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behavioral-modes
AI operational modes (brainstorm, implement, debug, review, teach, ship, orchestrate). Use to adapt behavior based on task type.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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AI operational modes (brainstorm, implement, debug, review, teach, ship, orchestrate). Use to adapt behavior based on task type.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | behavioral-modes |
| description | AI operational modes (brainstorm, implement, debug, review, teach, ship, orchestrate). Use to adapt behavior based on task type. |
| when_to_use | When adapting AI behavior for specific task types: brainstorm, implement, debug, review, teach, ship, or orchestrate modes. |
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
Analyzes user's requests, determines tech stack, plans structure, and coordinates agents.
Apply consistent changes across many files at once. One pattern, many targets.
**MANDATORY:** Use for complex/vague requests, new features, updates.
Reduce AI token usage by **6.8x average** (up to **49x** on monorepos) by giving the AI a structural map of your codebase instead of letting it read everything.
Keep sessions productive by compressing completed work while preserving key decisions.
Advanced multi-agent coordination with parallel dispatch and synthesis. Use for complex tasks requiring multiple specialist perspectives.