qa-expert
daymade/claude-code-skills
Establish world-class QA testing processes with Google Testing Standards, OWASP security testing, and autonomous LLM execution.
What is qa-expert?
QA Expert sets up comprehensive QA infrastructure including test case writing, execution tracking, bug classification, quality metrics, and security testing. Use it when establishing QA processes, writing standardized test cases, executing test plans, or implementing security testing for any software project.
- Initialize complete QA project structure with templates, tracking CSVs, and documentation
- Write standardized test cases following AAA pattern (Arrange-Act-Assert) with P0-P4 severity classification
- Execute and track test plans with ground-truth principle to prevent documentation/CSV sync issues
- File bugs with proper severity levels (P0 blocker through P4 low priority)
- Calculate quality metrics and enforce release gates (test execution, pass rate, P0/P1 bugs, code coverage, OWASP security)
- Generate daily summaries and weekly progress reports against baseline metrics
How to install qa-expert
npx skills add https://github.com/daymade/claude-code-skills --skill qa-expert- Python 3.7+ for initialization and metrics calculation scripts
- Access to project codebase and test environment
- Database and dev server setup for test data
How to use qa-expert
- 1.Run initialization: python scripts/init_qa_project.py <project-name> to create directory structure, templates, and tracking CSVs
- 2.Read test case template from assets/templates/TEST-CASE-TEMPLATE.md and write test cases following AAA pattern
- 3.Execute tests by reading from category documents (e.g., 02-CLI-TEST-CASES.md) and updating TEST-EXECUTION-TRACKING.csv immediately after each test
- 4.File bugs in BUG-TRACKING-TEMPLATE.csv with required fields: Bug ID, Severity (P0-P4), Steps to Reproduce, Environment
- 5.Calculate metrics using: python scripts/calculate_metrics.py <path/to/TEST-EXECUTION-TRACKING.csv>
- 6.Generate daily/weekly reports using templates WEEKLY-PROGRESS-REPORT.md and reference prompts from llm_prompts_library.md
- 7.For autonomous execution: copy master prompt from references/master_qa_prompt.md and paste to LLM session
Use cases
- Setting up QA infrastructure for a new project from scratch
- Writing and executing comprehensive test plans across multiple categories (CLI, Web, API, Database, Security)
- Implementing security testing against OWASP Top 10 vulnerabilities
- Tracking bugs with proper severity classification and generating stakeholder reports
- Onboarding new QA engineers with structured 5-hour Day 1 guide
- QA engineers and test automation specialists
- Engineering teams establishing QA processes
- Project leads implementing quality gates before release
- Security-focused teams testing OWASP compliance
- Development teams migrating to structured QA methodology
qa-expert FAQ
Manual execution requires human testers to read test cases and update tracking CSVs after each test. Autonomous execution uses a master LLM prompt that auto-executes tests, auto-tracks results, auto-files bugs, and auto-generates reports—delivering 100x faster execution with zero human tracking error.
Follow the Ground Truth Principle: test case documents (e.g., 02-CLI-TEST-CASES.md) are the authoritative source for test specifications, while the tracking CSV records only execution status. Always read test steps from the category document, never from the CSV.
All five gates must pass: 100% test execution, ≥80% pass rate, 0 P0 bugs, ≤5 P1 bugs, ≥80% code coverage, and 90% OWASP security coverage.
The Day 1 onboarding guide takes 5 hours: 1 hour environment setup, 1 hour documentation review, 1 hour test data setup, 1 hour executing first test, 1 hour team planning. By end of Day 1, the engineer is ready for Week 1 testing.
Yes. For small projects, use a 2-week timeline with 2-3 test categories (e.g., Frontend, Backend), execute 5-7 tests daily, and generate daily summaries only (skip weekly reports).
Full instructions (SKILL.md)
Source of truth, from daymade/claude-code-skills.
name: qa-expert description: >- Sets up QA testing processes: test strategies, Google Testing Standards test cases, P0-P4 bug tracking, quality metrics, progress reports, autonomous execution via master prompts, and OWASP security testing, with third-party QA handoff docs. Use when establishing QA infrastructure, writing test cases, executing test plans, or tracking bugs. Not for auditing an already-rendered UI (use frontend-visual-qa).
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
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