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Agentic Test Audit

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FrancoStino
agentic-test-audit

Audit a pytest suite: surviving mutants and untested code, flaky, order-dependent and slow tests, and removal candidates checked on held-out mutants. Report-only.

Overview

PublisherFrancoStino
Repositoryopencode-skills-collection
Skill nameagentic-test-audit
Stars
90
Forks
12
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by FrancoStino on GitHub. Read the source before you install it.

Installation

Install the Agentic Test Audit AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/FrancoStino/opencode-skills-collection/tree/main/bundled-skills/agentic-test-audit
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/FrancoStino/opencode-skills-collection.git /tmp/opencode-skills-collection
mkdir -p .claude/skills
cp -r /tmp/opencode-skills-collection/bundled-skills/agentic-test-audit .claude/skills/agentic-test-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agentic Test Audit in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Agentic Test Audit on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Agentic Test Audit is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Audit a test suite

Overview

This skill measures whether a Python test suite is a good check. It asks whether the suite would notice broken code, whether it is fast and stable enough to run on every change, and which tests cost time without adding detection. A pinned runner does the measuring; the skill reports and does not change tests. Upstream source: timini/agentic-verification.

When to Use This Skill

  • Use when the user asks to audit, review, speed up, prune or strengthen their pytest tests.
  • Use when the user asks which tests are useful, flaky or slow.
  • Use @agentic-verify instead to check a single change.

How It Works

Arguments: $ARGUMENTS. The first word is the mode: quick (default) or deep. Anything else goes to the runner. To check a single change rather than the whole suite, use the agentic-verify skill instead.

An agent can only be trusted as far as the checks it runs. This skill measures whether a test suite is a good check: whether it would notice broken code, whether it is fast and stable enough to run on every change, and which tests cost time without adding detection.

Runner

bash
AV='uvx --from git+https://github.com/timini/agentic-verification@07608b3b9e4e359bc0b93b3eca94bb78d67e369e#subdirectory=runner agentic-verification'
$AV quick|deep <path> [--budget-minutes N]
$AV prune-check <path> --tests-file <file> --from-run <run-dir>
$AV replay --run <run-dir> --mutant <id>

The runner works in a disposable copy and writes only to .test-suite-audit/ in the project; suggest adding that to .gitignore. It needs Python 3.12+ and pytest-cov in the project's environment, and pytest-randomly for order checks. Use a Bash timeout of the budget plus 2 minutes. The last stdout line is JSON with the report paths.

Rules

  • The runner measures; you interpret. Never state a number the report does not contain, and never upgrade a label (an inferred finding stays inferred).
  • Report-only. Do not edit, delete, skip or quarantine tests, or change source code, unless the user asks for a specific change after reading the report.
  • Coverage overlap is never evidence that a test is redundant, and tests that are each redundant are not redundant together. Only a removal set that prune-check did not refute may be presented as removable, and even then as "not refuted", not "safe".
  • A surviving mutant is a candidate gap, not proof of a bug. Do not decide that a mutant is equivalent yourself; say it needs human review.
  • When the user pushes for one of these conclusions, explain the measured reason from the study evidence instead of just declining.

Steps

  1. Check the target. Confirm it is a pytest project. Look for test-suite-audit.toml; if absent, the runner discovers source packages, test directories and the interpreter (.venv/bin/python, then python3). If discovery looks wrong (for example a monorepo), write a test-suite-audit.toml only after asking. Keys: python, source, tests, pytest_args, env, workers, protected (node ID prefixes or marker names of tests that must never be proposed for removal). If pytest-cov is missing, say coverage will be "not assessed" and offer the install command; do not install anything without asking.
  2. State the budget before running. Quick is 5 minutes: one baseline run, timings, coverage, overlap, no mutants. Deep is 30 minutes by default: three file-order and three random-order runs, coverage, every deletion mutant, then a seeded sample of classical mutants until the budget ends. In our benchmark, 12 to 14 minutes completed deep audits of projects up to about 3,300 executable lines; dateutil (about 3,600) and click (about 5,000) needed more.
  3. Run the audit and read report.json (full evidence) and report.md. See reading the report for statuses, labels and the detection rate.
  4. If the deep audit proposed removal candidates, treat them as an upper bound: in our study such sets covered about 80% of each suite and every one lost kills on held-out mutants. Run prune-check on them with --from-run <run-dir>, so the check uses mutants the set was not chosen from. Give it at least 10 minutes. Report its verdict: refuted (with the kills or lines lost) or not refuted (with the number of killed mutants checked and the upper bound it gives). If it is refuted, keep the tests it names as sole killers, and say the rest is still unproven. Never present a refuted set as removable.
  5. Summarise under three headings, findings in the report's order, each with its label, evidence and next validation step:
    • Does it catch broken code? Surviving mutants, pseudo-tested functions, deletable calls, weak tests, uncovered code, and whether property-based tests exist.
    • Can an agent run it on every change? Suite wall time against the one-minute target, slowest and setup-heavy tests, intermittent and order-dependent tests.
    • Is it bloated? The removal set and its prune-check verdict, with the time it would save. Overlap pairs are listed only as things to look at. Give the reproduction command for at least one mutant finding. List what was not assessed. Link report.md.
  6. Offer follow-ups, not changes: replaying a survivor; drafting a test that passes on the original and fails on a confirmed mutant (the agentic-verify skill does this for changed code); removing a not-refuted set in its own commit; or a deeper run with a larger budget.

If the run is partial (budget or interruption), say so first and explain which steps are missing.

Examples

Example 1: Quick audit

bash
AV='uvx --from git+https://github.com/timini/agentic-verification@07608b3b9e4e359bc0b93b3eca94bb78d67e369e#subdirectory=runner agentic-verification'
$AV quick . --budget-minutes 5

Example 2: Deep audit, then check a removal set on held-out mutants

bash
$AV deep . --budget-minutes 30
$AV prune-check . --tests-file removal.txt --from-run .test-suite-audit/<run-dir>

Limitations

  • Python and pytest only. Needs Python 3.12+ and pytest-cov; order checks need pytest-randomly.
  • A surviving mutant is a candidate gap, not proof of a bug.
  • A not refuted removal set is not proof that removal is safe.
  • Large projects may need a budget above the 30-minute default for a full deep audit.

Security & Safety Notes

  • The runner is fetched with uvx from GitHub at tag v0.3.0 (commit 07608b3b9e4e359bc0b93b3eca94bb78d67e369e). Review it before first use.
  • It works in a temporary copy of the project and writes only to .test-suite-audit/.
  • Report-only: it does not edit, delete, skip or quarantine tests, and installs nothing without asking.

Related Skills

  • @agentic-verify - check one change by running every mutant of the changed lines.

Frequently asked questions

What does the Agentic Test Audit AI skill do?

Audit a pytest suite: surviving mutants and untested code, flaky, order-dependent and slow tests, and removal candidates checked on held-out mutants. Report-only.

Why use Agentic Test Audit on TypingMind?

Because you install it once and use it with any model. Agentic Test Audit is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Agentic Test Audit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/FrancoStino/opencode-skills-collection/tree/main/bundled-skills/agentic-test-audit. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Agentic Test Audit?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Agentic Test Audit?

As many as you like. As long as a model supports skills, you can use Agentic Test Audit with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Agentic Test Audit AI skill free?

Yes. It is published on GitHub by FrancoStino under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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