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Game Visual Qa

Community
fagemx
game-visual-qa

Visual QA for games. Reviews art style consistency, UI alignment, animation quality, screen adaptation, and visual polish.

Overview

Publisherfagemx
Repositorygstack-game
Skill namegame-visual-qa
Stars
81
Forks
10
Bundled files
6
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by fagemx on GitHub. Read the source before you install it.

Installation

Install the Game Visual Qa AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/fagemx/gstack-game/tree/main/skills/game-visual-qa
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/fagemx/gstack-game.git /tmp/gstack-game
mkdir -p .claude/skills
cp -r /tmp/gstack-game/skills/game-visual-qa .claude/skills/game-visual-qa
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Game Visual Qa in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Game Visual Qa on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Game Visual Qa is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Preamble (run first)

bash
setopt +o nomatch 2>/dev/null || true  # zsh compat
_GD_VERSION="0.5.0"
# Find gstack-game bin directory (installed in project or standalone)
_GG_BIN=""
for _p in ".claude/skills/gstack-game/bin" ".claude/skills/game-review/../../gstack-game/bin" "$(dirname "$(readlink -f .claude/skills/game-review/SKILL.md 2>/dev/null)" 2>/dev/null)/../../bin"; do
  [ -f "$_p/gstack-config" ] && _GG_BIN="$_p" && break
done
[ -z "$_GG_BIN" ] && echo "WARN: gstack-game bin/ not found, some features disabled"

# Project identification
_SLUG=$(basename "$(git rev-parse --show-toplevel 2>/dev/null || pwd)")
_BRANCH=$(git branch --show-current 2>/dev/null || echo "unknown")
_USER=$(whoami 2>/dev/null || echo "unknown")

# Session tracking
mkdir -p ~/.gstack/sessions
touch ~/.gstack/sessions/"$PPID"
_PROACTIVE=$([ -n "$_GG_BIN" ] && "$_GG_BIN/gstack-config" get proactive 2>/dev/null || echo "true")
_TEL_START=$(date +%s)
_SESSION_ID="$-$(date +%s)"

# Shared artifact storage (cross-skill, cross-session)
mkdir -p ~/.gstack/projects/$_SLUG
_PROJECTS_DIR=~/.gstack/projects/$_SLUG

# Telemetry (sanitize inputs before JSON interpolation)
mkdir -p ~/.gstack/analytics
_SLUG_SAFE=$(printf '%s' "$_SLUG" | tr -d '"\\\n\r\t')
_BRANCH_SAFE=$(printf '%s' "$_BRANCH" | tr -d '"\\\n\r\t')
echo '{"skill":"game-visual-qa","ts":"'$(date -u +%Y-%m-%dT%H:%M:%SZ)'","repo":"'"$_SLUG_SAFE"'","branch":"'"$_BRANCH_SAFE"'"}'  >> ~/.gstack/analytics/skill-usage.jsonl 2>/dev/null || true

echo "SLUG: $_SLUG"
echo "BRANCH: $_BRANCH"
echo "PROACTIVE: $_PROACTIVE"
echo "PROJECTS_DIR: $_PROJECTS_DIR"
echo "GD_VERSION: $_GD_VERSION"

# Artifact summary
_ARTIFACT_COUNT=$(ls "$_PROJECTS_DIR"/*.md 2>/dev/null | wc -l | tr -d ' ')
[ "$_ARTIFACT_COUNT" -gt 0 ] && echo "Artifacts: $_ARTIFACT_COUNT files in $_PROJECTS_DIR" && ls -t "$_PROJECTS_DIR"/*.md 2>/dev/null | head -5 | while read f; do echo "  $(basename "$f")"; done

Shared artifact directory: $_PROJECTS_DIR (~/.gstack/projects/{slug}/) stores all skill outputs:

  • Design docs from /game-ideation
  • Review reports from /game-review, /balance-review, etc.
  • Player journey maps from /player-experience

All skills read from this directory on startup to find prior work. All skills write their output here for downstream consumption.

If PROACTIVE is "false", do not proactively suggest gstack-game skills.

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.

Public Output Redaction Lite

Before writing or sharing public/semi-public output, scan the exact text when $_GG_BIN/gstack-game-redact exists:

bash
printf '%s' "$OUTPUT_TEXT" | "$_GG_BIN/gstack-game-redact" --json

Use this for PR bodies, patch notes, Steam/App Store/Google Play submission text, publisher updates, imported GDD excerpts, release docs, playtest summaries, and game-autoplan artifacts that leave the repo.

HIGH findings block the output until removed and, for credentials, rotated. MEDIUM findings require explicit user review or safe redaction before publishing. Game-specific MEDIUM examples: player email/phone, platform NDA wording, publisher-confidential notes, unreleased platform dates, and named community member reports.

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.

AskUserQuestion Format (Game Design)

ALWAYS follow this structure for every AskUserQuestion call:

  1. Re-ground: Project, branch, what game/feature is being reviewed. (1-2 sentences)
  2. Simplify: Plain language a smart 16-year-old gamer could follow. Use game examples they'd know (Minecraft, Genshin, Among Us, etc.) as analogies.
  3. Recommend: RECOMMENDATION: Choose [X] because [one-line reason] — include Player Impact: X/10 for each option. Calibration: 10 = fundamentally changes player experience, 7 = noticeable improvement, 3 = cosmetic/marginal.
  4. 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)

Option Overflow (Game Design)

Never drop game options because a question UI only accepts 2-4 choices. Lost options become accidental product decisions.

When a decision has more than four mutually exclusive options, split it instead of trimming it:

  • Ask the first question at the category level, then ask a follow-up for the chosen category.
  • Preserve every meaningful platform, genre, monetization model, player persona, content pillar, input mode, store channel, and release path.
  • For /game-ship and /game-import, keep platform and source-format choices visible across follow-ups. Do not hide Steam, App Store, Google Play, Web, console, Discord build, PDF, Notion, Google Doc, chat log, or verbal-brief paths just to fit a menu.

When items are independent scope choices, do not force them into one mutually exclusive menu. Ask one AskUserQuestion per item with the same four actions:

Include / Defer / Cut / Hold

Use this for feature lists, launch checklist items, QA matrices, accessibility tasks, localization languages, analytics events, and content drops.

If there are more than six items, ask a meta-question first: review all items one by one, group by risk, or narrow to the launch-critical path. Still name the dropped-or-deferred group explicitly.

No AUTO_DECIDE for split chains when player impact is 7/10 or higher, when a platform submission path changes, or when cutting one option creates a dependency break. Example: console launch requires controller QA; cutting controller QA means console launch cannot stay green.

Next Step Routing Protocol

After every Completion Summary, include a Next Step: block. Route based on status:

  1. STATUS = BLOCKED — Do not suggest a next skill. Report the blocker only.
  2. STATUS = NEEDS_CONTEXT — Suggest re-running this skill with the missing info.
  3. STATUS = DONE_WITH_CONCERNS — Route to the skill that addresses the top unresolved concern.
  4. 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:

  1. Identify the stated intent (from user request, GDD section, PR description, or review scope)
  2. Compare what you've actually been analyzing against that intent
  3. 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:

  1. Note the age gap: "These docs are N commits behind HEAD"
  2. Flag sections that may be stale based on recent commit messages
  3. ASK whether to proceed with stale artifacts or wait for updates

Telemetry (run last)

bash
_TEL_END=$(date +%s)
_TEL_DUR=$(( _TEL_END - _TEL_START ))
[ -n "$_GG_BIN" ] && "$_GG_BIN/gstack-telemetry-log" \
  --skill "game-visual-qa" --duration "$_TEL_DUR" --outcome "OUTCOME" \
  --used-browse "false" --session-id "$_SESSION_ID" 2>/dev/null &

Load References

bash
setopt +o nomatch 2>/dev/null || true  # zsh compat
echo "=== Loading game-visual-qa reference files ==="
ls references/*.md 2>/dev/null | while read f; do echo "  $f"; done

Read ALL references/ files NOW before any user interaction. They contain visual thresholds, animation standards, platform requirements, scoring model, and gotchas. Zero interruption — load everything upfront.

Scope Boundaries

  • /game-visual-qa: Reviews the visual output of a built game (what the player sees on screen)
  • /asset-review: Reviews asset files in the project (formats, sizes, naming, pipeline)
  • /game-qa Section 3: Quick visual pass as part of full QA — use /game-visual-qa for deep visual-only review

/game-visual-qa: Visual Quality Assurance

Systematic visual review of game screens, UI, animations, and art consistency. Apply references/gotchas.md throughout the entire review.

Step 0: Visual Context

AskUserQuestion: What are we reviewing?

  • A) Full game visual sweep
  • B) Specific screen/scene
  • C) UI-only review
  • D) Animation review

Establish:

  1. Art style reference — art bible, moodboard, or reference game?
  2. Art style classification: A) Pixel art, B) Hand-drawn/2D, C) 3D stylized, D) 3D realistic, E) Mixed media
    • Style classification affects which thresholds from references/animation-standards.md apply
  3. Target resolution and target platforms (determines thresholds from references/platform-requirements.md)
  4. Build status — final art, WIP, or placeholder? (See references/gotchas.md Scope Traps)

STOP. Confirm context before proceeding.

Section 1: First Impression (weight: 10%)

AI confidence: 25% — first impressions are inherently subjective. This section is a prompt for human evaluation, not an AI judgment.

1-second gut reaction:

  • Professional or amateur?
  • Cohesive or inconsistent?
  • Clear or cluttered?

Forcing questions:

  • "If you saw this screenshot on a store page with no title, would you wishlist it?"
  • "What genre and quality tier does the art communicate in the first second?"

Score using the per-section scoring from references/scoring.md. Section 1 Score: ___/10

STOP. One issue per AskUserQuestion.

Section 2: Art Style Consistency (weight: 20%)

AI confidence: 35% — style evaluation requires art direction context. Flag inconsistencies but defer final judgment to the user.

  • Color palette adherence — Do all elements use the same palette?
  • Line weight consistency — Outlines same thickness across all assets?
  • Lighting direction — Consistent light source across all scenes?
  • Character proportion — Same head-to-body ratio across characters?
  • Environmental scale — Objects correct size relative to characters?
  • Effect style — Particles/VFX match the art style? (realistic effects in pixel art = mismatch)

Forcing questions:

  • "Show me the 2 most visually different screens side by side. Are they from the same game?"
  • "If you removed the HUD, could you identify which game this screenshot is from?"

Classify severity per issue using references/scoring.md severity definitions. Score: Start at 10, deduct per issue found.

STOP. One issue per AskUserQuestion.

Section 3: UI Polish (weight: 20%)

AI confidence: 65% — most UI checks are measurable against references/visual-thresholds.md.

  • Alignment — Elements on grid? Consistent margins? (see references/visual-thresholds.md Pixel Alignment)
  • Typography — Font hierarchy clear? Readable at target resolution? (see font size minimums)
  • Color meaning — Consistent (red = damage, green = heal) across ALL screens?
  • Touch/click targets — Meet platform minimums from references/visual-thresholds.md?
  • States — Hover, pressed, disabled, loading states all designed?
  • Transitions — Screens transition smoothly? No jarring cuts?

Forcing questions:

  • "Navigate the 3 most-used menus using only the intended input method. Any hesitation point?"
  • "Screenshot every screen with text. Is the font hierarchy identical across all of them?"

Classify severity per issue using references/scoring.md. Score: ___/10

STOP. One issue per AskUserQuestion.

Section 4: Animation Quality (weight: 20%)

AI confidence: 60% — frame counts and blend times are measurable; feel and weight are subjective.

  • Frame count — Meets guidelines from references/animation-standards.md for the art style?
  • Anticipation — Actions have wind-up >= 2 frames? (makes them readable)
  • Follow-through — Actions have recovery >= 2 frames? (makes them feel weighty)
  • Timing — Fast actions feel fast? Heavy actions feel heavy?
  • Blending — Transition times within targets from references/animation-standards.md? No T-pose flashes?
  • Idle — Characters alive when not acting? Breathing, blinking, shifting weight?

Forcing questions:

  • "Play the most common action at 0.25x speed. Is every frame intentional?"
  • "Watch a character stand idle for 30 seconds. Do they feel alive?"

Check against common animation bugs in references/animation-standards.md. Classify severity per issue using references/scoring.md. Score: ___/10

STOP. One issue per AskUserQuestion.

Section 5: Screen Adaptation (weight: 15%)

AI confidence: 75% — safe zones and aspect ratios are fully measurable against references/platform-requirements.md.

  • Aspect ratios — Test at widest and narrowest supported ratio. Nothing cut off?
  • Safe zone — Critical UI within safe area per references/platform-requirements.md?
  • Scaling — Text readable on smallest target screen? (check against references/visual-thresholds.md)
  • Notch/cutout — Content not hidden behind camera notch? Uses platform safe area APIs?
  • Orientation — If mobile: landscape/portrait handled correctly?

Forcing questions:

  • "Screenshot the main gameplay screen at 4:3 (iPad) and 19.5:9 (iPhone). What breaks?"
  • "Can you read all HUD text on the smallest target device at arm's length?"

Classify severity per issue using references/scoring.md. Score: ___/10

STOP. One issue per AskUserQuestion.

Section 6: Performance Visual (weight: 15%)

AI confidence: 70% — most performance visuals are observable, though root causes may not be.

  • Texture quality — No blurry textures at expected view distance?
  • Pop-in — LOD transitions visible? Asset streaming visible?
  • Z-fighting — Overlapping surfaces flickering?
  • Overdraw — Excessive transparency layers? (check with wireframe)
  • Particle budget — Particle effects reasonable? No FPS drop during effects?
  • Frame pacing — Consistent frame delivery? (see FPS targets in references/animation-standards.md)

Forcing questions:

  • "Run through the busiest scene in the game. Any visual hitches or pop-in?"
  • "Toggle between lowest and highest quality settings. What changes are visible?"

Classify severity per issue using references/scoring.md. Score: ___/10

STOP. One issue per AskUserQuestion.

Scoring

Calculate weighted final score using formula from references/scoring.md:

Visual QA Score:
  First Impression:       _/10 (weight: 10%)
  Art Consistency:        _/10 (weight: 20%)
  UI Polish:              _/10 (weight: 20%)
  Animation Quality:      _/10 (weight: 20%)
  Screen Adaptation:      _/10 (weight: 15%)
  Performance Visual:     _/10 (weight: 15%)
  ─────────────────────────────
  WEIGHTED TOTAL:         _/10

Interpret using score ranges from references/scoring.md: 90-100 ship-ready, 75-89 shippable with known issues, 60-74 needs work, below 60 not ready.

AUTO/ASK/ESCALATE

  • AUTO: Flag measurable threshold violations (font size, touch targets, contrast ratios, safe zone violations, animation frame counts below minimum)
  • ASK: Style direction choices, animation priority tradeoffs, screen adaptation strategy (pillarbox vs expand vs crop), severity disputes
  • ESCALATE: Art style fundamentally inconsistent (multiple conflicting styles with no art direction justification), UI unusable at target resolution, critical animation bugs in main gameplay loop

Anti-Sycophancy

Forbidden:

  • "Beautiful art style"
  • "Smooth animations"
  • "Clean UI"
  • "Polished visuals"
  • "Great aesthetic"
  • "Nice color palette"
  • "Looks professional"

Instead use specific, measurable observations: "Button text is 10px at 720p — below readability threshold (14px min). 3 screens use rounded buttons, 2 use square — inconsistent."

Push-back cadence: After every 3 positive findings, actively look for one issue. If you find none, state: "Checked [area] for [specific problem] — none found."

Calibrated acknowledgment examples:

  • "Walk cycle is 8 frames with correct contact/pass/up/down phases. Foot sliding is under 1px — within tolerance."
  • "Color contrast on main HUD passes WCAG AA (measured 5.2:1). Settings menu contrast is 2.9:1 — below 4.5:1 minimum for body text."

Completion Summary

Visual QA:
  Scope: [full/screen/UI/animation]
  Art style: [pixel/2D/3D-stylized/3D-realistic/mixed]
  Issues: ___ critical, ___ high, ___ medium, ___ low
  Visual Score: _/10
  STATUS: DONE / DONE_WITH_CONCERNS / BLOCKED

  Next Step:
    PRIMARY: /game-qa — visual issues cataloged, include in QA
    (if asset problems): /asset-review — pipeline issue

Save Artifact

bash
_DATETIME=$(date +%Y%m%d-%H%M%S)
echo "Saving to: $_PROJECTS_DIR/${_USER}-${_BRANCH}-visual-qa-${_DATETIME}.md"

Write to $_PROJECTS_DIR/{user}-{branch}-visual-qa-{datetime}.md. Supersedes prior if exists.

Discoverable by: /game-ship, /game-qa

Review Log

bash
[ -n "$_GG_BIN" ] && "$_GG_BIN/gstack-review-log" '{"skill":"game-visual-qa","timestamp":"TIMESTAMP","status":"STATUS","visual_score":SCORE,"issues":N,"commit":"COMMIT"}' 2>/dev/null || true

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Game Visual Qa AI skill do?

Visual QA for games. Reviews art style consistency, UI alignment, animation quality, screen adaptation, and visual polish.

Why use Game Visual Qa on TypingMind?

Because you install it once and use it with any model. Game Visual Qa is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Game Visual Qa in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/fagemx/gstack-game/tree/main/skills/game-visual-qa. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Game Visual Qa?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Game Visual Qa?

As many as you like. As long as a model supports skills, you can use Game Visual Qa with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Game Visual Qa AI skill free?

Yes. It is published on GitHub by fagemx under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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