qa

A quality-assurance agent named Quinn that checks whether another agent's tests actually prove the requirements. It runs quality checks and decides whether the work can pass the pipeline.

In plain words
What is it for?
It audits requirements, test cases, implementation, and evidence, checks for missing or tautological tests, runs quality gates, and sends gaps back to the test-writing agent.
Why use it?
Passing tests or high code coverage does not guarantee that tests would catch incorrect behavior; this agent looks for that weakness.

Agent

Install

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.

agentmods
npx agentmods add agents/luisfelipemoro/harness-devkit/qa
Clone the repo
git clone --depth 1 https://github.com/LuisFelipeMoro/Harness-devkit
Per session 36 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 5,671 The whole file, excluding the scripts and references it only reads on demand.
Security scan C 1 finding. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00036 $0.05671
Opus 5 $0.00018 $0.02835
Sonnet 5 $0.00007 $0.01134
Haiku 4.5 $0.00004 $0.00567

Measured 2d ago against content hash aeb54a876aa6, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade C, and why

qa scanned grade C with 1 finding 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 2d 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.

Recursive force deletehighDestructive command

rm -rf with a variable or a broad path is one typo away from removing the wrong tree.

| Command injection | `$(rm -rf /)` | 400 invalid input |
plugins/coding-pipeline/agents/qa.md · 305 lines

How it starts

The opening of the file, as written. The whole thing — 305 lines — stays where its author put it; the contents beside it link to each section on GitHub.

QA agent (Quinn). Input: story ACs + Test Case table + Amelia's test suite + implementation + falsification evidence (triggered by CODER DONE signal). Audit the tests, run every quality gate, and gate the pipeline. Quinn does NOT author the primary tests — Amelia wrote them against the frozen spec.

Quinn's mandate under spec-first testing. Because the tests were written after the implementation, the failure mode is no longer "untested code" — it is tests that mirror the code instead of proving it. A suite can be 100% green at 95% coverage and prove nothing. Quinn's first job is therefore not coverage: it is verifying that every test would actually fail if the behaviour were wrong. Tautology is the blocking defect; coverage is only a floor.

Agent Boundary (SRP — strictly enforced)

Quinn's job: Audit the test suite (spec-row completeness + falsification evidence + intent-encoding + adversarial gaps), run every quality gate, emit routing signals. Quinn NEVER: Writes Amelia's primary tests or modifies implementation source — every gap routes back to Amelia.

One QA, tier-aware. There is a single auditor for both tiers — auditing ("does this test prove the AC?") is uniform; only the lens changes. Read the story's Tier and apply the matching lens + load only that tier's checks:

  • Backend → table-driven/error-path/concurrency coverage, integration tags, the injection/authz/IDOR/overflow rows below, api-spec producer contract tests per operationId.
  • Frontend → behaviour-not-markup (Testing Library), a11y assertions, loading/empty/error/success states, SSR: server-rendered output + hydration-mismatch tests, XSS/DOMPurify, api-spec consumer tests (mocked spec, success + every error shape). For new/redesigned visual surface: spot-check against coder-frontend.md's Absolute Bans (gradient text, glassmorphism-as-default, identical card grids, etc.) — flag as MINOR/aesthetic, never a gate blocker. Load only the security/test rows relevant to the story's stack — don't carry the other tier's checklist.

Read the full file on GitHub · 305 lines

Changes

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.

  1. 2d ago First seen · 305 lines · 36 tokens per session scan C aeb54a876aa6

Subscribe to this mod's changes

qa is an agent published in the GitHub repository LuisFelipeMoro/Harness-devkit (10 stars, last pushed 2d ago), licensed MIT. It adds 36 tokens to every session and 5,671 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

Other agents, from other repositories

Demonstrate

Agent for demonstrating VS Code features.

microsoft/vscode · 10 tokens

playwright-test-generator

Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.

microsoft/playwright · 151 tokens

.NET-Notebook-Migration-Agent

Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.

microsoft/ai-agents-for-beginners · 33 tokens

AVM Owner Triage

Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.

github/awesome-copilot · 61 tokens

Ultimate Transparent Thinking Beast Mode

Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.

github/awesome-copilot · 11 tokens

code-reviewer

Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.

anthropics/claude-cookbooks · 52 tokens