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Gameplay Visual Review

Community
witnesstodark
gameplay-visual-review

Verify a visual game change using controlled native captures, targeted behavioral checks and a reviewable evidence record. Use for animation, VFX, HUD or scene changes whose appearance must be judged in the actual game.

Overview

Publisherwitnesstodark
Repositorymr-mak-workspace
Skill namegameplay-visual-review
Stars
545
Forks
183
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Gameplay Visual Review 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/witnesstodark/mr-mak-workspace/tree/main/.agents/skills/gameplay-visual-review
  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/witnesstodark/mr-mak-workspace.git /tmp/mr-mak-workspace
mkdir -p .claude/skills
cp -r /tmp/mr-mak-workspace/.agents/skills/gameplay-visual-review .claude/skills/gameplay-visual-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gameplay Visual Review 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 Gameplay Visual Review 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 Gameplay Visual Review 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.

Gameplay visual review

Read the requested change and its accepted baseline. Choose a short test that can reveal the difference. Preserve the current scene and unrelated systems; inspect editor state before taking control, and coordinate if another agent is already using the same editor. Do not restart an active tool merely for a capture.

Build a fair comparison

Use the game's actual camera, lighting, materials and normal playback speed. Keep seed, subjects, route/action and framing comparable for OFF/ON or before/after captures. Record any unavoidable difference. A slow-motion close-up is useful diagnostic evidence, but it should not replace normal gameplay zoom.

A review lab should isolate the changed feature while retaining its production code path. Do not "fix" a comparison with a separate demonstration implementation. If synthetic input is needed, separate it from physical devices and restore the prior input settings afterward. Discard transient test orders rather than saving them into the authored scene.

Check appearance and behavior separately

For animation, inspect contact, seams, interrupted transitions and responsive controls. For VFX, inspect contrast, overlap, attachment and cleanup. For UI, inspect readability, overflow, input blocking and real state. For terrain, inspect scale, grounding, traversal and the gameplay camera.

Counters, hashes and saved values can prove wiring or unchanged inputs. They do not prove visual quality. A screenshot does not prove a timed transition, and a passing browser study does not prove native rendering cost. Measure the relevant system only, and keep unrelated sound/narration quiet during visual-only checks.

Leave useful evidence

Record the source revision, saved configuration, exact scenario, capture type, observations and remaining issues. Label generated art, browser schematics, isolated engine previews and native gameplay clearly. A successful agent check does not imply the user accepted the result.

Provide short local loops or videos for motion and stills for detail. Keep the original capture and a lightweight review copy with provenance. Do not repeatedly recompress an already compressed delivery. Update the current specification when behavior changes; put chronological details in the development log.

Example: compare a shield impact OFF/ON on dark and pale ground with identical gameplay. Deliver the visible comparison plus cancellation and cleanup results; leave final aesthetic acceptance explicit. Dependencies: native engine access and a local capture method; a report viewer is optional.

Frequently asked questions

What does the Gameplay Visual Review AI skill do?

Verify a visual game change using controlled native captures, targeted behavioral checks and a reviewable evidence record. Use for animation, VFX, HUD or scene changes whose appearance must be judged in the actual game.

Why use Gameplay Visual Review on TypingMind?

Because you install it once and use it with any model. Gameplay Visual Review 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 Gameplay Visual Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/witnesstodark/mr-mak-workspace/tree/main/.agents/skills/gameplay-visual-review. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Gameplay Visual Review?

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 Gameplay Visual Review?

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

Is the Gameplay Visual Review AI skill free?

Yes. It is published on GitHub by witnesstodark 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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