during the daily scan, after the lab sweep. Source lists live in SOURCES.md → "GitHub watch".

SKILL.md

4.6 KB, as published. Nobody here has run it

GitHub watch — repos, profiles, and fork trees

In big projects, important changes show up as PR/issue/fork activity well before the headline release. Watching repos, the people behind them, and their fork trees surfaces that build-up early.

How to fetch (feeds first, no auth, no rate-limit worries)

Prefer GitHub's public Atom feeds and plain pages — the same RSS pattern as the lab sweep, readable via tvly with no API key and no rate limit:

  • Releases: https://github.com/<o>/<r>/releases.atom
  • Tags: https://github.com/<o>/<r>/tags.atom
  • Commits on default branch: https://github.com/<o>/<r>/commits.atom
  • A user's public activity: https://github.com/<u>.atom
  • PR list / issues / a fork's page: extract the HTML via tvly (no feed exists).

The REST API (api.github.com) is OPTIONAL — use it only if it is reachable and authenticated in this environment (it may not be: the network often routes through Tavily only, and unauthenticated API is 60 req/hr). When available it gives cleaner structured data (exact ahead_by, merged_at); when not, the feeds and page extraction above are sufficient. Never let the watch block on the API.

Check activity since the last scan (previous date in logs/source_rotation.md). Log the watch every run, even when quiet ("github-watch: no movement").

1. Watched repositories

For each repo in SOURCES.md → "Watched repositories":

  • Releases / tags (releases.atom / tags.atom) — a new release is a citable artifact (evidence-eligible against a trend).
  • Merged PRs — extract the PRs page (/<o>/<r>/pulls?q=is:pr+is:merged); a notable merge (feature, format, kernel, model support) is a citable artifact.
  • Hot issues / discussions — extract /<o>/<r>/issues?q=is:open+sort:comments; a suddenly high-activity thread is a QUEUE signal until it lands.

2. Watched profiles / users

For each user in SOURCES.md → "Watched profiles/users", read their activity feed (https://github.com/<u>.atom) to see where they're heading: brand-new repos, a repo getting heavy pushes, a new release on any of their repos. New repos/releases are citable; early push activity on an unreleased repo is a queue signal ("watch <user>/<repo>, pre-release").

3. Fork-tree analysis (depth 3) + Fork Notability Score

For each project in SOURCES.md → "Fork-tree analysis", walk the fork tree up to depth 3 (the repo's forks page / /network/members, then forks of the active ones, etc.). Prune aggressively: only descend into a fork that is itself active (updated in the last ~30 days) — dead forks have no live children worth chasing.

Compute the Fork Notability Score (FNS) per fork. Read the values from the fork's page ("This branch is N commits ahead", star count, Releases tab) — or from the REST API if it's available:

gate:   updated within last 30 days          (else skip — stale)
FNS  =  commits_ahead_of_upstream            (the "N commits ahead" figure)
      + 2 × (fork's own stars)
      + 20 if the fork has its own releases/tags
      + 15 if the fork has an open PR back to upstream

Surface a fork under the project's trend when FNS ≥ 50 AND commits_ahead ≥ 20.

  • A fork that clears the bar AND ships a release/artifact → citable evidence under that project's trend (cite the fork repo/release).
  • A fork that clears the bar but is still pre-release (just diverging fast) → observation_queue signal ("fork <o>/<r> diverging, N commits ahead").
  • Name the fork in the trend's notes/evidence ("fork X carries feature Y not yet upstream"). Tune the threshold over time and log changes in the report.

Rationale: in this ecosystem the real innovation often lives in an active, far-ahead fork (performance/quant variants) months before it merges upstream — FNS is meant to catch exactly those, not the thousands of bookmark forks.

Self-maintenance (agent owns this)

Add repos/users/projects that become central to a tracked trend; drop ones that go silent or off-scope (one-line reason in the report). Adjust the FNS weights and threshold as you learn what they surface — record any change. Keep all lists in SOURCES.md.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.