autonomous-qa-orchestration
Skill1 starsChanged 4 months agoMITView on GitHub
Use when asked to autonomously test an application end-to-end with minimal hand-holding — discover, explore, plan, execute, investigate, report, and verify fixes as one continuous QA pass. The top-level playbook that pulls in the other testing skills as needed.
1 other project uses a version of this, 1 of them edited.
Adopted by
Most recently taken up by
- mejbaurbahar/software-tester-skillsedited, first seen Oct 13, 2026
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 1
- mejbaurbahar/software-tester-skills★ 111% same
Wrote their own version · 0
None found.
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository24 other files it gives its agents.
- accessibility-testingSkill
- ai-ml-testingSkill
- api-testingSkill
- bug-reportingSkill
- chaos-resilience-testingSkill
- chatbot-voice-agent-testingSkill
- cicd-testingSkill
- cloud-infrastructure-testingSkill
- contract-testingSkill
- database-testingSkill
- fagunSkill
- functional-testingSkill
- llm-testingSkill
- manual-testingSkill
- mobile-testingSkill
- observability-testingSkill
- performance-testingSkill
- qa-code-intelligenceSkill
- qa-fundamentalsSkill
- regression-testingSkill
- security-testingSkill
- test-automationSkill
- test-data-engineeringSkill
- visual-testingSkill
The file
We split this skill into the rules it gives an agent
3 rules. About 0 are common, 2 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 23
Run performance checks: if the task involves load-sensitive paths — see [[performance-testing]].
- Very specific: line 26
Create/update tickets: if the harness has ticket integration available (Jira/GitHub — check for jira/github skill availability) rather than …
- Distinctive: line 27
Re-run failed tests / verify fixes: when asked to confirm a fix — re-execute the exact original repro, don't just spot-check something …
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Autonomous QA Orchestration
This is the playbook for an open-ended "go test this" request. It sequences the other skills in this library rather than duplicating their content — pull each one in when you reach that stage.
The pass
- Discover the application — what is it, what stack, what environment (staging/prod/local), what's already known about it (existing memory/reports,
~/qa-agent/reports/, prior findings). - Understand architecture — frontend/backend split, API shape, DB, third-party integrations, auth model. Skim rather than exhaustively read — enough to know where risk concentrates.
- Understand requirements/scope — what changed recently (git log/diff, linked ticket/PRD) if this is feature-specific; if it's a general audit, use risk-based prioritization from [[qa-fundamentals]] to decide where to spend time.
- Explore the UI — landmark tour per [[manual-testing]]'s exploratory testing method; use Claude in Chrome / Chrome DevTools MCP for live interaction.
- Explore the API — capture real requests via network inspection, replay/probe per [[api-testing]].
- Generate a test plan — scoped to what was actually found in steps 1-5, using [[qa-fundamentals]]'s test plan structure; don't run a generic checklist disconnected from the actual app.
- Generate test cases — via [[manual-testing]]'s design techniques and, where a schema/spec exists, [[qa-code-intelligence]]'s automatic generation approach.
- Execute — manual where exploratory, automated where repeatable (promote to [[test-automation]] if the flow is worth keeping).
- Discover & reproduce bugs — minimal reliable repro before filing, per [[qa-code-intelligence]].
- Collect evidence — screenshots, console logs, network requests/responses, relevant DB state.
- Investigate backend/logs/traces — walk the observability chain per [[observability-testing]] and [[qa-code-intelligence]] to find actual root cause, not just symptom.
- Run security checks on anything touching auth, user input, or data boundaries — at minimum the OWASP quick-pass in [[security-testing]], not a full pentest unless asked.
- Run performance checks if the task involves load-sensitive paths — see [[performance-testing]].
- Compare against baseline / detect regressions — per [[regression-testing]], especially if this follows a recent deploy.
- Generate the report — one structured report per [[bug-reporting]], not a stream-of-consciousness log; group related findings, state severity with evidence.
- Create/update tickets if the harness has ticket integration available (Jira/GitHub — check for
jira/githubskill availability) rather than leaving findings only in chat. - Re-run failed tests / verify fixes when asked to confirm a fix — re-execute the exact original repro, don't just spot-check something adjacent.
- Maintain the regression suite — promote newly-found, high-value bugs into permanent regression tests so they can't silently reappear, per [[regression-testing]] and [[qa-code-intelligence]].
Operating discipline
- Never declare "passed" without verification — the standard from [[bug-reporting]] applies throughout: check actual HTTP status/DOM state/DB state, not appearances.
- Time-box exploration — an open-ended "test this" request can expand indefinitely; scope to a session/charter (per [[manual-testing]]) and report what was and wasn't covered, rather than silently doing a partial job and calling it complete.
- State assumptions explicitly — when requirements are ambiguous or missing, say what assumption was tested against rather than silently guessing.
- Escalate genuine blockers — missing credentials, an environment that's down, a destructive action needed that wasn't authorized — surface these rather than working around them silently or giving up silently.
- Respect safety boundaries from every underlying skill: no unauthorized security testing ([[security-testing]]), no unscoped chaos experiments ([[chaos-resilience-testing]]), no raw production PII in test data ([[test-data-engineering]]).
This harness's specific tooling
- Environment:
~/qa-agent/.venv/bin/python(pytest, playwright, requests, locust);qaCLI dispatcher (qa test,qa smoke,qa regression,qa security,qa report); Chrome DevTools MCP on port 9222; reports to~/qa-agent/reports/; audit log to~/qa-agent/memory/audit.log. - Both Claude CLI and Antigravity CLI read from this shared skill knowledge — Antigravity's
qa_command_centerrouter (~/qa-agent/skills/qa_command_center/SKILL.json) lists all sub-skills and should be kept in sync when new skills are added here.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

