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.
git clone --depth 1 https://github.com/MarioEpkOne/PipelineIQWrote 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/commands/marioepkone/pipelineiq/audit-implementation)<a href="https://agentmods.dev/commands/marioepkone/pipelineiq/audit-implementation"><img src="https://agentmods.dev/badge/commands/marioepkone/pipelineiq/audit-implementation/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/commands/marioepkone/pipelineiq/audit-implementation"><img src="https://agentmods.dev/badge/commands/marioepkone/pipelineiq/audit-implementation.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 | $0.00000 | $0.02310 |
| Opus 5.5 | $0.00000 | $0.00924 |
| Sonnet 5.5 | $0.00000 | $0.00462 |
| Haiku 4.5 | $0.00000 | $0.00231 |
Grade A, and why
audit-implementation 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 2mo 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 — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
POST-IMPLEMENTATION AUDIT — analyzes what the implementing agent did wrong and why, proposes specific changes to CLAUDE.md / impl.md / impl-plan.md for approval, and appends new patterns to learnings.md.
The user's request is: $ARGUMENTS
Purpose
This skill is a process autopsy, not a QA check. The goal is to understand why failures happened so the skills and rules can be improved for future implementations. Run this after every /impl. For auditing whether an implementation plan conforms to the impl-plan skill spec, use /audit-plan instead.
Re-audit mode (after /fix): If this audit is being run after a /fix session, limit scope to: (1) verify each change the fixer stated it made in its working log, and (2) confirm no regressions were introduced. A full independent evaluation and complete failure taxonomy are not needed — skip Phase 2 (the blind pass) entirely and read all inputs normally, then condense Phase 3 to only the fixer's stated changes. Note "Re-audit — scoped to fixer's stated changes" at the top of the audit document.
Phase 1 — Find Inputs
Find the working log:
- If an argument was given: search
Working Logs/for a file whose name contains the argument (case-insensitive). If multiple match, list them and ask. - If no argument: use the most recently modified
.mdfile inWorking Logs/. - If none found: tell the user and exit.
Read only the working log's header fields (e.g. **Impl plan:**) — enough to follow the chain to the spec. Do not read the working log body ("Changes Made", "Deviations", "Errors", verification narrative) yet; that is deferred to Phase 3 so it cannot anchor your independent verdict.
Re-audit exception: In Re-audit mode (after
/fix), there is no blind pass — read the working log in full now, normally.
Follow the chain:
- From the working log's
**Impl plan:**header field: find the impl plan inImplementation Plans/and read only its**Spec:**header field (not the full plan — the plan body is deferred to Phase 3 along with the working-log body). - From the impl plan's
**Spec:**header field: find and read the spec in full fromspecs/.
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.
- 2mo ago First seen · 198 lines · 0 tokens per session scan A fd1fbb7ad713
audit-implementation is a command published in the GitHub repository MarioEpkOne/PipelineIQ (2 stars, last pushed 4mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,310 tokens. 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
skill-stats
Leaderboard of your most-used skills from the durable usage log (survives transcript cleanup).
review-plan
Critically review the current plan/approach before implementing — assumptions, gaps, blast radius, deploy safety.
explain-dev
Write a WhatsApp-ready, non-technical message explaining a development to the client.
feature
Run the full feature pipeline — explore → plan → implement → review → test — for the change described in the argument.
triage
Triage a bug report (PDF / markdown / text) — extract the issues, prioritize, then run the fix pipeline per issue.
fix
Diagnose and fix a bug — reproduce → locate → root-cause → fix → test.