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 agentmods add skills/everyone-needs-a-copilot/claude-copilot/qanpx skills add Everyone-Needs-A-Copilot/claude-copilot --skill qagit clone --depth 1 https://github.com/Everyone-Needs-A-Copilot/claude-copilotWrote 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/everyone-needs-a-copilot/claude-copilot/qa)<a href="https://agentmods.dev/skills/everyone-needs-a-copilot/claude-copilot/qa"><img src="https://agentmods.dev/badge/skills/everyone-needs-a-copilot/claude-copilot/qa.svg" alt="Measured on agentmods" 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 | $0.00044 | $0.00887 |
| Opus 5 | $0.00022 | $0.00443 |
| Sonnet 5 | $0.00009 | $0.00177 |
| Haiku 4.5 | $0.00004 | $0.00089 |
Grade A, and why
qa 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 today.
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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
QA Engineer
Use this skill to make quality concrete.
Operating Lens
- Verify behavior, not just builds.
- Reproduce defects before fixing when possible.
- Cover edge cases: empty, null, invalid, boundary, permission, race, and recovery paths.
- For product-facing work, verify design intent, workflow quality, visual fidelity, responsive behavior, accessibility, and product language.
- Check alignment with
SOUL.md, architecture guiding principles, or specialist design outputs when they apply. - Prefer deterministic tests that explain the expected behavior.
- Report residual risk honestly.
- Use Live Docs when verifying behavior tied to installed third-party APIs.
Success Criteria
- Acceptance criteria are explicit.
- Relevant tests or checks are run and reported.
- Edge cases and regression paths are covered in proportion to risk.
- Product-facing changes include design-fidelity checks.
- QA verdict is recorded in a
testwork product when task context exists. - Passing QA verdicts cite an external
ARTIFACT:marker, not only the model's judgment. - QA-required tasks can pass
scripts/copilot-gate.sh.
Workflow
- Check task and implementation work products when a task exists.
- Hydrate config and search memory for prior failures when
ccis configured. - Define the behavior under test and acceptance criteria.
- Use
cc docs get <pkg>when verification depends on installed third-party APIs. - Identify likely regression paths, edge cases, and design-fidelity risks.
- Run or write the smallest meaningful tests.
- Exercise user-facing flows when UI or workflow behavior changed.
- Inspect responsive states, accessibility behavior, visual hierarchy, and product language when product-facing.
- Store a
testwork product with anARTIFACT:marker andVERDICT: APPROVED,VERDICT: APPROVED-WITH-MINOR-FIXES, orVERDICT: REJECTED.
Output
Return:
- acceptance criteria
- tests/checks run
- design-fidelity checks when product-facing
- pass/fail verdict
- uncovered risk
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.
- today First seen · 93 lines · 44 tokens per session scan A 20d0789dbae7
qa is a skill published in the GitHub repository Everyone-Needs-A-Copilot/claude-copilot (13 stars, last pushed today), licensed MIT. It adds 44 tokens to every session and 887 once invoked, about $0.0002 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 2026-09-04.
Other skills, from other repositories
cadence-first
Meta-orchestrator: assigns skill-chain cadence per block (Tier 1/2/3) via Q1-Q6 rules. Reads target taskblocks + task.md, writes cadence-decisions-{R}.md artifact. Standalone (executor invokes) or Batch (generator hand-off). Use when: deciding cadence for a batch of blocks, generator hand-off from decomposition skills.
ship-first
Final task completion protocol: report → user-note → deploy → smoke test → close? → guide? → routing.db → propagate → STATUS → sessions. Invoked from fast-track and pipeline flows after task execution, not directly by the user. Use when: invoked after the execute step of a fast-track or pipeline task.
ui-ai-first
Final audit of a large task before closure — finds which operations are available only via code / curl / SQL and decides per each: automate with a skill or AI agent (A) or create a UI task (B). Protects against invisible usability debt. Walks through each implemented block: reads task.md + reports + guides + code →…
decision-first
Makes an architectural / project / scope decision using a 5-part model INSTEAD of asking the user. Structure: 🎯 Decision / Why / 🛡 Security / 📈 Scalability / Alternatives / Plain-language analogy. 1 question = 1 atomic artifact. Use when: the agent is about to ask an architectural / scope question…
library-first
Mandatory protocol before executing any fast-track task. Analyzes the task, builds a table: what we do / where it comes from / how many lines of code. Principle: maximum reuse of existing libraries and components, minimum new code. Waits for explicit user approval — does nothing until confirmed. Use when: fast-track…
fixture-new
Creates a parity fixture — the frozen scenario plus the contract its output must satisfy. Asks which skill and case, what shape the run must produce, and writes input.md and expect.yml. Ends by proving the new fixture actually fails on an empty directory. A fixture that passes when nothing ran is worse than no…