qa-expert

Overview of qa-expert skill

What qa-expert does

The qa-expert skill is a QA planning and quality-gate assistant for teams that need a clearer testing strategy, not just a list of generic test ideas. It is best used when you want to decide what to test first, how deep to test it, and which checks should block commits, merges, or releases.

Who should install qa-expert

qa-expert is a good fit for engineering leads, test automation engineers, platform teams, and product teams that need lightweight structure around quality without building a full QA program from scratch. It is especially relevant if you want qa-expert for Test Automation planning, coverage decisions, or release gate design.

Real job-to-be-done

Most users are not looking for abstract QA theory. They need help turning a feature, repo, or release into:

• a risk-based test plan
• a reasonable testing pyramid
• concrete quality gates
• a coverage review with next actions

That is where qa-expert skill is more useful than an ordinary one-off prompt.

What makes this skill different

The useful differentiator is its opinionated structure:

• risk-based prioritization by impact
• explicit testing pyramid allocation
• staged quality gates such as pre-commit and pre-merge
• supporting references for gates, metrics, and strategy
• helper scripts that generate test-plan and coverage-analysis documents

This makes qa-expert more install-worthy for process design than a generic “write some tests” assistant.

What to know before adopting

This skill is strongest as a planning and governance aid. Based on the repository, it does not ship framework-specific test implementations, CI templates, or deep tooling integrations by default. If you need Playwright/Cypress/Jest code generation, this is not the whole solution by itself. If you need a repeatable QA decision framework, it is a strong starting point.

How to Use qa-expert skill

Install qa-expert in your skills environment

The repository does not expose a skill-local install command in SKILL.md, so use the collection install pattern:

npx skills add https://github.com/zhaono1/agent-playbook --skill qa-expert

After install, verify the skill is available in your agent environment and open the source files before relying on defaults.

Read these files first

For a fast understanding of qa-expert usage, read in this order:

1. skills/qa-expert/SKILL.md
2. skills/qa-expert/references/strategy.md
3. skills/qa-expert/references/gates.md
4. skills/qa-expert/references/metrics.md
5. skills/qa-expert/scripts/generate_test_plan.py
6. skills/qa-expert/scripts/coverage_analysis.py

That path gives you the decision model first, then the reusable templates.

When to invoke qa-expert skill

Use qa-expert when your prompt sounds like one of these:

• “Create a QA plan for this feature.”
• “Set up quality gates for our repo.”
• “What tests should we write first?”
• “Review our coverage gaps and suggest priorities.”
• “Design a release gate for a high-risk workflow.”

If your need is only “write one unit test,” this skill is probably broader than necessary.

What input qa-expert needs

The quality of output depends heavily on the context you supply. The skill works best when you provide:

• feature or system name
• user-critical flows
• risk areas such as money, auth, data loss, compliance, or integrations
• current stack and test tools
• release cadence
• current pain points like flaky E2E or low coverage
• desired gate strictness for commit, merge, and release

Without that, the skill will fall back to generic QA structure.

Turn a rough goal into a strong qa-expert prompt

Weak prompt:

Create a QA plan.

Stronger prompt:

Use qa-expert to create a QA plan for our checkout flow. Stack: React, Node.js, PostgreSQL. Critical risks: payment failure, duplicate charges, promo code edge cases, order-loss after timeout. Current tests: some unit tests, almost no integration tests, no release gates. We deploy twice weekly. Recommend test levels, coverage priorities, pre-commit and pre-merge gates, and metrics we should track for the next 30 days.

This works better because it gives the skill scope, risk, current state, and decision constraints.

Use the risk model deliberately

A practical reason to install qa-expert skill is its risk-based testing table. The repository distinguishes:

• critical areas like money, security, and data
• high-risk core features
• medium-risk secondary features
• low-risk edge features

Use that model to force prioritization. If you do not label critical paths explicitly, teams often overinvest in low-value tests and underinvest in failure-heavy workflows.

Apply the testing pyramid, not just more tests

The skill recommends a simple split:

• 60% unit
• 30% integration
• 10% E2E

Treat those as planning defaults, not fixed law. For qa-expert for Test Automation, this is useful because it helps teams resist an E2E-heavy test suite that becomes slow and flaky. Ask the skill to map real modules or journeys into each layer rather than stopping at percentages.

Use the built-in scripts for faster adoption

The support scripts are small but practical.

Generate a test plan template:

python skills/qa-expert/scripts/generate_test_plan.py --name "Checkout" --owner "Payments Team"

Generate a coverage analysis template:

python skills/qa-expert/scripts/coverage_analysis.py --name "Checkout Service" --owner "Payments Team"

These scripts do not analyze your code automatically; they generate structured documents you can fill or refine with the skill. That makes qa-expert install useful even for teams that want a lightweight docs-first workflow.

Shape outputs around decision points

A good workflow is:

1. ask qa-expert for a risk-ranked strategy
2. ask for quality gates by lifecycle stage
3. generate a test plan document
4. review coverage gaps for critical areas
5. convert recommendations into CI checks and team ownership

This sequence is more effective than asking for one huge QA answer upfront.

Adapt the quality gates to your stack

The repository examples include checks like:

• npm run lint
• npm run format:check
• npm run type-check
• npm run test:unit
• npm test
• npm audit
• npm run check:licenses

These are useful defaults for JavaScript or TypeScript projects, but you should rewrite them for your actual ecosystem. The value of qa-expert guide is the stage-based gating logic, not the exact npm commands.

What materially improves output quality

Ask the skill for:

• top 5 risks by business impact
• exact gates for pre-commit, pre-merge, and release
• which flows deserve E2E versus integration tests
• acceptable coverage threshold and where it should not be uniform
• metrics owners and review cadence

That pushes qa-expert usage from generic advice into team-operable output.

qa-expert skill FAQ

Is qa-expert good for beginners?

Yes, if you already know your product and need help structuring QA decisions. It is beginner-friendly at the strategy level because it gives a clear pyramid, gates, and metrics. It is less beginner-friendly if you expect it to teach a full testing framework from scratch.

Is qa-expert only for automated testing?

No. The skill centers strongly on test automation and quality gates, but its planning model also supports manual validation, release criteria, and risk review. Still, the strongest value is qa-expert for Test Automation strategy rather than exploratory testing coaching.

What does qa-expert do better than a normal prompt?

A normal prompt may generate a broad testing checklist. qa-expert is more useful when you need:

• prioritization by risk
• explicit gate stages
• a reusable test-plan structure
• QA metrics to track over time

In short, it gives a more repeatable operating model.

When is qa-expert a poor fit?

Skip qa-expert if you only need:

• one test case
• one bug reproduction
• framework-specific implementation details
• a deep audit of an existing CI pipeline with tool-specific remediations

The repository evidence shows stronger support for planning and templates than for implementation-heavy automation.

Does qa-expert integrate with CI out of the box?

Not directly. It provides gate examples and supporting references, but you will still need to translate them into GitHub Actions, GitLab CI, Jenkins, or another pipeline system yourself.

Can qa-expert help with coverage decisions?

Yes. This is one of the more practical reasons to use the skill. The included coverage_analysis.py script creates a coverage review template, and the strategy encourages you to focus on critical paths and recent change risk rather than chasing a single blanket percentage.

How to Improve qa-expert skill

Give qa-expert better system context

The fastest way to improve qa-expert output is to include:

• architecture summary
• critical flows
• external dependencies
• compliance or security concerns
• current test inventory
• release and incident history

The skill is only as good as the risk picture you provide.

Ask for repository-specific mapping

Do not stop at “make a QA strategy.” Ask qa-expert to map recommendations to:

• actual services or folders
• high-change modules
• specific user journeys
• named CI stages
• responsible teams

That turns a generic plan into something actionable.

Correct the most common failure mode

The main failure mode is overgeneralization. If you ask for a plan without constraints, the skill will produce a plausible but generic strategy. Fix this by forcing tradeoffs:

• limited engineer time
• maximum test runtime
• release frequency
• flaky-suite tolerance
• modules that cannot block deploys

Tradeoffs produce better prioritization.

Push beyond percentage-only coverage thinking

If the first answer focuses too much on overall coverage numbers, ask qa-expert to revise around:

• critical path coverage
• mutation-risk or recent-change areas
• missing integration contracts
• release-blocking scenarios
• defect escape patterns

This aligns the skill with real QA outcomes, not vanity metrics.

Iterate after the first draft

A productive second-round prompt is:

Revise this qa-expert plan by cutting low-value tests, identifying the three highest-risk regressions, and rewriting the gates for a team that can only maintain 15 minutes of CI time on pull requests.

This kind of iteration usually improves usefulness more than asking for more detail.

Use the reference files as answer scaffolding

If output quality is inconsistent, direct the skill to structure its answer around:

• references/strategy.md for scope and objectives
• references/gates.md for release criteria
• references/metrics.md for team reporting

This keeps qa-expert skill aligned with the repository's strongest materials instead of drifting into generic QA prose.

Pair the templates with your own examples

The bundled scripts generate document skeletons, not finished analysis. Improve results by pasting:

• a recent incident
• current CI checks
• a flaky test list
• a feature spec or PRD
• a module-level coverage snapshot

Then ask qa-expert to fill the template using that evidence. This is the highest-leverage way to improve qa-expert guide outcomes in real teams.