behavioral-modes
AI operational modes (brainstorm, implement, debug, review, teach, ship, orchestrate). Use to adapt behavior based on task type.
About this skill
The 'Behavioral Modes' skill provides an AI agent with a framework to adopt specific operational styles tailored to different stages or types of tasks. By invoking a mode like 'BRAINSTORM', 'IMPLEMENT', 'DEBUG', 'REVIEW', 'TEACH', 'SHIP', or 'ORCHESTRATE', the AI can fundamentally alter its approach to problem-solving, its interaction patterns, and its focus. This ensures that the AI's output and methodology are optimized for the context, leading to more relevant, efficient, and higher-quality results across various project phases, from initial ideation to final deployment.
Best use case
Guiding an AI agent through complex, multi-stage projects like software development, content creation, or strategic planning. It enables the AI to dynamically adjust its cognitive process, communication style, and prioritization, ensuring optimal performance for tasks ranging from creative ideation to rigorous debugging or structured teaching.
AI operational modes (brainstorm, implement, debug, review, teach, ship, orchestrate). Use to adapt behavior based on task type.
Users can expect the AI agent to produce more focused, context-appropriate, and higher-quality responses. The AI will demonstrate improved efficiency in navigating multi-stage tasks, ask more relevant clarifying questions, offer diverse solutions when appropriate, and align its communication style precisely with the demands of the current operational mode.
Practical example
Example input
Mode: BRAINSTORM. We need to come up with innovative ideas for a new eco-friendly smart home device. Give me some initial concepts.
Example output
Okay, switching to **BRAINSTORM Mode**. I will ask clarifying questions, offer multiple alternatives (at least 3), think divergently, and focus purely on ideas. No code or implementation details will be discussed yet. **Clarifying Questions before diving in:** 1. What is the primary environmental problem this device aims to solve? (e.g., energy consumption, waste reduction, water usage) 2. What is the target demographic for this device? (e.g., tech-savvy early adopters, environmentally conscious families, budget-focused homeowners) 3. Are there any existing smart home ecosystems we should consider integrating with, or is this a standalone product? **Initial Innovative Device Concepts:** **Option 1: 'Compost Companion' (Smart Food Waste Diverter)** * **Concept:** An AI-powered countertop device that identifies food waste type, suggests optimal composting methods (local pickup, home compost, fermentation), and automates pre-processing (shredding/drying). It gamifies waste reduction and connects users to local composting services. * **Unconventional Aspect:** Moves beyond simple sorting; actively manages and processes organic waste while educating users and facilitating community-based solutions. **Option 2: 'Adaptive Bio-Luminary' (Dynamic Smart Lighting)** * **Concept:** Lighting fixtures powered by engineered bioluminescent organisms or advanced low-power LEDs mimicking natural light cycles. AI adjusts light spectrum and intensity not just for human comfort but also to optimize indoor plant growth or even minimize insect attraction, all while being ultra-energy efficient. * **Unconventional Aspect:** Integrates advanced biology/ecology principles into smart home lighting, moving beyond mere energy saving to creating symbiotic indoor environments. **Option 3: 'Resource Echo' (Invisible Leak & Waste Detector)** * **Concept:** A non-intrusive sensor network (e.g., using subtle acoustic analysis, micro-radar, or even molecular sensing) that monitors water, gas, and energy flows without physical contact. AI learns household patterns, detects micro-leaks or inefficiencies before they become visible, and provides predictive maintenance alerts and precise consumption breakdowns. * **Unconventional Aspect:** Focuses on detecting 'invisible' waste and potential failures at their earliest stages, offering proactive resource management rather than reactive alerts after damage occurs. Do any of these spark further interest, or would you like to explore a different angle based on my clarifying questions?
When to use this skill
- When initiating a new project or feature and needing creative, divergent thinking (e.g., BRAINSTORM mode).
- When executing a plan or writing code, requiring focused, direct action (e.g., IMPLEMENT mode).
- When identifying and resolving issues in code or logic, demanding critical analysis (e.g., DEBUG mode).
- When evaluating existing work for quality, completeness, or adherence to standards (e.g., REVIEW mode).
When not to use this skill
- For simple, one-off queries that do not require a specific operational mindset.
- When the AI's default behavior is already perfectly suited for the task without needing adaptation.
- If the task requires a blended approach that doesn't fit neatly into a single, defined mode.
- For purely informational retrieval tasks where behavioral modification offers no significant benefit.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/behavioral-modes/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How behavioral-modes Compares
| Feature / Agent | behavioral-modes | Standard Approach |
|---|---|---|
| Platform Support | Claude | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
AI operational modes (brainstorm, implement, debug, review, teach, ship, orchestrate). Use to adapt behavior based on task type.
Which AI agents support this skill?
This skill is designed for Claude.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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 `npm run 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: | 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 | --- ## 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 ``` ## When to Use This skill is applicable to execute the workflow or actions described in the overview.
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