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
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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_queuesignal ("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.