Context, not Control

A skill that transforms how you work with AI - from micromanaging every step to providing context and letting AI make decisions. Inspired by the "Context, not Control" philosophy from the OpenClaw community.

Core Philosophy

Traditional approach: You tell AI exactly what to do, step by step.
This approach: You tell AI what you want to achieve, AI figures out how.

The key insight: AI works best when you give it rich context about your goals, constraints, and preferences - then trust it to execute within appropriate boundaries.

When to Use This Skill

• - Starting a new project with vague requirements
• Want to reduce back-and-forth and rework
• Need AI to take more initiative and make decisions
• Want to establish clear permission boundaries
• Transitioning from "micromanaging AI" to "trusting AI"

Quick Start

1. Initialize Your Context

Run the initialization script to set up your project context and permission level:

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This creates:

• - PROJECT.md - Your project context (goals, constraints, preferences)
• INLINECODE1 - Your permission boundaries

2. Set Your Permission Level

Choose one of three levels:

Level 1 - Master Mode (Full autonomy)

• - AI makes all technical decisions
• Only confirms: spending money, public messages, deleting databases
• Best for: High trust, high risk tolerance

Level 2 - Collaborative Mode (Balanced, recommended)

• - AI executes most tasks autonomously
• Confirms: money, public messages, important deletions, system changes
• Best for: Most users, balanced control

Level 3 - Assistant Mode (High control)

• - AI provides suggestions and code
• Confirms: All operations before execution
• Best for: New users, low risk tolerance, learning mode

3. Start with Requirements

Instead of detailed specifications, start with what you want:

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AI will ask clarifying questions:

• - Who is this for?
• What's the core use case?
• Any similar products to reference?
• Technical constraints?
• Time/budget limits?

4. Iterate and Execute

AI clarifies → You answer → AI confirms understanding → You approve → AI executes

All clarified requirements are saved to PROJECT.md for future reference.

How It Works

Requirement Clarification Framework

When you provide a vague requirement, AI uses a structured approach:

1. 1. Understand the domain - What type of project is this?
2. Identify the user - Who will use this?
3. Clarify the goal - What problem does this solve?
4. Establish constraints - Technical, time, budget limits?
5. Set success criteria - What does "done" look like?
6. Confirm understanding - Repeat back what you heard

See references/clarification-framework.md for detailed question templates.

Permission System

The skill automatically checks permissions before executing operations:

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Customize your red/yellow/green lines in PERMISSION_CONFIG.yaml.

Context Management

All clarified requirements are automatically saved to PROJECT.md:

• - Project goals and constraints
• Technical stack decisions
• Success criteria
• Permission level
• Iteration history

This context is loaded in future conversations, eliminating repeated questions.

Permission Levels in Detail

Level 1: Master Mode

Philosophy: Maximum autonomy, minimum interruption

AI can do without asking:

• - Write, test, and deploy code
• Install dependencies and tools
• Modify configurations
• Create/update files
• Make architectural decisions
• Research and learn new technologies

AI must confirm:

• - Spending money (API calls, services, domains)
• Sending public messages (emails, tweets, posts)
• Deleting databases or critical data
• Restarting production services

Best for: Experienced users who trust AI and can handle mistakes

Level 2: Collaborative Mode (Default)

Philosophy: Trust but verify on important operations

AI can do without asking:

• - Write and test code
• Create/update files
• Research and documentation
• Install development dependencies
• Run tests and checks

AI must confirm:

• - Spending money
• Sending any external messages
• Deleting important files/data
• Modifying system configurations
• Restarting services
• Installing system-level packages

Best for: Most users, balanced approach

Level 3: Assistant Mode

Philosophy: AI suggests, you decide

AI can do without asking:

• - Provide suggestions and explanations
• Show code examples
• Research information

AI must confirm:

• - All file operations
• All code execution
• All installations
• All external calls

Best for: New users, learning mode, high-stakes environments

Examples

See references/examples.md for detailed examples including:

• - Building a chat application from vague requirements
• Migrating a legacy system with unclear scope
• Creating automation tools with evolving needs

See assets/EXAMPLE_DIALOG.md for sample conversations.

Customization

Custom Permission Rules

Edit PERMISSION_CONFIG.yaml to define your own boundaries:

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Project Templates

Create custom templates in assets/ for recurring project types:

• - INLINECODE10
• INLINECODE11
• INLINECODE12

Troubleshooting

See references/troubleshooting.md for common issues:

• - AI asking too many questions
• AI not asking enough questions
• Permission checks too restrictive/loose
• Context not being saved properly

Scripts Reference

init_context.py

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clarify_requirement.py

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permission_check.py

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update_context.py

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Philosophy: Three Modes of AI Usage

Mode 1: Paintbrush (Micromanagement)

• - You specify every detail
• AI is a tool that executes exactly what you say
• Upper limit: Your expertise

Mode 2: Employee (Delegation)

• - You assign tasks with some guidance
• AI follows your preferred patterns
• Still requires oversight

Mode 3: Master (Autonomy)

• - You set goals and constraints
• AI makes decisions and executes
• You review outcomes, not process

This skill helps you transition from Mode 1 → Mode 3 at your own pace.

Credits

Inspired by the "Context, not Control" philosophy discussed in the OpenClaw community, particularly the experiences shared by contributors who achieved remarkable results by trusting AI with more autonomy.

Version

1.0.0 - Initial release