Establish world-class QA testing processes for any software project using proven methodologies from Google Testing Standards and OWASP security best practices.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionqa-expertExecute the skills CLI command in your project's root directory to begin installation:
Fetches qa-expert from daymade/claude-code-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate qa-expert. Access via /qa-expert in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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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.
Trigger this skill when:
One-command initialization:
python scripts/init_qa_project.py <project-name> [output-directory]
What gets created:
tests/docs/, tests/e2e/, tests/fixtures/)TEST-EXECUTION-TRACKING.csv, BUG-TRACKING-TEMPLATE.csv)BASELINE-METRICS.md, WEEKLY-PROGRESS-REPORT.md)For autonomous execution (recommended): See references/master_qa_prompt.md - single copy-paste command for 100x speedup.
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.
Write standardized, reproducible test cases following AAA pattern (Arrange-Act-Assert):
assets/templates/TEST-CASE-TEMPLATE.mdTest 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.
Ground Truth Principle (critical):
02-CLI-TEST-CASES.md) = authoritative source for test stepsreferences/ground_truth_principle.md for preventing doc/CSV sync issuesManual execution:
02-CLI-TEST-CASES.md) ← always start hereTEST-EXECUTION-TRACKING.csv immediately after EACH test (never batch)BUG-TRACKING-TEMPLATE.csv if test failsAutonomous execution (recommended):
references/master_qa_prompt.mdInnovation: 100x faster vs manual + zero human error in tracking + auto-resume capability.
File bugs with proper severity classification:
Required fields:
Severity classification:
Reference: See BUG-TRACKING-TEMPLATE.csv for complete template with examples.
Calculate comprehensive QA metrics and quality gates status:
python scripts/calculate_metrics.py <path/to/TEST-EXECUTION-TRACKING.csv>
Metrics dashboard includes:
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 |
Generate QA reports for stakeholders:
Daily summary (end-of-day):
Weekly report (every Friday):
WEEKLY-PROGRESS-REPORT.md (created by init script)BASELINE-METRICS.mdReference: See references/llm_prompts_library.md for 30+ ready-to-use reporting prompts.
Implement OWASP Top 10 security testing:
Coverage targets:
Target: 90% OWASP coverage (9/10 threats mitigated).
Each security test follows AAA pattern with specific attack vectors documented.
For new QA engineers joining a project, complete 5-hour onboarding guide:
Read: references/day1_onboarding.md
Timeline:
Checkpoint: By end of Day 1, environment running, first test executed, ready for Week 1.
Enable LLM-driven autonomous QA testing with single master prompt:
Read: references/master_qa_prompt.md
Features:
Benefits:
Usage: Copy master prompt, paste to LLM, let it run autonomously for 5 weeks.
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)Test case templates and bug report formats:
assets/templates/TEST-CASE-TEMPLATE.md - Complete template with CLI and security examplesAutomation scripts for QA infrastructure:
scripts/init_qa_project.py - Initialize QA infrastructure (one command setup)scripts/calculate_metrics.py - Generate quality metrics dashboard1. 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
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
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
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)
This skill is effective when:
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
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Useful defaults in qa-expert — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
We added qa-expert from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
qa-expert is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
qa-expert fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: qa-expert is focused, and the summary matches what you get after install.
qa-expert reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend qa-expert for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in qa-expert — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
qa-expert fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend qa-expert for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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