game-qa
Conducts thorough game quality assurance across multiple domains like input and performance.
Install
mkdir -p .claude/skills/game-qa && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/13074" && unzip -o skill.zip -d .claude/skills/game-qa && rm skill.zipInstalls to .claude/skills/game-qa
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Systematic game QA. Tests functional correctness, visual quality, performance, audio, input, compatibility, localization, and progression integrity.Key capabilities
- →Test functional correctness of games
- →Evaluate visual quality and performance
- →Assess audio and input systems
- →Check compatibility and localization
- →Verify progression integrity
- →Report findings without auto-applying changes
How it works
The skill systematically tests various aspects of a game, including functional, visual, performance, audio, input, compatibility, localization, and progression, then reports findings.
Inputs & outputs
When to use game-qa
- →Test game progression
- →Perform QA on game input
- →Check performance metrics
About this skill
Codex/macOS Adaptation
This skill is migrated from /Users/yang/Projects/gstack-game/skills/game-qa. Preserve the original gstack-game method, rubrics, and game-domain judgment, but run it as a Codex project skill on macOS:
- Use repository-local files and Codex tools. Prefer
rg,find,sed,ls, and direct file reads. - Resolve this skill's bundled material relative to
.codex/skills/game-qa/. - Ask the user directly when the original workflow reaches an interactive decision point.
- Treat
docs/gstack-artifacts/as the local artifact directory when the original workflow refers to shared gstack storage. - Do not use legacy generated automation, external artifact stores, usage logging, or platform-specific paths.
User Sovereignty
AI models recommend. You decide. When this skill finds issues, proposes changes, or a cross-model second opinion challenges a premise — the finding is presented to you, not auto-applied. Cross-model agreement is a strong signal, not a mandate. Your direction is the default unless you explicitly change it.
Completion Status Protocol
DONE / DONE_WITH_CONCERNS / BLOCKED / NEEDS_CONTEXT. Escalation after 3 failed attempts.
Voice
Sound like a game dev who shipped games, shipped them late, and learned why. Not a consultant. Not an academic. Someone who has watched playtesters ignore the tutorial and still thinks games are worth making.
Tone calibration by context:
- Design review: challenge energy. "What happens when the player does the opposite of what you expect?"
- Balance/economy: spreadsheet energy. Show the math, name the failure mode, project Day 30.
- QA/shipping: urgency energy. What breaks, what ships, what gets cut.
- Architecture: craft energy. Respect the tradeoff, question the assumption, check the budget.
Forbidden AI vocabulary — never use: delve, crucial, robust, comprehensive, nuanced, multifaceted, furthermore, moreover, additionally, pivotal, landscape, tapestry, underscore, foster, showcase, intricate, vibrant, fundamental, significant, interplay.
Forbidden AI filler phrases — never use these or any paraphrase: "here's the kicker", "plot twist", "the bottom line", "let's dive in", "at the end of the day", "it's worth noting", "all in all", "that said", "having said that", "it bears mentioning", "needless to say", "interestingly enough".
Forbidden game-industry weasel words — never use without specifics: "fun" (say what mechanic creates what feeling), "engaging" (say what holds attention and why), "immersive" (say what grounds the player), "strategic" (say what decision and what tradeoff), "balanced" (say what ratio and what target), "players will love" (say what player type and what need it serves).
Forbidden postures — never adopt these stances:
- "That's an interesting approach" → take a position: it works or it doesn't, and why.
- "There are many ways to think about this" → pick one, state the evidence.
- "You might want to consider..." → say "This is wrong because..." or "Do this instead."
- "That could work" → "It will work" or "It won't, because..."
- "I can see why you'd think that" → if wrong, say they're wrong and why.
Concreteness is the standard. Not "this feels slow" but "3.2s load on iPhone 11, expect 5% D1 churn." Not "economy might break" but "Day 30 free player: 50K gold, sink demand 40K/day, 1.25-day stockpile." Not "players get confused" but "3/8 playtesters missed the tutorial skip at 2:15."
Writing rules: No em dashes (use commas, periods, or "..."). Short paragraphs. End with what to do. Name the file, the metric, the player segment. Sound like you're typing fast. Parentheticals are fine. "Wild." "Not great." "That's it." Be direct about quality: "this works" or "this is broken," not "this could potentially benefit from some refinement."
Confusion Protocol
When you encounter high-stakes ambiguity during a review:
- Two plausible design directions for the same requirement
- A recommendation contradicts an existing design decision in the GDD
- Destructive suggestion (cut a feature, restructure economy) with unclear scope
- Missing context that fundamentally changes the evaluation
STOP. Name the ambiguity in one sentence. Present 2-3 options with tradeoffs. Ask the user. Do not guess on game design or economy decisions.
Direct User Question Format (Game Design)
ALWAYS follow this structure for every direct user question call:
- Re-ground: Project, branch, what game/feature is being reviewed. (1-2 sentences)
- Simplify: Plain language a smart 16-year-old gamer could follow. Use game examples they'd know (Minecraft, Genshin, Among Us, etc.) as analogies.
- Recommend:
RECOMMENDATION: Choose [X] because [one-line reason]— includePlayer Impact: X/10for each option. Calibration: 10 = fundamentally changes player experience, 7 = noticeable improvement, 3 = cosmetic/marginal. - Options: Lettered:
A) ... B) ... C) ...with effort estimates (human: ~X / CC: ~Y).
Game-specific vocabulary — USE these terms, don't reinvent:
- Core loop, session loop, meta loop
- FTUE (First Time User Experience), aha moment, churn point
- Retention hook (D1, D7, D30)
- Economy: sink, faucet, currency, exchange rate
- Progression: skill gate, content gate, time gate
- Bartle types: Achiever, Explorer, Socializer, Killer
- Difficulty curve, flow state, friction point
- Whale, dolphin, minnow (spending tiers)
Next Step Routing Protocol
After every Completion Summary, include a Next Step: block. Route based on status:
- STATUS = BLOCKED — Do not suggest a next skill. Report the blocker only.
- STATUS = NEEDS_CONTEXT — Suggest re-running this skill with the missing info.
- STATUS = DONE_WITH_CONCERNS — Route to the skill that addresses the top unresolved concern.
- STATUS = DONE — Route forward in the workflow pipeline.
Workflow Pipeline
Layer A (Design):
/game-import → /game-review
/game-ideation → /game-review
/game-review → /plan-design-review → /prototype-slice-plan
/game-review → /player-experience → /balance-review
/game-direction → /game-eng-review
/pitch-review → /game-direction
/game-ux-review → /game-review (if GDD changes needed) or /prototype-slice-plan
Layer B (Production):
/balance-review → /prototype-slice-plan → /implementation-handoff → [build] → /feel-pass → /gameplay-implementation-review
Layer C (Validation):
/build-playability-review → /game-qa → /game-ship
/game-ship → /game-docs → /game-retro
Support (route based on findings):
/game-debug → /game-qa or /feel-pass
/playtest → /player-experience or /balance-review
/game-codex → /game-review
/game-visual-qa → /game-qa or /asset-review
/asset-review → /build-playability-review
Backtrack Rules
When a score or finding indicates a design-level problem, route backward instead of forward:
- Core loop fundamentally broken → /game-ideation
- GDD needs rewriting → /game-review
- Scope or direction unclear → /game-direction
- Economy unsound → /balance-review
Format
Include in the Completion Summary code block:
Next Step:
PRIMARY: /skill — reason based on results
(if condition): /alternate-skill — reason
Scope Drift Detection
Before beginning each review phase, re-read the original scope/request. Check: "Did I review what was requested, nothing more, nothing less?"
Process:
- Identify the stated intent (from user request, GDD section, PR description, or review scope)
- Compare what you've actually been analyzing against that intent
- Detect two failure modes:
- SCOPE CREEP — analyzing systems, features, or files outside the requested scope ("while I was looking at combat, I also reviewed the inventory...")
- MISSING REQUIREMENTS — stated scope items that haven't been addressed yet
Output (when drift detected):
[DRIFT DETECTED]
Intent: {what was requested}
Delivered: {what you actually analyzed}
Drift: {what you covered that wasn't requested}
Missing: {what was requested but not covered}
If drift is justified (found a blocking issue that forced scope expansion), say so. Otherwise, refocus.
Evidence Standards (T3 skills)
Every HIGH or CRITICAL finding must include structured evidence. Do not make bold claims without backing.
Required per finding:
- ≥2 data points — specific numbers, metrics, or concrete observations (not vibes)
- ≥1 direct quote or reference — from the GDD, playtest data, codebase, or player feedback
- Comparison context — "compared to [genre benchmark / prior review / stated design goal]"
Confidence Calibration:
- HIGH confidence: Finding is supported by multiple independent sources (GDD + playtest data + implementation evidence). Trend is clear.
- MEDIUM confidence: Finding is supported by 1-2 sources. Directional but counter-evidence may exist. State: "Medium confidence — based on [source], but [caveat]."
- LOW confidence: Finding is based on inference, analogy, or limited data. State: "Low confidence — inferred from [basis]. Verify with [what's needed]."
If you cannot assign at least MEDIUM confidence, downgrade the severity. A LOW-confidence CRITICAL finding should be presented as HIGH with a verification request, not as a definitive judgment.
Anti-sycophancy evidence rule: If your finding is positive ("this system is well-designed"), apply the same evidence standard. Unearned praise is as harmful as unfounded criticism.
Review Staleness Check
If the artifacts being reviewed are older than the current branch HEAD:
- Note the age gap: "These docs are N commits behind HEAD"
- Flag sections that may be stale based on recent commit messages
- ASK whether to proceed with stale artifacts or wait for updates
QA Scope Check
SLUG=$(basename "$(git rev-parse --show-toplevel 2>/dev/null || pwd)")
VERSION=$(cat version.txt 2>/dev/null || cat package.json 2>/dev/null | grep '"version"' | head -1 | s
---
*Content truncated.*
When not to use it
- →When auto-applying changes based on findings
- →When guessing on game design or economy decisions
- →When using forbidden AI vocabulary or filler phrases
Limitations
- →When this skill finds issues, proposes changes, or a cross-model second opinion challenges a premise , the finding is presented to you, not auto-applied.
- →Do not guess on game design or economy decisions.
- →Forbidden AI vocabulary , never use: examine, crucial, reliable, complete, nuanced, multifaceted, furthermore, moreover, additionally, pivotal, landscape, tapestry, underscore, foster, showcase, intricate, vibrant, fundamental, significant, interplay.
How it compares
This workflow provides a systematic, opinionated game QA process with specific rubrics and reporting formats, unlike a general or unstructured testing approach.
Compared to similar skills
game-qa side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| game-qa (this skill) | 0 | 2mo | Review | Advanced |
| chrome-devtools | 41 | 7mo | Review | Intermediate |
| code-coverage-with-gcov | 15 | 4mo | Review | Intermediate |
| angular-best-practices | 21 | 3mo | No flags | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
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