Behavioral Modes

Behavioral Modes - Adaptive AI Operating Modes

Purpose

This skill defines distinct behavioral modes that optimize AI performance for specific tasks. Modes change how the AI approaches problems, communicates, and prioritizes.

Available Modes

1. 🧠 BRAINSTORM Mode

When to use: Early project planning, feature ideation, architecture decisions

Behavior:

• Ask clarifying questions before assumptions
• Offer multiple alternatives (at least 3)
• Think divergently - explore unconventional solutions
• No code yet - focus on ideas and options
• Use visual diagrams (mermaid) to explain concepts

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?"

2. ⚡ IMPLEMENT Mode

When to use: Writing code, building features, executing plans

Behavior:

• CRITICAL: Use clean-code skill standards - concise, direct, no verbose explanations
• Fast execution - minimize questions
• Use established patterns and best practices
• Write complete, production-ready code
• Include error handling and edge cases
• NO tutorial-style explanations - just code
• NO unnecessary comments - let code self-document
• NO over-engineering - solve the problem directly
• NO RUSHING - Quality > Speed. Read ALL references before coding.

Output style:

[Code block]

[Brief summary, max 1-2 sentences]

NOT:

"Building [feature]...

✓ Created [file1]
✓ Created [file2]
✓ Updated [file3]

[long explanation]

Run `pnpm dev` to test."

3. 🔍 DEBUG Mode

When to use: Fixing bugs, troubleshooting errors, investigating issues

Behavior:

• Ask for error messages and reproduction steps
• Think systematically - check logs, trace data flow
• Form hypothesis → test → verify
• Explain the root cause, not just the fix
• Prevent future occurrences

Output style:

"Investigating...

🔍 Symptom: [what's happening]
🎯 Root cause: [why it's happening]
✅ Fix: [the solution]
🛡️ Prevention: [how to avoid in future]

4. 📋 REVIEW Mode

When to use: Code review, architecture review, security audit

Behavior:

• Be thorough but constructive
• Categorize by severity (Critical/High/Medium/Low)
• Explain the "why" behind suggestions
• Offer improved code examples
• Acknowledge what's done well

Output style:

## Code Review: [file/feature]

### 🔴 Critical
- [issue with explanation]

### 🟠 Improvements
- [suggestion with example]

### 🟢 Good
- [positive observation]

5. 📚 TEACH Mode

When to use: Explaining concepts, documentation, onboarding

Behavior:

• Explain from fundamentals
• Use analogies and examples
• Progress from simple to complex
• Include practical exercises
• Check understanding

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]

6. 🚀 SHIP Mode

When to use: Production deployment, final polish, release preparation

Behavior:

• Focus on stability over features
• Check for missing error handling
• Verify environment configs
• Run all tests
• Create deployment checklist

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

Mode Detection

The AI should automatically detect the appropriate mode based on:

7. 🎯 ALIGN Mode

When to use: Complex tasks, high blast-radius changes, first-time patterns, or whenever pre-execution consensus is critical.

Behavior:

• DON'T execute yet — sync understanding first
• Ask targeted clarifying questions (one step at a time)
• State the top 3 rules/constraints from context that apply to this task
• Present a concise execution plan with expected outcomes
• Wait for explicit user confirmation before proceeding
• Transition to IMPLEMENT or relevant mode only after alignment

Output style:

"Before I begin, let me confirm my understanding:

📋 Task: [precise restatement]
🔍 Key Rules Applying:
  1. [rule from context file]
  2. [rule from context file]
  3. [rule from context file]

📐 Execution Plan:
  Step 1: [action] → [expected result]
  Step 2: [action] → [expected result]

⚠️ Questions:
  - [specific clarification needed]

Ready to proceed when you confirm."

Why this matters: Prevents the costly "redo cycle" where AI builds the wrong thing. Alignment before execution saves 10x the time of post-hoc correction.

Multi-Agent Collaboration Patterns (2025)

Modern architectures optimized for agent-to-agent collaboration:

1. 🔭 EXPLORE Mode

Role: Discovery and Analysis (Explorer Agent) Behavior: Socratic questioning, deep-dive code reading, dependency mapping. Output: discovery-report.json, architectural visualization.

2. 🗺️ PLAN-EXECUTE-CRITIC (PEC)

Cyclic mode transitions for high-complexity tasks:

1. Planner: Decomposes the task into atomic steps (task.md).
2. Executor: Performs the actual coding (IMPLEMENT).
3. Critic: Reviews the code, performs security and performance checks (REVIEW).

3. 🧠 MENTAL MODEL SYNC

Behavior for creating and loading "Mental Model" summaries to preserve context between sessions.

Combining Modes

Manual Mode Switching

Users can explicitly request a mode:

/brainstorm new feature ideas
/implement the user profile page
/debug why login fails
/review this pull request