### Qa Plan
Works with
description: "Generate a QA test plan for a sprint or feature. Reads GDDs and story files, classifies stories by test type (Logic/Integration/Visual/UI), and produces a structured test plan covering a
argument-hint: "[sprint | feature: system-name | story: path]"
allowed-tools: Read, Glob, Grep, Write, AskUserQuestion
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionqa-planExecute the skills CLI command in your project's root directory to begin installation:
Fetches qa-plan from Donchitos/Claude-Code-Game-Studios 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-plan. Access via /qa-plan 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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| name | qa-plan |
| description | "Generate a QA test plan for a sprint or feature. Reads GDDs and story files, classifies stories by test type (Logic/Integration/Visual/UI), and produces a structured test plan covering automated tests required, manual test cases, smoke test scope, and playtest sign-off requirements. Run before sprint begins or when starting a major feature." |
| argument-hint | "[sprint | feature: system-name | story: path]" |
| user-invocable | true |
| allowed-tools | Read, Glob, Grep, Write, AskUserQuestion |
| agent | qa-lead |
This skill generates a structured QA plan for a sprint, feature, or individual story. It reads all in-scope story files and their referenced GDDs, classifies each story by test type, and produces a plan that tells developers exactly what to automate, what to verify manually, what the smoke test scope is, and when to bring in a playtester.
Run this before a sprint begins so the team knows upfront what testing work is required. A test plan written after implementation is a post-mortem, not a plan.
Output: production/qa/qa-plan-[sprint-slug]-[date].md
Argument: $ARGUMENTS (blank = ask user via AskUserQuestion)
Determine scope from the argument:
sprint — read the most recent file in production/sprints/, extract
every story file path referenced. If production/sprint-status.yaml exists,
use it as the primary story list and fall back to the sprint plan for story
metadata.feature: [system-name] — glob production/epics/*/story-*.md, filter
to stories whose file path or title contains the system name. Also check the
epic index file (EPIC.md) in that system's directory.story: [path] — validate that the path exists and load that single file.AskUserQuestion:
After resolving scope, report: "Building QA plan for [N] stories in [scope]."
If a story file path is referenced but the file does not exist, note it as MISSING and continue with the remaining stories. Do not fail the entire plan for one missing file.
For each in-scope story file, read the full file and extract:
Type: Logic)After reading stories, load supporting context once (not per story):
design/gdd/systems-index.md — to understand system priorities and which
GDDs are approveddocs/architecture/control-manifest.md — scan for forbidden patterns that
automated tests should guard against (if the file exists)If no GDD is referenced in a story, note it as a gap but do not block the plan. The story will be classified using acceptance criteria alone.
For each story, assign a Story Type. If the story already has a Type: field
in its header, use that value and validate it against the criteria below. If the
field is missing or ambiguous, infer the type from the acceptance criteria.
| Story Type | Classification Indicators |
|---|---|
| Logic | Acceptance criteria reference calculations, formulas, numerical thresholds, state transitions, AI decisions, data validation, buff/debuff stacking, economy transactions, or any testable computation |
| Integration | Criteria involve two or more systems interacting, signals or events propagating across system boundaries, save/load round-trips, network sync, or persistence |
| Visual/Feel | Criteria reference animation behaviour, VFX, shader output, "feels responsive", perceived timing, screen shake, particle effects, audio sync, or visual feedback quality |
| UI | Criteria reference menus, HUD elements, buttons, screens, dialogue boxes, inventory panels, tooltips, or any player-facing interface element |
| Config/Data | Changes are limited to balance tuning values, data files, or configuration — no new code logic is involved |
Mixed stories (e.g., a story that adds both a formula and a UI display): assign the primary type based on which acceptance criteria carry the highest implementation risk, and note the secondary type. Mixed Logic+Integration or Visual+UI combinations are the most common.
After classifying all stories, produce a classification summary table in conversation before proceeding to Phase 4. This gives the user visibility into how tests will be allocated.
Assemble the full QA plan document. Use this structure:
# QA Plan: [Sprint/Feature Name]
**Date**: [date]
**Generated by**: /qa-plan
**Scope**: [N stories across [N systems]]
**Engine**: [engine name from .claude/docs/technical-preferences.md, or "Not configured"]
**Sprint File**: [path to sprint plan if applicable]
---
## Test Summary
| Story | Type | Automated Test Required | Manual Verification Required |
|-------|------|------------------------|------------------------------|
| [story title] | Logic | Unit test — `tests/unit/[system]/` | None |
| [story title] | Integration | Integration test — `tests/integration/[system]/` | Smoke check |
| [story title] | Visual/Feel | None (not automatable) | Screenshot + lead sign-off |
| [story title] | UI | Interaction walkthrough | Manual step-through |
| [story title] | Config/Data | Data validation test | Spot-check in-game values |
---
## Automated Tests Required
### [Story Title] — [Type]
**Test file path**: `tests/[unit|integration]/[system]/[story-slug]_test.[ext]`
**What to test**:
- [Specific formula or rule from the GDD Formulas section]
- [Each named state transition or decision branch]
- [Each side effect that should or should not occur]
**Edge cases to cover**:
- Zero/minimum input values (e.g., 0 damage, empty inventory)
- Maximum/boundary input values (e.g., max level, stat cap)
- Invalid or null input (e.g., missing target, dead entity)
- [Any edge case explicitly called out in the GDD Edge Cases section]
**Estimated test count**: ~[N] unit tests
[If no GDD formula reference was found for this story, note:]
*No formula found in referenced GDD — test cases must be derived from acceptance
criteria directly. Review the GDD Formulas section before writing tests.*
---
## Manual QA Checklist
### [Story Title] — [Type]
**Verification method**: [Screenshot + designer sign-off | Playtest session |
Manual step-through | Comparison against reference footage]
**Who must sign off**: [designer / lead-programmer / qa-lead / art-lead]
**Evidence to capture**: [screenshot of X | video clip of Y | written playtest
notes | side-by-side comparison]
Checklist:
- [ ] [Specific observable condition — concrete and falsifiable]
- [ ] [Another condition]
- [ ] [Every acceptance criterion translated into a manual check item]
*If any criterion uses subjective language ("feels", "looks", "seems"), it must
be supplemented with a specific benchmark or a playtest protocol note.*
---
## Smoke Test Scope
Critical paths to verify before any QA hand-off for this sprint:
1. Game launches to main menu without crash
2. New game / new session can be started
3. [Primary mechanic introduced or changed this sprint]
4. [Any system with a regression risk from this sprint's changes]
5. Save / load cycle completes without data loss (if save system exists)
6. Performance is within budget on target hardware (no new frame spikes)
*Smoke tests are verified by the developer via `/smoke-check`. Reference this
list when running that skill.*
---
## Playtest Requirements
| Story | Playtest Goal | Min Sessions | Target Player Type |
|-------|--------------|--------------|-------------------|
| [story] | [What question must the session answer?] | [N] | [new player / experienced] |
**Sign-off requirement**: Playtest notes must be written to
`production/session-logs/playtest-[sprint]-[story-slug].md` and reviewed by
the [designer / qa-lead] before the story can be marked COMPLETE.
If no stories require playtest validation: *No playtest sessions required for
this sprint.*
---
## Definition of Done — This Sprint
A story is DONE when ALL of the following are true:
- [ ] All acceptance criteria verified — via automated test result OR documented
manual evidence (screenshot, video, or playtest notes with sign-off)
- [ ] Test file exists at the specified path for all Logic and Integration stories
- [ ] Manual evidence document exists for all Visual/Feel and UI stories
- [ ] Smoke check passes (run `/smoke-check sprint` before QA hand-off)
- [ ] No regressions introduced
- [ ] Code reviewed (via `/code-review` or documented peer review)
- [ ] Story file updated to `Status: Complete` (via `/story-done`)
When generating content, use the actual story titles, GDD formula text, and acceptance criteria extracted in Phase 2. Do not use placeholder text — every test entry should reflect the real requirements of these specific stories.
Show the complete plan in conversation (or a summary if the plan is very long), then ask:
"May I write this QA plan to production/qa/qa-plan-[sprint-slug]-[date].md?"
Write the plan exactly as generated — do not truncate.
After writing:
"QA plan written to production/qa/qa-plan-[sprint-slug]-[date].md.
Next steps:
/smoke-check sprint after all stories are implemented to gate QA hand-off/story-done checks for them"AskUserQuestion for scope selection when no argument is provided.
Keep all other phases non-interactive — present findings, then ask once to
approve the write.Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
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✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
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Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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Useful defaults in qa-plan — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
qa-plan has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: qa-plan is focused, and the summary matches what you get after install.
qa-plan is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend qa-plan for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
qa-plan reduced setup friction for our internal harness; good balance of opinion and flexibility.
Keeps context tight: qa-plan is the kind of skill you can hand to a new teammate without a long onboarding doc.
Useful defaults in qa-plan — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Solid pick for teams standardizing on skills: qa-plan is focused, and the summary matches what you get after install.
We added qa-plan from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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