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 iradoweck/antigravity-awesome-skills --skill agentic-verifygit clone --depth 1 https://github.com/iradoweck/antigravity-awesome-skillsWrote 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/iradoweck/antigravity-awesome-skills/agentic-verify)<a href="https://agentmods.dev/skills/iradoweck/antigravity-awesome-skills/agentic-verify"><img src="https://agentmods.dev/badge/skills/iradoweck/antigravity-awesome-skills/agentic-verify/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/iradoweck/antigravity-awesome-skills/agentic-verify"><img src="https://agentmods.dev/badge/skills/iradoweck/antigravity-awesome-skills/agentic-verify.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 | $0.00039 | $0.02136 |
| Opus 5.5 | $0.00016 | $0.00854 |
| Sonnet 5.5 | $0.00008 | $0.00427 |
| Haiku 4.5 | $0.00004 | $0.00214 |
Grade A, and why
agentic-verify 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 5d 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.
This is a copy
100% identical to agentic-verify — 4 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Verify a change
Overview
This skill checks that code Claude wrote or changed is actually tested. It defines the check before coding, runs every mutant of the changed lines with a pinned runner, and writes tests until the tests catch them. It reports which mutants were killed and which new tests fail on the old code, so the user can trust the change from evidence. Upstream source: timini/agentic-verification.
When to Use This Skill
- Use after implementing a feature or fix in a Python/pytest project, before saying the work is done.
- Use when the user asks whether a change is tested.
- Use
@agentic-test-auditinstead to review a whole test suite rather than one change.
How It Works
Arguments: $ARGUMENTS. --base is the git revision to compare with: HEAD (default) checks
uncommitted work, a branch such as main checks a whole branch. Other options go to the runner.
The goal is that the user can trust the change from evidence, without reading every line. A change is verified when the tests fail if any part of it is wrong. Passing tests alone do not show that: our study found 71% of the calls that could be deleted unnoticed sat in functions that looked well tested.
Runner
AV='uvx --from git+https://github.com/timini/agentic-verification@07608b3b9e4e359bc0b93b3eca94bb78d67e369e#subdirectory=runner agentic-verification'
$AV verify <project-root> --base <rev> [--budget-minutes N]
$AV replay --run <run-dir> --mutant <id> --all
$AV base-check <project-root> --base <rev> [tests...]
Pass the project root (the directory pytest runs in), not a package directory or a file.
base-check runs tests against the production source as it was at --base, in a disposable
copy, and lists the tests that pass now and fail there: those detect the change.
The runner works in a disposable copy and writes only to .test-suite-audit/ in the project.
It needs Python 3.12+ and pytest-cov in the project's environment, plus pytest-randomly for
the order check. Use a Bash timeout of the budget plus 2 minutes. The last stdout line is JSON
with the verdict and report paths.
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.
- 5d ago First seen · 154 lines · 39 tokens per session scan A 6c37c3558e7e
agentic-verify is a skill published in the GitHub repository iradoweck/antigravity-awesome-skills (30 stars, last pushed 5d ago), licensed MIT. It adds 39 tokens to every session and 2,136 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to agentic-verify, differing in 4 lines, and is treated as a copy.
Other skills, from other repositories
agentic-verify
Check that a Python change is caught by tests: run every mutant of the changed lines and write tests until they catch them. Reports evidence instead of a diff to read.
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.
test-critic
Evaluates test quality — coverage analysis, mutation testing (do tests actually catch bugs?), test gap detection, and test code quality scoring. Supports Jest, Vitest, pytest. Use after writing tests to verify they're meaningful.
verification-strategy
Thorough verification of completed work before declaring done.
test-generator
Generate pytest test cases for Python functions and classes.
testing-perf
Performance and load testing patterns — k6 load tests, Locust stress tests, pytest execution optimization (xdist parallel, plugins), test type classification, and performance benchmarking. Use when writing load tests, optimizing test execution speed, or setting up pytest infrastructure.