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 alchemiststudiosDOTai/tunacode --skill audit-harnessgit clone --depth 1 https://github.com/alchemiststudiosDOTai/tunacodeWrote 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/alchemiststudiosdotai/tunacode/audit-harness)<a href="https://agentmods.dev/skills/alchemiststudiosdotai/tunacode/audit-harness"><img src="https://agentmods.dev/badge/skills/alchemiststudiosdotai/tunacode/audit-harness/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/alchemiststudiosdotai/tunacode/audit-harness"><img src="https://agentmods.dev/badge/skills/alchemiststudiosdotai/tunacode/audit-harness.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 | $0.00069 | $0.00746 |
| Opus 5.5 | $0.00028 | $0.00298 |
| Sonnet 5.5 | $0.00014 | $0.00149 |
| Haiku 4.5 | $0.00007 | $0.00075 |
Grade A, and why
audit-harness 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 — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audit Harness
HARNESS.md is mission-critical. Audit it with zero tolerance.
Trigger
Use this skill when the user asks to:
- audit
HARNESS.md - verify harness accuracy
- run pre-commit or pre-push hooks manually
- confirm architecture or CI gates
- explain what actually enforces the harness
Hard Rules
- Treat any mismatch, omission, skipped gate, auto-fix, or failing check as a critical failure.
- Never describe results as "mostly passing", "just one failure", or equivalent minimization.
- State the exact failure first.
- Explain the finding before changing code or docs.
- Do not proceed with a fix until the user tells you to proceed.
- During harness audits, never use
make check,scripts/run_gates.py, or any loop/script wrapper as the primary audit path. - Run checks manually, one by one, in the same order they appear in the source-of-truth config.
- If a hook modifies files, report the exact files immediately.
- Do not revert hook changes unless the user explicitly asks.
Source Of Truth Order
Read these first:
HARNESS.md.pre-commit-config.yamlMakefiletests/test_dependency_layers.pyscripts/grimp_layers_report.py.github/workflows/*.ymldocs/git/practices.mdAGENTS.md
Manual Audit Procedure
Pre-commit
- Enumerate the active pre-commit hooks from
.pre-commit-config.yaml. - Start at the top.
- Run each hook manually:
uv run pre-commit run <hook-id> --all-files
- After each hook:
state
Passed,Failed,Skipped, orModified files. - If a hook fails, stop and explain why before proposing a fix.
Pre-push
- Enumerate the active pre-push hooks from
.pre-commit-config.yaml. - Run each one manually, one by one:
uv run pre-commit run <hook-id> --hook-stage pre-push --all-files
- Treat any failure as critical.
Architecture
tests/test_dependency_layers.pyis the source of truth forgrimpenforcement.scripts/grimp_layers_report.pyis report generation only.scripts/run_gates.pyis supplemental only and not canonical.
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 · 102 lines · 69 tokens per session scan A be146927dfad
audit-harness is a skill published in the GitHub repository alchemiststudiosDOTai/tunacode (125 stars, last pushed 15d ago), licensed MIT. It adds 69 tokens to every session and 746 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 2026-08-30.
Other skills, from other repositories
knowledge-engineering-quality-and-delivery
A project-wide quality and delivery workflow covering tests, release checks, builds, publishing, and bilingual documentation for Blade Code.
evaluating-llms-harness
Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.
dogfood
Exploratory QA of web apps: find bugs, evidence, reports.
test-driven-development
TDD: enforce RED-GREEN-REFACTOR, tests before code.
baby-sit
Monitor a GitHub pull request until CI is green, diagnose failures, and rerun only evidence-backed flaky GitHub Actions jobs.
reproduce
Reproduces a paper's result, a claim, an artifact or a previous run with the target and success criterion frozen first, the canonical code path run before any substitute, exact inputs, environment, seeds and commands captured, and a verdict from a fixed set, reproduced, reproduced within tolerance, partially…