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 cenconq25/claude-code-app-studio --skill team-qagit clone --depth 1 https://github.com/cenconq25/claude-code-app-studioWrote 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/cenconq25/claude-code-app-studio/team-qa)<a href="https://agentmods.dev/skills/cenconq25/claude-code-app-studio/team-qa"><img src="https://agentmods.dev/badge/skills/cenconq25/claude-code-app-studio/team-qa/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/cenconq25/claude-code-app-studio/team-qa"><img src="https://agentmods.dev/badge/skills/cenconq25/claude-code-app-studio/team-qa.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 | $0.00059 | $0.01946 |
| Opus 5 | $0.00030 | $0.00973 |
| Sonnet 5 | $0.00012 | $0.00389 |
| Haiku 4.5 | $0.00006 | $0.00195 |
Grade A, and why
team-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 6d 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.
How it starts
The opening of the file, as written. The whole thing — 263 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Team QA
End-to-end orchestrator for a testing cycle. Spawns qa-lead and qa-tester subagents, walks through plan, smoke gate, manual execution, and lands on a sign-off verdict the build either passes or does not.
Team Composition
- qa-lead — strategy, story classification, sign-off verdict.
- qa-tester — manual test case writing, bug report drafting, on-device walkthrough.
Spawn each via Task with subagent_type: qa-lead or qa-tester. Always
hand the subagent the full context (story paths, plan path, device matrix,
scope constraints). Run independent qa-tester invocations in parallel
when scaffolding test cases for multiple stories at once.
Phase 1: Load Context
Resolve the scope:
sprint-NN-> read all stories inproduction/sprints/sprint-NN/.feature: <name>-> glob stories tagged for that feature.- No argument -> consult
production/session-state/active.md.
Read production/qa/qa-plan-[sprint]-*.md if it exists. If not, prompt:
"No QA plan for this scope. Run /qa-plan first?"
Read production/stage.txt for the current project phase.
Report to user:
"QA cycle for [scope]. [N] stories. Stage: [phase]. Plan: [path or none]. Begin?"
Phase 2: QA Strategy via qa-lead
Spawn qa-lead via Task. Prompt template:
Read every story in [scope] and the QA plan at [path]. Produce a strategy: classify each story by Type (Logic/Integration/Visual/UI/ Config-Data); flag any story missing acceptance criteria or test evidence; estimate manual hours; assess whether the smoke spec covers the scope adequately. Return a summary table.
Render the qa-lead's output. Use AskUserQuestion:
[A] Looks good — proceed to smoke check[B] Adjust classifications first[C] Skip blocked stories and proceed with the rest[D] Cancel — resolve blockers first
Phase 3: Smoke Gate
Run the /smoke-check skill (or invoke its workflow inline). Capture the
verdict.
- PASS -> continue.
- PASS WITH WARNINGS -> note for sign-off, continue.
- FAIL -> stop. Surface failures, point user at fix path. The cycle cannot proceed past a failed smoke check.
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.
- 6d ago First seen · 263 lines · 59 tokens per session scan A 0fe4bc2d2657
team-qa is a skill published in the GitHub repository cenconq25/claude-code-app-studio (40 stars, last pushed 4mo ago), licensed MIT. It adds 59 tokens to every session and 1,946 once invoked, about $0.0003 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 2026-09-03.
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