Skip to content
cccskills
FreePublished on GitHub

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.

The English summary is being prepared. Showing the original description.

See how to install

Files included (1)

  • SKILL.md5.3 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

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

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 cod

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Automate 21risk tasks via Rube MCP (Composio). Always search tools first for current schemas.

The English summary is being prepared. Showing the original description.

ranbot-ai/awesome-skills7Updated Oct 22, 2026

Automate 2chat tasks via Rube MCP (Composio). Always search tools first for current schemas.

The English summary is being prepared. Showing the original description.

ranbot-ai/awesome-skills7Updated Oct 22, 2026

Usar al iniciar o mejorar un proyecto de IA: diagnostica código y restricciones, propone skills existentes y redacta tareas y un plan de implementación en español.

The English summary is being prepared. Showing the original description.

ranbot-ai/awesome-skills7Updated Oct 22, 2026

Usar para crear o mejorar una skill de Andru.ia: define entradas y resultados, redacta SKILL.md con ejemplos y límites, valida la estructura y prepara una PR.

The English summary is being prepared. Showing the original description.

ranbot-ai/awesome-skills7Updated Oct 22, 2026

Usar tras definir el sector de un proyecto: prepara un dossier en español con contexto, normas aplicables, patrones UX y riesgos, distinguiendo fuentes verificadas de hipótesis.

The English summary is being prepared. Showing the original description.

ranbot-ai/awesome-skills7Updated Oct 22, 2026

AI-powered presentation generation via the 2slides API — create slides from text, match a reference image style, summarize documents into decks, add AI voice narration, and export pages/audio. Use f

The English summary is being prepared. Showing the original description.

ranbot-ai/awesome-skills7Updated Oct 22, 2026

See all skills by ranbot-ai

Report a problem with this skill