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 skills add LucasSantana-Dev/sharekit --skill audit-deepgit clone --depth 1 https://github.com/LucasSantana-Dev/sharekitWrote 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/lucassantana-dev/sharekit/audit-deep)<a href="https://agentmods.dev/skills/lucassantana-dev/sharekit/audit-deep"><img src="https://agentmods.dev/badge/skills/lucassantana-dev/sharekit/audit-deep/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/lucassantana-dev/sharekit/audit-deep"><img src="https://agentmods.dev/badge/skills/lucassantana-dev/sharekit/audit-deep.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00066 | $0.01459 |
| Opus 5.5 | $0.00026 | $0.00584 |
| Sonnet 5.5 | $0.00013 | $0.00292 |
| Haiku 4.5 | $0.00007 | $0.00146 |
Grade A, and why
audit-deep 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 1mo 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audit Deep
Runs every audit skill in parallel against one repo, reconciles findings by severity, and proposes a single prioritized remediation plan. Replaces the "run six audits manually and try to remember what each said" pattern.
Auto-invocation triggers
- User asks "is this project healthy", "audit this repo", "tech debt review"
- Weekly per-active-repo via launchd (combine with diagnostic-skills schedule)
- Pre-release (before any version bump on a production-bound repo)
- After significant architecture change or new team member onboarding
Workflow
Phase 1 — Parallel audit dispatch (always)
Invoke in parallel via Agent tool or sequential Skill calls:
test-health— suite proportionality, coverage, runtimeconfig-drift-detect— gate compatibilityhook-effectiveness— hooks fire/exit/latency statsperformance-auditorperformance-test— runtime perfsecurity-audit— secrets, deps, OWASPmcp-audit— MCP server usageplugin-audit— plugin enabled-vs-usedsocket-audit— supply chain (npm only)forge-audit— if Forge ecosystem reposcripts/check-harness-manifest.sh— verifies.harness/manifest.jsonsha256 fingerprints match tracked files and MCP policy invariants hold (defaultDeny=true, non-emptydangerousPatterns+approvedServers). Run from repo root when.harness/exists; non-zero exit is a CRITICAL finding.skills/catalog-gardener— detects dead links, stale artifacts, oversize skills (>8KB body, >150 lines), orphan skills, and frontmatter completeness issues. Outputs severity-ranked report with remediation hints. Run as part of Phase 1 parallel audit dispatch.
Each returns a structured verdict + findings.
Phase 2 — Reconcile by severity
Aggregate all findings into one ranked list:
- CRITICAL — blocks merge / release / production safety
- HIGH — degrades workflow significantly
- MEDIUM — measurable but not blocking
- INFO — track but no action
Cross-reference: a HIGH from config-drift that explains a HIGH from test-health
is reported as one root cause, not two findings.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 140 lines · 66 tokens per session scan A c0af93cedeec
audit-deep is a skill published in the GitHub repository LucasSantana-Dev/sharekit (1 stars, last pushed 3d ago), licensed MIT. It adds 66 tokens to every session and 1,459 once invoked, about $0.0003 per session on Opus 5.5. 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 skills, from other repositories
code_explorer
Explores the repository to locate primary source files, coupled UI components, and test files for bug reports or feature requests.
test-failure-explorer
Import and query an existing Vitest 3.2 JSON report to explore test failures, file-level errors and original diagnostic evidence.
feedback
Skill "feedback" from Hmbown/Codewhale, covering feedback, when to use, setup, workflow and non-goals.
smiles-validation
Strict SMILES validation, structural comparison, and modification verification. Catches invalid LLM-generated molecules.
dd-symdb
Symbol Database - search service symbols, find probe-able methods.
debugging
Runs a hypothesis-driven debugging loop across any language or binary, escalating to orthogonal oracle angles and locking the fix with a failing test. Use for crashes, silent failures, hangs, wrong responses, memory leaks, async misbehavior, or reverse engineering.