2d-games
無料2D game development principles. Sprites, tilemaps, physics, camera.
日本語の概要は準備中です。原文の説明を表示しています。
AI operational modes (brainstorm, implement, debug, review, teach, ship, orchestrate). Use to adapt behavior based on task type.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
This skill defines distinct behavioral modes that optimize AI performance for specific tasks. Modes change how the AI approaches problems, communicates, and prioritizes.
When to use: Early project planning, feature ideation, architecture decisions
Behavior:
Output style:
"Let's explore this together. Here are some approaches:
Option A: [description]
✅ Pros: ...
❌ Cons: ...
Option B: [description]
✅ Pros: ...
❌ Cons: ...
What resonates with you? Or should we explore a different direction?"
When to use: Writing code, building features, executing plans
Behavior:
clean-code skill standards - concise, direct, no verbose explanationsOutput style:
[Code block]
[Brief summary, max 1-2 sentences]
NOT:
"Building [feature]...
✓ Created [file1]
✓ Created [file2]
✓ Updated [file3]
[long explanation]
Run `npm run dev` to test."
When to use: Fixing bugs, troubleshooting errors, investigating issues
Behavior:
Output style:
"Investigating...
🔍 Symptom: [what's happening]
🎯 Root cause: [why it's happening]
✅ Fix: [the solution]
🛡️ Prevention: [how to avoid in future]
When to use: Code review, architecture review, security audit
Behavior:
Output style:
## Code Review: [file/feature]
### 🔴 Critical
- [issue with explanation]
### 🟠 Improvements
- [suggestion with example]
### 🟢 Good
- [positive observation]
When to use: Explaining concepts, documentation, onboarding
Behavior:
Output style:
## Understanding [Concept]
### What is it?
[Simple explanation with analogy]
### How it works
[Technical explanation with diagram]
### Example
[Code example with comments]
### Try it yourself
[Exercise or task]
When to use: Production deployment, final polish, release preparation
Behavior:
Output style:
## Pre-Ship Checklist
### ✅ Code Quality
- [ ] No TypeScript errors
- [ ] ESLint passing
- [ ] All tests passing
### ✅ Security
- [ ] No exposed secrets
- [ ] Input validation complete
### ✅ Performance
- [ ] Bundle size acceptable
- [ ] No console.logs
### 🚀 Ready to deploy
The AI should automatically detect the appropriate mode based on:
| Trigger | Mode |
|---|---|
| "what if", "ideas", "options" | BRAINSTORM |
| "build", "create", "add" | IMPLEMENT |
| "not working", "error", "bug" | DEBUG |
| "review", "check", "audit" | REVIEW |
| "explain", "how does", "learn" | TEACH |
| "deploy", "release", "production" | SHIP |
Modern architectures optimized for agent-to-agent collaboration:
Role: Discovery and Analysis (Explorer Agent)
Behavior: Socratic questioning, deep-dive code reading, dependency mapping.
Output: discovery-report.json, architectural visualization.
Cyclic mode transitions for high-complexity tasks:
task.md).IMPLEMENT).REVIEW).Behavior for creating and loading "Mental Model" summaries to preserve context between sessions.
Users can explicitly request a mode:
/brainstorm new feature ideas
/implement the user profile page
/debug why login fails
/review this pull request
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
2D game development principles. Sprites, tilemaps, physics, camera.
日本語の概要は準備中です。原文の説明を表示しています。
3D game development principles. Rendering, shaders, physics, cameras.
日本語の概要は準備中です。原文の説明を表示しています。
Create aesthetically beautiful interfaces following proven design principles. Use when building UI/UX, analyzing designs from inspiration sites, generating design images with ai-multimodal, implementing visual hierarchy and color theory, adding micro-interactions, or creating design documentation. Includes workflows for capturing and analyzing inspiration screenshots with chrome-devtools and ai-multimodal, iterative design image generation until aesthetic standards are met, and comprehensive design system guidance covering BEAUTIFUL (aesthetic principles), RIGHT (functionality/accessibility), SATISFYING (micro-interactions), and PEAK (storytelling) stages. Integrates with chrome-devtools, ai-multimodal, media-processing, ui-styling, and web-frameworks skills.
日本語の概要は準備中です。原文の説明を表示しています。
Use when implementing or managing persistent, hierarchical memory systems for AI agents. Covers cross-session state, fact supersession, and self-managed memory tools to enable long-term recall and adaptive agent behavior.
日本語の概要は準備中です。原文の説明を表示しています。
Use when monitoring, tracing, or debugging agentic workflows in production. Keywords: observability, tracing, OpenTelemetry, Langfuse, latency, token cost, loop detection, telemetry.
日本語の概要は準備中です。原文の説明を表示しています。
Use when building self-correcting retrieval systems for AI agents. Keywords: RAG, retrieval, Corrective RAG, Self-RAG, query decomposition, reranking, hallucination, grounding.
日本語の概要は準備中です。原文の説明を表示しています。