quality-assurance

quality-assurance is a skill for Claude Code, Codex from fmind/dot. It costs 48 tokens per session (1,122 once invoked), scanned A, original, MIT.

A risk-based testing workflow that checks whether important product behavior has actually been proved. It covers tests from small code checks through full user journeys, including browser, accessibility, performance, and manual testing.

In plain words
What is it for?
Use it to plan and run tests for success paths, errors, boundaries, permissions, retries, cancellation, concurrency, and recovery.
Why use it?
It prevents teams from testing only the changed code while missing risky failures in real use. It also shows which requirements and failure cases remain untested.

Skill for Claude CodeCodex

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 skills/fmind/dot/quality-assurance
Any agent
npx skills add fmind/dot --skill quality-assurance
Clone the repo
git clone --depth 1 https://github.com/fmind/dot

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for quality-assurance

README.md
[![agentmods](https://agentmods.dev/badge/skills/fmind/dot/quality-assurance.svg)](https://agentmods.dev/skills/fmind/dot/quality-assurance)
Your own site
<a href="https://agentmods.dev/skills/fmind/dot/quality-assurance"><img src="https://agentmods.dev/badge/skills/fmind/dot/quality-assurance.svg" alt="Measured on agentmods" height="20"></a>
Per session 48 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,122 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. 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 5.1 $0.00048 $0.01122
Opus 5 $0.00024 $0.00561
Sonnet 5 $0.00010 $0.00224
Haiku 4.5 $0.00005 $0.00112

Measured 2d ago against content hash d5bc4796195a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

quality-assurance 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 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.

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.

skills/quality-assurance/SKILL.md · 42 lines

How it starts

The opening of the file, as written. The whole thing — 42 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Quality Assurance

Test the risks that matter with real behavior and report what stays unproved; test-driven-development owns implementation, diff-review owns one change, and production-readiness owns the launch gate.

Workflow

  1. Resolve the candidate: Record the requirement or spec, base and head or working-tree identity, environment, versions, and existing proof.
  2. Build the risk matrix: Start from requirements, changed behavior, user journeys, and known failure modes; rank by impact, likelihood, detectability, reversibility, and change exposure, and cover the highest risk first.
  3. Choose the lightest layer: Unit tests for logic, property or fuzz tests for wide input spaces, contract tests for interfaces, integration tests for owned boundaries, end-to-end tests for critical journeys.
  4. Prepare controlled state: Create isolated data with explicit setup and teardown; confirm the test cannot mutate user or external state beyond the authorized scope.
  5. Run changed behavior first: Exercise the success path, unhappy paths, boundaries, permissions, cancellation, retries, concurrency, and recovery the requirements promise.
  6. Test real presentation: Prefer direct HTTP or API evidence; use a browser only for rendering, interaction, session state, or accessibility, driving it with playwright through roles and labels and verifying state after every action.
  7. Test non-functional risk: Measure latency, load, resource use, resilience, security boundaries, and observability only where the matrix or spec requires; set the baseline and threshold first with benchmark.
  8. Run regression proof: Execute the relevant package or subsystem suite before the full gate.
  9. Gate the candidate: Run the full gate (mise run all); if the tree carries unrelated changes and the gate write-formats, run it in a temporary git worktree or fall back to mise run check and mise run test (see mise).
  10. Retest fixes narrowly: Reproduce the original failure, verify the fix, then rerun impacted journeys and the gate; avoid open-ended visual polishing loops.
  11. Report the matrix: Record per case the risk and requirement, layer and environment, preconditions, steps or command, expected result, actual evidence, status (pass, fail, blocked, or not run), and cleanup with residual risk.
  12. Summarize: Lead with blockers, then failures, passes, and untested boundaries with the authority or capability needed to test them; report the highest proven rung of the proof ladder.

Read the full file on GitHub · 42 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 · 42 lines · 48 tokens per session scan A d5bc4796195a

Subscribe to this mod's changes

quality-assurance is a skill published in the GitHub repository fmind/dot (4 stars, last pushed today), licensed MIT. It adds 48 tokens to every session and 1,122 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 2026-09-03.