Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/localplugins/plugins/docs-scangit clone --depth 1 https://github.com/localplugins/pluginsWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00017 | $0.00489 |
| Opus 5 | $0.00009 | $0.00244 |
| Sonnet 5 | $0.00003 | $0.00098 |
| Haiku 4.5 | $0.00002 | $0.00049 |
Grade A, and why
docs-scan scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
/docs-scan
A coverage report over this project's dependencies — no $ARGUMENTS. This
is lighter than /docs: it uses local manifest/lockfile parsing plus
registry metadata only, and never fetches or distills a full doc page.
-
Detect ecosystems — scan the project root (and one level of obvious subdirs for monorepos) for the lockfiles/manifests listed in
skills/docpin/references/resolver.md§1 (npm, PyPI, crates, Go). -
Enumerate direct dependencies — for each detected ecosystem, list its direct (top-level) dependencies and their pinned/resolved versions, using the same lockfile-over-manifest precedence as the skill.
-
Classify resolvability — for each dependency, determine whether docpin can reach a version-pinned doc source for it by checking the doc-source precedence in the ecosystem's recipe (
references/ecosystem-npm.md,ecosystem-pypi.md,ecosystem-crates.md,ecosystem-go.md) — registry metadata lookup is enough for this; do not fetch/distill the full doc page. Classify each as:✓— resolvable: the primary version-pinned doc source (or a documented fallback source) exists for the resolved version.fallback— only reachable via a fallback inreferences/resolver.md§5 (e.g. only a closest-available version has docs, or only local bundled docs are available).✗— unresolvable: no registry entry, no doc source, and no local bundled docs found.
-
Print one line per dependency, grouped by ecosystem:
name version <✓ | fallback | ✗> -
Summarize coverage at the end: total dependencies scanned per ecosystem and overall, and counts for each of
✓/fallback/✗.
This command never writes files and never fetches full documentation — it's a fast, read-mostly report suitable for a quick health check or a demo.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 42 lines · 17 tokens per session scan A ae3391293957
docs-scan is a command published in the GitHub repository localplugins/plugins (5 stars, last pushed 1mo ago), licensed MIT. It adds 17 tokens to every session and 489 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
security-audit-static
Static security audit of AI-built code — map trust boundaries, cross-reference documented intent, self-refute every finding, and report only evidence-backed risks.
performance-audit-static
Static performance audit of AI-built code — find N+1 queries and request waterfalls, over-fetching, missing indexes, and caching opportunities, ranked by effort and impact.
sprint
Sprint lifecycle — plan a sprint, run a retrospective, or generate release notes.
analyze-test
Analyze A/B test results — statistical significance, sample size validation, and ship/extend/stop recommendations.
plan-okrs
Brainstorm team-level OKRs aligned with company objectives — qualitative objectives with measurable key results.
write-stories
Break a feature into backlog items — user stories, job stories, or WWA format with acceptance criteria.