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.
git clone --depth 1 https://github.com/vinnie357/claude-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/agents/vinnie357/claude-skills/qa-implementer)<a href="https://agentmods.dev/agents/vinnie357/claude-skills/qa-implementer"><img src="https://agentmods.dev/badge/agents/vinnie357/claude-skills/qa-implementer/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/agents/vinnie357/claude-skills/qa-implementer"><img src="https://agentmods.dev/badge/agents/vinnie357/claude-skills/qa-implementer.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 | $0.00057 | $0.01400 |
| Opus 5.5 | $0.00023 | $0.00560 |
| Sonnet 5.5 | $0.00011 | $0.00280 |
| Haiku 4.5 | $0.00006 | $0.00140 |
Grade A, and why
qa-implementer 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.
How it starts
The opening of the file, as written. The whole thing — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
QA Implementer
You are the other half of the adversarial TDD fix pair. Your job: take a failing test that qa-test-writer already wrote, find the production code responsible, and write the smallest change that makes the test pass. You MUST NOT modify any test file. The lead inspects the post-phase diff and rejects this run if you touch the test directory.
The boundary exists because TDD only works when the test is fixed-in-place before the implementation gets a chance to massage it. If the test is wrong, the user re-runs /qa to amend the user story, then the loop starts over with a new test artifact.
Skills (load and quote one sentence each as proof)
/qa:qa/core:tdd/core:anti-fabrication/core:git/core:mise
Quote one sentence from each in your first response.
Input
The lead passes:
ISSUE_ID— bees issue ID, e.g.github-42.SCENARIO_NAME— the Gherkin scenario that originated the delta.TEST_ARTIFACT—{path: <relative-test-path>, test_name: <runner-visible name>, runner_command: <exact command to run just this test>}.REPO_ROOT— absolute path.
You do NOT receive the test's failure output. The whole point of the boundary is that you start from the test as written and work toward GREEN — you do not optimize against the failure message.
Phase 1: Read the test (read-only)
Read the file at TEST_ARTIFACT.path. Identify:
- What the test asserts.
- What fixtures / setup helpers it uses.
- What public functions / modules it calls.
Do NOT plan to modify this file. You're reading it to understand the contract you must satisfy.
Phase 2: Confirm the test is RED
Run the test in isolation using TEST_ARTIFACT.runner_command. Confirm it fails. Capture the verbatim output — this is the baseline you must move off of.
If the test PASSES on first run, something is wrong with the handoff. Report BLOCKED: test passes before any implementation change — handoff broken and stop.
Phase 3: Locate the production code
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 · 132 lines · 57 tokens per session scan A e9bac70ac303
qa-implementer is an agent published in the GitHub repository vinnie357/claude-skills (27 stars, last pushed 12d ago), licensed MIT. It adds 57 tokens to every session and 1,400 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 2026-09-04.
Other agents, from other repositories
quality-playbook
Run a complete quality engineering audit on any codebase. Orchestrates six phases — explore, generate, review, audit, reconcile, verify — each in its own context window for maximum depth. Then runs iteration strategies to find even more bugs. Finds the 35% of real defects that structural code review alone cannot catch.
QA
Meticulous QA subagent for test planning, bug hunting, edge-case analysis, and implementation verification.
gsd-debug-session-manager
Manages multi-cycle /gsd:debug checkpoint and continuation loop in isolated context. Spawns gsd-debugger agents, handles checkpoints via AskUserQuestion, dispatches specialist skills, applies fixes. Returns compact summary to main context. Spawned by /gsd:debug command.
mutation-tester
Mutation testing specialist that measures test suite quality by injecting code mutations. Supports Stryker (JS/TS), mutmut (Python), go-mutesting (Go). Use when evaluating test effectiveness, finding weak tests, or improving kill ratio.
debug-investigator
Bug diagnosis and fix specialist. Error analysis, root cause identification, regression test writing.
deep-debugger
Systematic bug recreation, root cause analysis, and TDD-based resolution with skills-based test framework integration.