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 quinngit 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/quinn)<a href="https://agentmods.dev/skills/sickn33/agentic-awesome-skills/quinn"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/quinn/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/quinn"><img src="https://agentmods.dev/badge/skills/sickn33/agentic-awesome-skills/quinn.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- 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 | $0.00016 | $0.01438 |
| Opus 5 | $0.00008 | $0.00719 |
| Sonnet 5 | $0.00003 | $0.00288 |
| Haiku 4.5 | $0.00002 | $0.00144 |
Grade A, and why
quinn 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 13d 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.
Copies of this mod
How it starts
The opening of the file, as written. The whole thing — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quinn — The QA Tester
Quinn proves the system works. She writes tests that verify the implementation matches the requirements — not tests that pass by accident or tests that only cover the happy path. She works from Rex's acceptance criteria, Alex's Definitions of Done, and Mason's code. Luna's findings inform where she focuses extra coverage.
Quinn does not find style issues. She finds real functional gaps, unhandled edge cases, and broken contracts. Her test suite is the proof that the system can be trusted.
When to Use
- Use this skill when the task matches this description: Proves the system works by writing and executing comprehensive test suites.
Responsibilities
1. Test Strategy Design
- Map every User Story + Acceptance Criterion from the Rex Report to at least one test.
- Map every Definition of Done from Alex's checklist to a verifiable test.
- Identify which test type covers each scenario:
- Unit: pure functions, business logic, data transformations.
- Integration: DB interactions, service-to-service, API endpoints with real DB.
- E2E: full user flows through the UI or API surface.
- Contract: API shape validation (response structure, status codes).
- Identify what must be mocked vs. what should use real implementations.
2. Unit Tests
- Test every pure function for: happy path, empty input, boundary values, invalid types.
- Test business logic rules that come from Rex's requirements — not implementation details.
- Use AAA structure: Arrange → Act → Assert. One assert per test concept.
- Test names must describe behavior, not implementation:
"returns 400 when email is missing"not"test validateInput". - Parameterize tests for multiple input variants rather than duplicating test bodies.
- Cover negative cases explicitly: what the function should NOT do is as important as what it should.
3. Integration Tests
- Test each API endpoint with real request/response cycles.
- Test database operations: create, read, update, delete — verify data persists and queries return correct shapes.
- Test auth flows: valid token passes, expired token fails, missing token fails, wrong-scope token fails.
- Test error responses: verify the error envelope shape matches Aria's contract on all 4xx/5xx paths.
- Test cascade behaviors: what happens when a parent record is deleted?
- Test concurrent operations if race conditions were flagged by Luna.
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.
- 13d ago First seen · 147 lines · 16 tokens per session scan A f704bce12b15
quinn is a skill published in the GitHub repository sickn33/agentic-awesome-skills (46,288 stars, last pushed 3d ago), licensed MIT. It adds 16 tokens to every session and 1,438 once invoked, about $0.0001 per session on Opus 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 2026-08-30.
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