Borrowing it
Nothing to install: this file belongs to garfiec/Librechat-Mobile. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/garfiec/Librechat-Mobile/develop/.claude/skills/audit-deps/SKILL.mdgit clone --depth 1 https://github.com/garfiec/Librechat-MobileWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/garfiec/librechat-mobile/audit-deps)<a href="https://agentmods.dev/skills/garfiec/librechat-mobile/audit-deps"><img src="https://agentmods.dev/badge/skills/garfiec/librechat-mobile/audit-deps/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/garfiec/librechat-mobile/audit-deps"><img src="https://agentmods.dev/badge/skills/garfiec/librechat-mobile/audit-deps.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What 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 5.1 | $0.00086 | $0.02418 |
| Opus 5 | $0.00043 | $0.01209 |
| Sonnet 5 | $0.00017 | $0.00484 |
| Haiku 4.5 | $0.00009 | $0.00242 |
Grade A, and why
audit-deps 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 10d ago.
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.
How it starts
The opening of the file, as written. The whole thing — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audit Dependabot PRs
Audit open dependabot PRs and propose a safe merge order.
You are the team lead. You orchestrate. You do NOT read source code or run grep yourself during investigation — every codebase walk goes through a sub-agent inside a per-PR investigator. You handle GitHub queries, worktree setup, synthesis, and the merge-order proposal.
The skill is audit-only. It never merges, pushes, comments on PRs, or removes worktrees. It ends by presenting findings + a proposed sequence and asking the user what to do next.
Phase 0 — Discovery
Args: an optional list of PR numbers (/audit-deps 77 78 79). If omitted, audit every open
dependabot PR.
Run once:
gh pr list --state open --json number,title,headRefName,author,createdAt,mergeable,statusCheckRollup \
--jq '.[] | select(.author.login=="app/dependabot" or (.headRefName | startswith("dependabot/")))'
If args were passed, narrow to those PR numbers. From each PR object extract:
- PR number, title, head ref
mergeablestate (MERGEABLE/CONFLICTING/UNKNOWN)statusCheckRollup— count and roll up to "all green" / "N failing"- Age in days (today −
createdAt)
If the result is empty, exit early with a one-line message — no work to do.
If any PR has CONFLICTING AND age > 7 days, note it as "stale-conflict — rebase candidate" but do
NOT trigger a rebase. Rebase decisions belong to Phase 4 (user authorization).
State briefly to the user what you found before moving on:
Found N open dependabot PRs: #77 paging, #78 AGP 9.2.1, #79 kotlinx-datetime. Setting up worktrees.
Phase 1 — Worktree setup
Project worktree convention: .claude/worktrees/deps/pr-<N> on a local branch named pr-<N>.
For each PR:
# fetch dependabot branch as local pr-<N>
git fetch origin <head-ref>:pr-<N>
# create worktree (or refresh if it exists from a prior audit run)
if [ -d ".claude/worktrees/deps/pr-<N>" ]; then
git -C .claude/worktrees/deps/pr-<N> fetch origin
git -C .claude/worktrees/deps/pr-<N> reset --hard pr-<N>
else
git worktree add .claude/worktrees/deps/pr-<N> pr-<N>
fi
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.
- 10d ago First seen · 201 lines · 86 tokens per session scan A a7790a0d7274
audit-deps is a skill published in the GitHub repository garfiec/Librechat-Mobile (89 stars, last pushed today), licensed MIT. It adds 86 tokens to every session and 2,418 once invoked, about $0.0004 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 2026-08-30.
Other skills, from other repositories
human-review
Produce a plain-English review of the files you just edited and gate the changes behind an explicit Approve/Undo choice. Use when the Stop hook from the human-review-skill plugin asks for a review (you'll see a prompt referencing an edit log), OR when the developer's most recent message is "1", "2", "approve", or…
simplify-code
Parallel 4-agent cleanup of recent code changes.
requesting-code-review
Pre-commit review: security scan, quality gates, auto-fix.
opencode
Delegate coding to OpenCode CLI (features, PR review).
security-review
Complete a security review of the pending changes on the current branch.
code-review
Review the current diff, or a PR number/branch/path target, for correctness bugs and reuse/simplification/efficiency cleanups at the given effort level (low/medium: fewer, high-confidence findings; high→max: broader coverage, may include uncertain findings; ultra: deep multi-agent review in the cloud); with no level…