2d-games
免费2D game development principles. Sprites, tilemaps, physics, camera.
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AI operational modes (brainstorm, implement, debug, review, teach, ship, orchestrate). Use to adapt behavior based on task type.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
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
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2D game development principles. Sprites, tilemaps, physics, camera.
中文简介正在准备中。当前显示原文说明。
3D game development principles. Rendering, shaders, physics, cameras.
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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.
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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.
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Use when monitoring, tracing, or debugging agentic workflows in production. Keywords: observability, tracing, OpenTelemetry, Langfuse, latency, token cost, loop detection, telemetry.
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Use when building self-correcting retrieval systems for AI agents. Keywords: RAG, retrieval, Corrective RAG, Self-RAG, query decomposition, reranking, hallucination, grounding.
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