Borrowing it
Nothing to install: this file belongs to Smart-AI-Memory/attune-ai. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Smart-AI-Memory/attune-ai/main/.agents/skills/quick-test/SKILL.mdgit clone --depth 1 https://github.com/Smart-AI-Memory/attune-aiWrote 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/smart-ai-memory/attune-ai/quick-test)<a href="https://agentmods.dev/skills/smart-ai-memory/attune-ai/quick-test"><img src="https://agentmods.dev/badge/skills/smart-ai-memory/attune-ai/quick-test/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/smart-ai-memory/attune-ai/quick-test"><img src="https://agentmods.dev/badge/skills/smart-ai-memory/attune-ai/quick-test.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 | $0.00009 | $0.00207 |
| Opus 5.5 | $0.00004 | $0.00083 |
| Sonnet 5.5 | $0.00002 | $0.00041 |
| Haiku 4.5 | $0.00001 | $0.00021 |
Grade A, and why
quick-test 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.
What it actually says
quick-test
Fast feedback loop — run tests related to recently changed files only.
Context (pre-computed)
git diff --name-only HEAD 2>/dev/null
git diff --name-only --cached 2>/dev/null
Instructions
- Look at the changed files from the context above
- For each changed
.pyfile insrc/, find the corresponding test file:src/attune/foo/bar.py→tests/unit/foo/test_bar.pysrc/attune/foo.py→tests/unit/test_foo.py
- Run only those test files:
uv run pytest <test_files> -x -q --tb=short
- If no matching test files exist, say so and offer
to generate them with
/testing gen - Report pass/fail summary
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 · 33 lines · 9 tokens per session scan A 355601a433f9
quick-test is a skill published in the GitHub repository Smart-AI-Memory/attune-ai (10 stars, last pushed today), licensed Apache-2.0. It adds 9 tokens to every session and 207 once invoked, about $0.0000 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 2026-08-31.
Other skills, from other repositories
attest
Verifying spec compliance: extracts ACs from specs, adversarially checks conformance, generates BDD scenarios and traceability matrices. Use when impl must be proven to match a PRD/SRS/AC.
matrix
Controlling combinatorial explosion across multi-dimensional axes: minimum coverage sets, execution plans, test/deploy/UX/risk prioritization. Use when scoping multi-axis combinations.
probe
Integrating OWASP ZAP/Burp Suite/Nuclei, planning penetration tests, executing DAST, and scanning for vulnerabilities. For runtime vulnerability validation. Complements Sentinel static analysis.
siege
Verifying system resilience via load testing, contract testing, chaos engineering, and mutation testing. Use for limit verification, non-functional testing, or reliability validation.
voyager
Authoring web and native E2E tests, including Playwright, Appium, XCUITest, device farms, visual regression, and App Store screenshot pipelines. Not for unit/load tests.
radar
Adding edge-case tests, repairing flaky tests, and improving coverage. Use when test gaps need filling or regressions need guarding. Supports JS/TS, Python, Go, Rust, and Java.