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Build Threejs Enemy Systems

CommunityPopular
MengTo
build-threejs-enemy-systems

Build or refactor reusable, data-driven enemy archetype and moveset systems for Three.js action games. Use for enemy content schemas, model and rig conventions, combat move timing and contact contracts, runtime state boundaries, placeholder fallbacks, deterministic fixtures, or production playthrough validation.

Overview

PublisherMengTo
RepositorySkills
Skill namebuild-threejs-enemy-systems
Stars
6.1K
Forks
717
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Build Threejs Enemy Systems 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/MengTo/Skills.git /tmp/Skills
mkdir -p .claude/skills
cp -r /tmp/Skills/agent-skills/game-development/build-threejs-enemy-systems .claude/skills/build-threejs-enemy-systems
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Build Threejs Enemy Systems 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 Build Threejs Enemy Systems 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 Build Threejs Enemy Systems 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.

Build Three.js Enemy Systems

Make each enemy a portable authored definition consumed by shared runtime systems. Keep AI choice, combat resolution, rendering, and feedback as separate consumers of the same stable contract.

Separate content from runtime state

  • Put stable IDs, role/tags, base stats, presentation spec, move IDs, AI hints, feedback hook IDs, rewards, and variant data in immutable authored definitions.
  • Put instance ID, transform, health/posture, target, current move and phase clock, cooldowns, statuses, pathing, visibility, and LOD state in runtime instances.
  • Reference definitions by ID. Reject duplicate IDs, missing references, invalid ranges, or impossible timing during content validation.
  • Never mutate authored definitions, store live timers in content, or duplicate the combat clock across AI, animation, and rendering.

Normalize presentation contracts

Define these fields before integrating a model:

  • source or factory ID, provenance, coordinate system, forward axis, and meters-per-unit;
  • root pivot at ground contact, normalized scale, facing offset, and visual height;
  • simple movement collider plus named hurtboxes, independent of render geometry;
  • stable sockets for contact origins, weapons, projectiles, VFX, audio, UI markers, and targeting;
  • rig and semantic clip map for idle, locomotion, moves, reactions, and death;
  • LOD tiers with hysteresis, animation/update policy, and unchanged gameplay colliders.

Normalize at the asset boundary. Do not scatter scale fixes, pivot offsets, raw clip names, or compensating rotations through gameplay code.

Define moves as data

For every move, define:

  • stable ID, AI tags, eligibility range/angle/line-of-sight, weight, and state prerequisites;
  • startup and telegraph, one or more active windows, recovery, cooldown, and minimum commitment;
  • movement, facing, cancellation, armor, interrupt, stagger, and phase rules;
  • contact shape/socket, damage, posture, knockback, status, and per-target hit limits;
  • semantic animation, VFX, and audio hook IDs with cleanup rules.

Let AI select only legal move IDs; let the combat system own timing and outcomes. Resolve damage from authoritative collision/contact events using stable action and target identifiers. Apply each contact once, then drive animation, VFX, audio, and UI from the resolved event.

Provide an honest fallback

When a production model is unavailable, use a deliberate placeholder that preserves footprint, height, pivot, collider, sockets, facing, move timing, and state readability. Log the fallback once and keep it visually unmistakable. Never silently substitute a mismatched asset or call placeholder visuals production-ready.

Add a new enemy

  1. Existing archetype and moves fit? Configure a new definition; do not add runtime branches.
  2. A new behavior is essential? Add the smallest reusable move, hook, or AI tag before adding a new subsystem.
  3. Model ready? Normalize it to the shared presentation contract; otherwise use the honest fallback.
  4. Content complete? Validate references, timing, contact, cooldown, recovery, interrupts, and feedback hooks.
  5. Proof ready? Add deterministic fixtures and automated tests, then run desktop and mobile playthroughs.

Prove the system

  • Create deterministic fixtures for spawn, each move phase, wrong range/direction, obstruction, interrupt, stagger, death/reset, missing-model fallback, LOD transitions, and multiple instances sharing one definition.
  • Test content uniqueness and references, active-window boundaries, cooldown/recovery, per-target hit deduplication, lifecycle cleanup, and pause/frame-step behavior.
  • Play a representative encounter in the repository-approved browser. Confirm telegraphs, contact, state, animation, VFX/audio, targetability, and rewards agree.
  • Verify desktop and mobile camera distance, touch controls, reduced motion, console health, frame time, draw calls, memory stability, and crowded-scene LOD behavior. Report baseline failures separately.

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 Build Threejs Enemy Systems AI skill do?

Build or refactor reusable, data-driven enemy archetype and moveset systems for Three.js action games. Use for enemy content schemas, model and rig conventions, combat move timing and contact contracts, runtime state boundaries, placeholder fallbacks, deterministic fixtures, or production playthrough validation.

Why use Build Threejs Enemy Systems on TypingMind?

Because you install it once and use it with any model. Build Threejs Enemy Systems 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 Build Threejs Enemy Systems in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/MengTo/Skills/tree/main/agent-skills/game-development/build-threejs-enemy-systems. 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 Build Threejs Enemy Systems?

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 Build Threejs Enemy Systems?

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

Is the Build Threejs Enemy Systems AI skill free?

Yes. It is published on GitHub by MengTo 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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