manual-testing
Skill1 starsChanged 4 months agoMITView on GitHub
Use when designing test cases, doing exploratory testing, writing test plans/checklists, or manually verifying a feature before or instead of automating it. Covers test case design techniques, exploratory charters, and test plan structure.
8 other projects use a version of this, 1 of them edited.
Adopted by
Most recently taken up by
- snuffish/dotfilestheir own version, first seen Nov 1, 2026
- mejbaurbahar/software-tester-skillsedited, first seen Oct 13, 2026
- DataDog/browser-sdktheir own version, first seen Oct 8, 2026
- Relaticle/relaticletheir own version, first seen Oct 8, 2026
- relaticle/relaticletheir own version, first seen Oct 8, 2026
- mgifford/accessibility-skillstheir own version, first seen Jul 27, 2026
Other versions8 projects use a version of this.
Word for word · 0
None found.
Edited · 1
- mejbaurbahar/software-tester-skills★ 196% same
Wrote their own version · 7
- Relaticle/relaticle★ 1.8ksame name
- relaticle/relaticle★ 1.8ksame name
- DataDog/browser-sdk★ 400same name
- mgifford/accessibility-skills★ 48same name
- bmad-labs/skills★ 19same name
- miovrag/claude-skills★ 1same name
- snuffish/dotfiles★ 0same name
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
- autonomous-qa-orchestrationSkill
- bug-reportingSkill
- chaos-resilience-testingSkill
- chatbot-voice-agent-testingSkill
- cicd-testingSkill
- cloud-infrastructure-testingSkill
- contract-testingSkill
- database-testingSkill
- fagunSkill
- functional-testingSkill
- llm-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
9 rules. 0 are common, 7 are distinctive, and 2 are very specific to this project.
0
Common
Most of the 8 edited versions we read keep these.
7
Distinctive
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 9
Before automation exists, or when automation isn't worth the cost (one-off, highly visual, exploratory).
- Distinctive: line 51
Test with a freshly created account and an old account with legacy data — schema drift bugs hide here.
- Distinctive: line 52
Test empty states (zero items, zero results) not just populated ones.
- Distinctive: line 53
Test permissions boundaries: logged out, wrong role, expired session, another tenant's data.
- Distinctive: line 54
Test concurrency: two tabs, two users editing the same record, double-submit on slow network.
- Very specific: line 55
Test input extremes: very long strings, unicode/emoji/RTL text, negative numbers, decimals where integers expected, huge file uploads.
- Distinctive: line 56
Test browser back/forward and refresh mid-flow.
- Distinctive: line 57
Always verify against the actual backend state (DB row, API response), not just what the UI shows — the UI can lie.
- Very specific: line 60
When a manual test finds a defect, use the [[bug-reporting]] skill's template so severity/repro/evidence are consistent across the QA …
“Common” is measured: kept by at least 6 in 10 of the 8 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Manual Testing
When to reach for this
- Before automation exists, or when automation isn't worth the cost (one-off, highly visual, exploratory).
- New feature needs a test plan.
- Investigating a reported bug and need a repeatable, structured approach.
Test case design techniques
Pick techniques based on the input space, don't just guess values:
- Equivalence partitioning — split inputs into classes that should behave the same; test one representative per class.
- Boundary value analysis — test min, min-1, min+1, max, max-1, max+1, and just-outside-range values. Most bugs live at boundaries.
- Decision table testing — for features with multiple independent conditions (e.g. role × subscription tier × feature flag), build a table of condition combinations and expected outcomes.
- State transition testing — for anything with a lifecycle (order status, account state), test valid transitions, invalid transitions, and transitions from every state.
- Pairwise/combinatorial testing — when full combinatorial coverage is too large, use pairwise coverage to catch most interaction bugs cheaply.
- Error guessing — based on experience: empty strings, nulls, huge inputs, unicode/emoji, SQL-special chars, double-submits, back-button, concurrent edits.
Exploratory testing (session-based)
- Charter — one sentence: what area, what risk you're hunting for. e.g. "Explore checkout discount code entry for validation and edge-case bugs, 30 min."
- Time-box — 30–90 min sessions. Don't sprawl.
- Tour styles to vary coverage:
- Landmark tour — visit every major screen/feature once.
- FedEx tour — follow one piece of data end-to-end through the system.
- Saboteur tour — try to break things: kill network mid-action, resize window, spam-click, go back/forward.
- Money tour — focus on whatever the business cares about most (checkout, signup, payment).
- Log as you go: what you did, what you noticed, questions raised, bugs found — even things that "seem fine but weird."
- Debrief: summarize coverage, bugs, and follow-up charters needed.
Test plan structure
# Test Plan: <Feature/Release>
## Scope
- In scope / out of scope
## Test approach
- Manual / automated split, environments, browsers/devices
## Entry criteria
- Feature deployed to staging, test data seeded, etc.
## Test cases
- ID | Title | Preconditions | Steps | Expected | Priority
## Risk areas
- What's most likely to break, and why
## Exit criteria
- % pass rate, zero blockers/criticals, sign-off ownerChecklist habits that catch real bugs
- Test with a freshly created account and an old account with legacy data — schema drift bugs hide here.
- Test empty states (zero items, zero results) not just populated ones.
- Test permissions boundaries: logged out, wrong role, expired session, another tenant's data.
- Test concurrency: two tabs, two users editing the same record, double-submit on slow network.
- Test input extremes: very long strings, unicode/emoji/RTL text, negative numbers, decimals where integers expected, huge file uploads.
- Test browser back/forward and refresh mid-flow.
- Always verify against the actual backend state (DB row, API response), not just what the UI shows — the UI can lie.
Reporting
When a manual test finds a defect, use the [[bug-reporting]] skill's template so severity/repro/evidence are consistent across the QA harness (~/qa-agent/QA_RULES.md is the canonical format 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.

