AAS Core is a local control plane for coding agents that lets them search a large catalogue of skills, choose a stack, validate it, and create a reproducible plan. It is used to assemble and review agent workflows through its CLI, local MCP server, catalogue, plugins, and Workbench. The catalogue add-ons provide the skills, plugins, bundles, and workflows that AAS Core helps agents select and validate.
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 sickn33/agentic-awesome-skills --skill agentic-test-auditgit clone --depth 1 https://github.com/sickn33/agentic-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/sickn33/agentic-awesome-skills/agentic-test-audit)<a href="https://agentmods.dev/skills/sickn33/agentic-awesome-skills/agentic-test-audit"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/agentic-test-audit/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/sickn33/agentic-awesome-skills/agentic-test-audit"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/agentic-test-audit.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 | $0.00038 | $0.01943 |
| Opus 5.5 | $0.00015 | $0.00777 |
| Sonnet 5.5 | $0.00008 | $0.00389 |
| Haiku 4.5 | $0.00004 | $0.00194 |
Grade A, and why
agentic-test-audit 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 yesterday.
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 — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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-verifyinstead 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/[email protected]#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-checkdid 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.
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.
- yesterday First seen · 144 lines · 38 tokens per session scan A 8f53ac1bdcb9
agentic-test-audit is a skill published in the GitHub repository sickn33/agentic-awesome-skills (47,779 stars, last pushed today), licensed MIT. It adds 38 tokens to every session and 1,943 once invoked, about $0.0002 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 2026-10-17.
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