radar-repo-watch — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited radar-repo-watch (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
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.
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:
https://github.com/<o>/<r>/releases.atomhttps://github.com/<o>/<r>/tags.atomhttps://github.com/<o>/<r>/commits.atomhttps://github.com/<u>.atomtvly (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").
For each repo in SOURCES.md → "Watched repositories":
artifact (evidence-eligible against a trend).
/<o>/<r>/pulls?q=is:pr+is:merged);a notable merge (feature, format, kernel, model support) is a citable artifact.
/<o>/<r>/issues?q=is:open+sort:comments;a suddenly high-activity thread is a QUEUE signal until it lands.
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").
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 upstreamSurface a fork under the project's trend when FNS ≥ 50 AND commits_ahead ≥ 20.
under that project's trend (cite the fork repo/release).
observation_queue signal ("fork <o>/<r> diverging, N commits ahead").
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.
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.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.