qa-expert▌
daymade/claude-code-skills · updated Apr 8, 2026
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Establish world-class QA testing processes for any software project using proven methodologies from Google Testing Standards and OWASP security best practices.
QA Expert
Establish world-class QA testing processes for any software project using proven methodologies from Google Testing Standards and OWASP security best practices.
When to Use This Skill
Trigger this skill when:
- Setting up QA infrastructure for a new or existing project
- Writing standardized test cases (AAA pattern compliance)
- Executing comprehensive test plans with progress tracking
- Implementing security testing (OWASP Top 10)
- Filing bugs with proper severity classification (P0-P4)
- Generating QA reports (daily summaries, weekly progress)
- Calculating quality metrics (pass rate, coverage, gates)
- Preparing QA documentation for third-party team handoffs
- Enabling autonomous LLM-driven test execution
Quick Start
One-command initialization:
python scripts/init_qa_project.py <project-name> [output-directory]
What gets created:
- Directory structure (
tests/docs/,tests/e2e/,tests/fixtures/) - Tracking CSVs (
TEST-EXECUTION-TRACKING.csv,BUG-TRACKING-TEMPLATE.csv) - Documentation templates (
BASELINE-METRICS.md,WEEKLY-PROGRESS-REPORT.md) - Master QA Prompt for autonomous execution
- README with complete quickstart guide
For autonomous execution (recommended): See references/master_qa_prompt.md - single copy-paste command for 100x speedup.
Core Capabilities
1. QA Project Initialization
Initialize complete QA infrastructure with all templates:
python scripts/init_qa_project.py <project-name> [output-directory]
Creates directory structure, tracking CSVs, documentation templates, and master prompt for autonomous execution.
Use when: Starting QA from scratch or migrating to structured QA process.
2. Test Case Writing
Write standardized, reproducible test cases following AAA pattern (Arrange-Act-Assert):
- Read template:
assets/templates/TEST-CASE-TEMPLATE.md - Follow structure: Prerequisites (Arrange) → Test Steps (Act) → Expected Results (Assert)
- Assign priority: P0 (blocker) → P4 (low)
- Include edge cases and potential bugs
Test case format: TC-[CATEGORY]-[NUMBER] (e.g., TC-CLI-001, TC-WEB-042, TC-SEC-007)
Reference: See references/google_testing_standards.md for complete AAA pattern guidelines and coverage thresholds.
3. Test Execution & Tracking
Ground Truth Principle (critical):
- Test case documents (e.g.,
02-CLI-TEST-CASES.md) = authoritative source for test steps - Tracking CSV = execution status only (do NOT trust CSV for test specifications)
- See
references/ground_truth_principle.mdfor preventing doc/CSV sync issues
Manual execution:
- Read test case from category document (e.g.,
02-CLI-TEST-CASES.md) ← always start here - Execute test steps exactly as documented
- Update
TEST-EXECUTION-TRACKING.csvimmediately after EACH test (never batch) - File bug in
BUG-TRACKING-TEMPLATE.csvif test fails
Autonomous execution (recommended):
- Copy master prompt from
references/master_qa_prompt.md - Paste to LLM session
- LLM auto-executes, auto-tracks, auto-files bugs, auto-generates reports
Innovation: 100x faster vs manual + zero human error in tracking + auto-resume capability.
4. Bug Reporting
File bugs with proper severity classification:
Required fields:
- Bug ID: Sequential (BUG-001, BUG-002, ...)
- Severity: P0 (24h fix) → P4 (optional)
- Steps to Reproduce: Numbered, specific
- Environment: OS, versions, configuration
Severity classification:
- P0 (Blocker): Security vulnerability, core functionality broken, data loss
- P1 (Critical): Major feature broken with workaround
- P2 (High): Minor feature issue, edge case
- P3 (Medium): Cosmetic issue
- P4 (Low): Documentation typo
Reference: See BUG-TRACKING-TEMPLATE.csv for complete template with examples.
5. Quality Metrics Calculation
Calculate comprehensive QA metrics and quality gates status:
python scripts/calculate_metrics.py <path/to/TEST-EXECUTION-TRACKING.csv>
Metrics dashboard includes:
- Test execution progress (X/Y tests, Z% complete)
- Pass rate (passed/executed %)
- Bug analysis (unique bugs, P0/P1/P2 breakdown)
- Quality gates status (✅/❌ for each gate)
Quality gates (all must pass for release):
| Gate | Target | Blocker |
|---|---|---|
| Test Execution | 100% | Yes |
| Pass Rate | ≥80% | Yes |
| P0 Bugs | 0 | Yes |
| P1 Bugs | ≤5 | Yes |
| Code Coverage | ≥80% | Yes |
| Security | 90% OWASP | Yes |
6. Progress Reporting
Generate QA reports for stakeholders:
Daily summary (end-of-day):
- Tests executed, pass rate, bugs filed
- Blockers (or None)
- Tomorrow's plan
Weekly report (every Friday):
- Use template:
WEEKLY-PROGRESS-REPORT.md(created by init script) - Compare against baseline:
BASELINE-METRICS.md - Assess quality gates and trends
Reference: See references/llm_prompts_library.md for 30+ ready-to-use reporting prompts.
7. Security Testing (OWASP)
Implement OWASP Top 10 security testing:
Coverage targets:
- A01: Broken Access Control - RLS bypass, privilege escalation
- A02: Cryptographic Failures - Token encryption, password hashing
- A03: Injection - SQL injection, XSS, command injection
- A04: Insecure Design - Rate limiting, anomaly detection
- A05: Security Misconfiguration - Verbose errors, default credentials
- A07: Authentication Failures - Session hijacking, CSRF
- Others: Data integrity, logging, SSRF
Target: 90% OWASP coverage (9/10 threats mitigated).
Each security test follows AAA pattern with specific attack vectors documented.
Day 1 Onboarding
For new QA engineers joining a project, complete 5-hour onboarding guide:
Read: references/day1_onboarding.md
Timeline:
- Hour 1: Environment setup (database, dev server, dependencies)
- Hour 2: Documentation review (test strategy, quality gates)
- Hour 3: Test data setup (users, CLI, DevTools)
- Hour 4: Execute first test case
- Hour 5: Team onboarding & Week 1 planning
Checkpoint: By end of Day 1, environment running, first test executed, ready for Week 1.
Autonomous Execution (⭐ Recommended)
Enable LLM-driven autonomous QA testing with single master prompt:
Read: references/master_qa_prompt.md
Features:
- Auto-resume from last completed test (reads tracking CSV)
- Auto-execute test cases (Week 1-5 progression)
- Auto-track results (updates CSV after each test)
- Auto-file bugs (creates bug reports for failures)
- Auto-generate reports (daily summaries, weekly reports)
- Auto-escalate P0 bugs (stops testing, notifies stakeholders)
Benefits:
- 100x faster execution vs manual
- Zero human error in tracking
- Consistent bug documentation
- Immediate progress visibility
Usage: Copy master prompt, paste to LLM, let it run autonomously for 5 weeks.
Adapting for Your Project
Small Project (50 tests)
- Timeline: 2 weeks
- Categories: 2-3 (e.g., Frontend, Backend)
- Daily: 5-7 tests
- Reports: Daily summary only
Medium Project (200 tests)
- Timeline: 4 weeks
- Categories: 4-5 (CLI, Web, API, DB, Security)
- Daily: 10-12 tests
- Reports: Daily + weekly
Large Project (500+ tests)
- Timeline: 8-10 weeks
- Categories: 6-8 (multiple components)
- Daily: 10-15 tests
- Reports: Daily + weekly + bi-weekly stakeholder
Reference Documents
Access detailed guidelines from bundled references:
references/day1_onboarding.md- 5-hour onboarding guide for new QA engineersreferences/master_qa_prompt.md- Single command for autonomous LLM execution (100x speedup)references/llm_prompts_library.md- 30+ ready-to-use prompts for specific QA tasksreferences/google_testing_standards.md- AAA pattern, coverage thresholds, fail-fast validationreferences/ground_truth_principle.md- Preventing doc/CSV sync issues (critical for test suite integrity)
Assets & Templates
Test case templates and bug report formats:
assets/templates/TEST-CASE-TEMPLATE.md- Complete template with CLI and security examples
Scripts
Automation scripts for QA infrastructure:
scripts/init_qa_project.py- Initialize QA infrastructure (one command setup)scripts/calculate_metrics.py- Generate quality metrics dashboard
Common Patterns
Pattern 1: Starting Fresh QA
1. python scripts/init_qa_project.py my-app ./
2. Fill in BASELINE-METRICS.md (document current state)
3. Write test cases using assets/templates/TEST-CASE-TEMPLATE.md
4. Copy master prompt from references/master_qa_prompt.md
5. Paste to LLM → autonomous execution begins
Pattern 2: LLM-Driven Testing (Autonomous)
1. Read references/master_qa_prompt.md
2. Copy the single master prompt (one paragraph)
3. Paste to LLM conversation
4. LLM executes all 342 test cases over 5 weeks
5. LLM updates tracking CSVs automatically
6. LLM generates weekly reports automatically
Pattern 3: Adding Security Testing
1. Read references/google_testing_standards.md (OWASP section)
2. Write TC-SEC-XXX test cases for each OWASP threat
3. Target 90% coverage (9/10 threats)
4. Document mitigations in test cases
Pattern 4: Third-Party QA Handoff
1. Ensure all templates populated
2. Verify BASELINE-METRICS.md complete
3. Package tests/docs/ folder
4. Include references/master_qa_prompt.md for autonomous execution
5. QA team can start immediately (Day 1 onboarding → 5 weeks testing)
Success Criteria
This skill is effective when:
- ✅ Test cases are reproducible by any engineer
- ✅ Quality gates objectively measured
- ✅ Bugs fully documented with repro steps
- ✅ Progress visible in real-time (CSV tracking)
- ✅ Autonomous execution enabled (LLM can execute full plan)
- ✅ Third-party QA teams can start testing immediately
How to use qa-expert on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add qa-expert
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches qa-expert from GitHub repository daymade/claude-code-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate qa-expert. Access the skill through slash commands (e.g., /qa-expert) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★27 reviews- ★★★★★Evelyn Verma· Dec 8, 2024
Useful defaults in qa-expert — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Nikhil Shah· Dec 8, 2024
We added qa-expert from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Ren Gill· Nov 27, 2024
qa-expert is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Camila Malhotra· Nov 27, 2024
qa-expert fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Rahul Santra· Nov 11, 2024
Solid pick for teams standardizing on skills: qa-expert is focused, and the summary matches what you get after install.
- ★★★★★Michael Srinivasan· Oct 18, 2024
qa-expert reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Pratham Ware· Oct 2, 2024
I recommend qa-expert for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Emma Huang· Sep 25, 2024
Useful defaults in qa-expert — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Sakshi Patil· Sep 5, 2024
qa-expert fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Fatima Harris· Sep 1, 2024
I recommend qa-expert for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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