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Qa plan

Skill IdoCohen560/claude-unity-game-studio/game-studios-template/.claude/skills/qa-plan

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npx -y skills add IdoCohen560/claude-unity-game-studio --skill qa-plan

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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.

SKILL.md

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QA Plan

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


Phase 1: Parse Scope

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.
  • No argument — use AskUserQuestion:
    • "What is the scope for this QA plan?"
    • Options: "Current sprint", "Specific feature (enter system name)", "Specific story (enter path)", "Full epic"

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.


Phase 2: Load Inputs

For each in-scope story file, read the full file and extract:

  • Story title and story ID (from filename or header)
  • Story Type field (if present in the file header — e.g., Type: Logic)
  • Acceptance criteria — the complete numbered/bulleted list
  • Implementation files — listed under "Files to Create / Modify" or similar
  • Engine notes — any engine API warnings or version-specific notes
  • GDD reference — the GDD path(s) cited
  • ADR reference — the ADR(s) cited
  • Estimate — hours or story points if present
  • Dependencies — other stories this one depends on

After reading stories, load supporting context once (not per story):

  • design/gdd/systems-index.md — to understand system priorities and which GDDs are approved
  • For each unique GDD referenced across all stories: read only the Acceptance Criteria and Formulas sections. Do not load full GDD text — these two sections contain the testable requirements and the math to verify.
  • docs/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.


Phase 3: Classify Each Story

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 TypeClassification Indicators
LogicAcceptance criteria reference calculations, formulas, numerical thresholds, state transitions, AI decisions, data validation, buff/debuff stacking, economy transactions, or any testable computation
IntegrationCriteria involve two or more systems interacting, signals or events propagating across system boundaries, save/load round-trips, network sync, or persistence
Visual/FeelCriteria reference animation behaviour, VFX, shader output, "feels responsive", perceived timing, screen shake, particle effects, audio sync, or visual feedback quality
UICriteria reference menus, HUD elements, buttons, screens, dialogue boxes, inventory panels, tooltips, or any player-facing interface element
Config/DataChanges 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.


Phase 4: Generate Test Plan

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.


Phase 5: Write Output

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:

  • Share this plan with the team before sprint implementation begins
  • Run /smoke-check sprint after all stories are implemented to gate QA hand-off
  • For Logic/Integration stories, create the test files at the listed paths before marking stories done — /story-done checks for them"

Collaborative Protocol

  • Never write the plan without asking — Phase 5 requires explicit approval.
  • Classify conservatively: when a story is ambiguous between Logic and Integration, classify it as Integration — it requires both unit and integration tests.
  • Do not invent test cases beyond what acceptance criteria and GDD formulas support. If a formula is absent from the GDD, flag it rather than guessing.
  • Playtest requirements are advisory: the user decides whether a playtest is warranted for borderline Visual/Feel stories. Flag the case; do not mandate.
  • Use AskUserQuestion for scope selection when no argument is provided. Keep all other phases non-interactive — present findings, then ask once to approve the write.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most plan spec skills give in ~2.4k tokens

Counted across 1,099 of the 1,860 authors here whose files we hold, read 2026-08-07

  • Ask one question at a timein 51 of 1099
  • Break plans into vertical slicesin 29 of 1099, across 11 files
  • Publish issues in dependency orderin 27 of 1099, across 9 files
  • Iterate until user approves the breakdownin 25 of 1099, across 7 files
  • Explore the repository to understand the codebase statein 24 of 1099, across 7 files
  • Use domain glossary vocabularyin 23 of 1099, across 5 files
  • Apply correct triage labels to published issuesin 23 of 1099, across 5 files
  • Prefer AFK slices over HITLin 22 of 1099, across 7 files
  • Write a specification before writing any codein 22 of 1099, across 14 files
  • Write failing tests before implementation codein 22 of 1099, across 20 files
  • Ask clarifying questions until requirements are concretein 21 of 1099, across 13 files
  • Respect existing architecture decision recordsin 20 of 1099, across 5 files

Said here and by no other author read

  • Run before a sprint begins
  • Resolve the scope from arguments or user input
  • Note missing story files and continue processing
  • Extract story details and supporting context
  • Assign a story type to each story
  • Produce a classification summary table

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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