context-not-control

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name: context-not-control description: Enable "Context, not Control" workflow - clarify requirements through multi-turn dialogue, reduce rework, and execute with appropriate permission levels. Use when users want AI to take more autonomy, need help clarifying vague requirements, or want to establish trust-based collaboration patterns. Supports three permission levels (Master/Collaborative/Assistant) and automatic context management.

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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:

python scripts/init_context.py

This creates:

PROJECT.md - Your project context (goals, constraints, preferences)

PERMISSION_CONFIG.yaml - 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:

"I need a team chat tool"

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. 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:

# Example: AI wants to delete a file
if permission_check('delete_file', user_permission_level):
    # Ask user for confirmation
else:
    # Execute directly

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:

permission_level: 2
custom_red_lines:
  - deploy_to_production
  - modify_database_schema
  - send_customer_emails
custom_yellow_lines:
  - install_npm_packages
  - modify_env_files
Everything else is green (no confirmation needed)

Project Templates

Create custom templates in assets/ for recurring project types:

PROJECT_TEMPLATE_WEBAPP.md

PROJECT_TEMPLATE_API.md

PROJECT_TEMPLATE_AUTOMATION.md

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

Initialize project context and permission config

python scripts/init_context.py [--project-name NAME] [--permission-level 1|2|3]

clarify_requirement.py

Run requirement clarification dialogue

python scripts/clarify_requirement.py "I need a chat app"

permission_check.py

Check if an operation requires confirmation

python scripts/permission_check.py --action delete_file --level 2

update_context.py

Update project context with new information

python scripts/update_context.py --add-goal "Support 1000 concurrent users"

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

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.

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