Hz Unity Project Analyzer logo

Hz Unity Project Analyzer

Organization
meta-quest
hz-unity-project-analyzer

Analyzes, documents, and maintains a living `.agent-docs/` knowledge base for Unity projects targeting Meta Quest and Horizon OS. Use when the user asks to scan project structure, explain how a Unity system works, or update project docs after structural changes.

Overview

Publishermeta-quest
Repositoryagentic-tools
Skill namehz-unity-project-analyzer
Stars
195
Forks
17
Bundled files
Instructions only
LicenseApache-2.0
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 meta-quest on GitHub. Read the source before you install it.

Installation

Install the Hz Unity Project Analyzer 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.
  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/meta-quest/agentic-tools.git /tmp/agentic-tools
mkdir -p .claude/skills
cp -r /tmp/agentic-tools/skills/hz-unity-project-analyzer .claude/skills/hz-unity-project-analyzer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hz Unity Project Analyzer 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 Hz Unity Project Analyzer 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 Hz Unity Project Analyzer 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.

Unity Project Analyzer

Analyze, document, and maintain a living knowledge base of a Unity project's structure, optimized for AI agent comprehension. Output lives in .agent-docs/ as source-control-friendly markdown files.

Modes of Operation

This skill operates in four modes. Determine which mode to use based on context:

Mode 1: First-Time Full Scan

Trigger: .agent-docs/ directory does not exist or .agent-docs/index.md does not exist.

Mode 2: Incremental Update

Trigger: User asks to update docs, or AI agent has made structural changes (new scenes, scripts, prefabs, systems). Default update mode.

Important: Incremental updates are part of implementation, not a separate step. When creating or modifying scripts, prefabs, or assets, update the corresponding .agent-docs/ files in the same pass before moving on to the next task.

Mode 3: Full Rescan

Trigger: User explicitly requests a full rescan (e.g., "rescan project", "full project analysis").

Mode 4: Ingestion

Trigger: User asks about the project structure or how a system/feature works (e.g., "what does this project do", "how does the ball system work", "project structure", "load project analysis"). Only use this mode when .agent-docs/ exists. This mode is read-only — do not modify docs.


Instructions for Full Scan (Mode 1) and Full Rescan (Mode 3)

Step 1: Gather Project Overview

  1. Read README.md, CHANGELOG.md, and any docs in Documentation/ folder
  2. Read Packages/manifest.json to understand all included packages
  3. List all scenes: Assets/**/*.unity
  4. List all script folders and assembly definitions: Assets/**/*.asmdef
  5. List all prefab folders: find directories containing .prefab files
  6. Identify the project's Unity version from ProjectSettings/ProjectVersion.txt
  7. If Unity MCP is connected, optionally inspect scene hierarchies, prefab components, and project settings programmatically for richer data

Step 2: Ask Clarifying Questions

Before documenting, ask the user about anything that is not clear from the code or docs alone. Examples:

  • "I see scenes named X, Y, Z — which is the main entry point?"
  • "There's a folder called [name] with assets I can't determine the purpose of — what is it for?"
  • "I see multiple networking approaches — which is the primary one?"

Only ask about genuinely ambiguous items. If something is clear from naming, folder structure, or code inspection, document it directly.

Step 3: Analyze and Document

For each category below, create or update the corresponding sub-document:

3a. Project Overview (.agent-docs/project-overview.md)
  • Project name, description, Unity version
  • Target platform(s)
  • High-level architecture summary
  • Key third-party packages and how they are used in this project (surface-level only — no internals)
3b. Scene Flow (.agent-docs/scenes/)

Create one file per scene (e.g., .agent-docs/scenes/startup.md).

For each scene document:

  • Purpose and role in the project
  • Key GameObjects and their purpose
  • Which scripts/prefabs are used
  • Scene transitions (what loads this scene, what does this scene load)
  • Whether it's part of the main runtime flow, a test scene, or an example

Create a scene flow diagram in .agent-docs/scenes/_flow.md using mermaid:

mermaid
graph LR
    Startup --> MainMenu --> Gameplay
3c. Systems (.agent-docs/systems/)

Identify logical systems (networking, UI, audio, input, gameplay, etc.) and create one file per system.

For each system document:

  • Purpose and responsibility
  • Key scripts (with file paths) and their roles — focus on how to use them, not implementation
  • Key prefabs the system uses
  • Dependencies on other systems
  • Cross-reference related scenes and other system docs with relative links
3d. Prefabs (.agent-docs/prefabs/)

Group or document individually based on clarity:

  • If a folder of prefabs is self-explanatory by naming → group doc (e.g., .agent-docs/prefabs/ui-elements.md)
  • If individual prefabs have non-obvious purpose → document individually (e.g., .agent-docs/prefabs/player-rig.md)

For each prefab/group:

  • Purpose and when to use it
  • Nested prefab hierarchy (document nested prefabs and variants)
  • Key components attached
  • Which scenes use it
  • Configuration notes (important inspector values, required references)
3e. Assets (.agent-docs/assets/)

Document non-script, non-prefab assets adaptively:

  • Materials, shaders, textures, audio, animations, scriptable objects
  • Group when folder naming is clear; document individually when purpose is non-obvious
  • Focus on: what is it, what is it for, how/where is it used
3f. Scripts Reference (.agent-docs/scripts/)

Scripts are self-documenting through code. Here, document usage context only:

  • Organize by system or feature area
  • For each script: purpose, how to use it, which prefab/scene it belongs to
  • Do NOT duplicate code or describe implementation details

Step 4: Build the Index

Create or update .agent-docs/index.md as the main entry point:

markdown
# [Project Name] — Agent Documentation

> Auto-generated project knowledge base for AI agent comprehension.
> Last updated: YYYY-MM-DD

## Quick Context
[2-3 sentence project summary]

## Document Map
- [Project Overview](project-overview.md)
- Scenes
  - [Scene Flow](<scenes/_flow.md>)
  - [SceneName](<scenes/scene-name.md>)
  - ...
- Systems
  - [SystemName](<systems/system-name.md>)
  - ...
- Prefabs
  - [PrefabGroup](<prefabs/group-name.md>)
  - ...
- Assets
  - [AssetGroup](<assets/group-name.md>)
  - ...
- Scripts
  - [ScriptArea](<scripts/area-name.md>)
  - ...

## Runtime Flow
[Brief description of app lifecycle from launch to gameplay]

Step 5: Create Config

Create .agent-docs/_config.md:

markdown
---
last_full_scan: YYYY-MM-DD
last_update: YYYY-MM-DD
documented_systems:
  - system-name-1
  - system-name-2
documented_scenes:
  - scene-name-1
  - scene-name-2
documented_prefab_groups:
  - group-name-1
  - group-name-2
---

Instructions for Incremental Update (Mode 2)

  1. Read .agent-docs/_config.md and .agent-docs/index.md
  2. Identify what changed:
    • If the AI agent just made changes: update only the docs affected by those changes
    • If the user asks to update: compare current project state against documented state
      • Check for new/removed/renamed scenes, scripts, prefabs
      • Check for new packages in Packages/manifest.json
      • Check git status for recently modified files if helpful
  3. Update only the affected sub-documents
  4. Clean up stale references: If a script, prefab, or asset was deleted or renamed, remove or update references to it in the affected docs. Do not leave broken references.
  5. Update .agent-docs/index.md if new docs were added or removed
  6. Update last_update and the documented lists in .agent-docs/_config.md

Do NOT rewrite docs that haven't changed.


Instructions for Ingestion (Mode 4)

  1. Check if .agent-docs/index.md exists — if not, skip (no docs to ingest)
  2. Read .agent-docs/index.md to get the document map and quick context
  3. Based on the user's question, read only the sub-docs relevant to what they are asking about. For example:
    • "What does this project do?" → read project-overview.md
    • "How does the ball system work?" → read systems/balls.md
    • "What scenes are there?" → read scenes/_flow.md
    • General project questions → read project-overview.md and scenes/_flow.md
  4. Do NOT eagerly read all sub-docs — read on-demand to keep context focused

Document Writing Guidelines

  • Audience: AI agents first. Be explicit, structured, and unambiguous.
  • Size: Keep each sub-doc concise. Prefer structured lists over paragraphs. Target under 200 lines per doc.
  • Cross-references: Use relative markdown links between docs when there's an actual dependency or relationship.
  • Diagrams: Use mermaid when it clarifies flow or architecture better than prose. Skip when prose is clearer.
  • Third-party packages: Document how the project uses them. Do not document their internals.
  • Scripts: Document purpose and usage, not implementation. The code is self-documenting.
  • Prefabs: Document nested hierarchies. Note important inspector configuration.
  • Source control: One concept per file. Use descriptive filenames. Avoid large monolithic docs.
  • Staleness: Include Last updated: YYYY-MM-DD at the top of each sub-doc. This helps identify docs that may need refresh.
  • Dates: Use only the date in Last updated fields — no parenthetical annotations.

File Naming Convention

  • Use lowercase kebab-case for all filenames: player-controller.md, main-menu.md
  • Prefix flow/index files with underscore: _flow.md, _config.md
  • Match scene/system names but in kebab-case: Startup.unitystartup.md

Frequently asked questions

What does the Hz Unity Project Analyzer AI skill do?

Analyzes, documents, and maintains a living `.agent-docs/` knowledge base for Unity projects targeting Meta Quest and Horizon OS. Use when the user asks to scan project structure, explain how a Unity system works, or update project docs after structural changes.

Why use Hz Unity Project Analyzer on TypingMind?

Because you install it once and use it with any model. Hz Unity Project Analyzer 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 Hz Unity Project Analyzer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/meta-quest/agentic-tools/tree/main/skills/hz-unity-project-analyzer. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Hz Unity Project Analyzer?

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 Hz Unity Project Analyzer?

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

Is the Hz Unity Project Analyzer AI skill free?

Yes. It is published on GitHub by meta-quest under the Apache-2.0 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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