A workflow framework that shifts AI interaction from step-by-step micromanagement to goal-oriented autonomy through context-rich dialogue and configurable permission levels.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install context-not-control
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install context-not-control using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Context, not Control?
Context, not Control is a sophisticated workflow designed to transform how developers interact with AI agents. Instead of providing granular, step-by-step instructions, this approach focuses on providing rich context regarding goals, constraints, and preferences. By utilizing these Openclaw Skills, users can establish a trust-based relationship where the AI takes more initiative while operating within clearly defined safety boundaries.
This skill addresses the common friction of AI rework and constant back-and-forth by implementing a structured clarification framework. It allows the AI to understand the 'why' and 'who' behind a project, enabling it to make technical decisions autonomously while you maintain high-level oversight. It is the ideal solution for developers looking to scale their output by treating AI as a capable teammate rather than just a simple tool.
Context, not Control Use Cases
- Launching new software projects with evolving or vague requirements
- Reducing manual oversight for routine coding and deployment tasks
- Establishing safety protocols for AI-driven system modifications and file operations
- Onboarding AI agents to complex legacy codebases with specific technical constraints
- Transitioning a development workflow from micromanagement to high-level delegation
How Context, not Control Works
- The user initializes the environment to create the necessary context and configuration files.
- A specific permission level (Master, Collaborative, or Assistant) is selected to define the AI's autonomy.
- The user provides a high-level goal or requirement rather than detailed technical specifications.
- The AI employs a structured clarification framework to identify domain, users, goals, constraints, and success criteria.
- All clarified requirements are persisted to a project markdown file to ensure long-term context retention.
- The AI executes tasks, automatically checking the permission configuration before performing sensitive operations like deletions or financial transactions.
Context, not Control Setup
To begin using these Openclaw Skills for autonomous workflows, initialize your project context with the following command:
python scripts/init_context.py
This script will generate two critical files: PROJECT.md for storing your goals and constraints, and PERMISSION_CONFIG.yaml for managing your autonomy boundaries. You can further refine your requirements by running:
python scripts/clarify_requirement.py "Your project idea here"
Context, not Control Data Schema & Taxonomy
The skill organizes its operational intelligence through a structured file system:
| File/Folder | Description |
|---|---|
PROJECT.md |
Primary source of truth containing goals, technical stack, and success criteria. |
PERMISSION_CONFIG.yaml |
Defines the red, yellow, and green lines for autonomous AI actions. |
assets/ |
Stores project templates and example dialogue for reference. |
references/ |
Contains the detailed clarification framework and troubleshooting guides. |
Context, not Control Advanced Features
- Three-Tier Permission System: Toggle between Master, Collaborative, and Assistant modes to match your risk tolerance.
- Customizable Red Lines: Define specific actions in YAML that the AI is strictly prohibited from performing without explicit consent.
- Automated Context Persistence: Requirements gathered during dialogue are automatically saved to ensure continuity across different sessions.
- Domain-Specific Templates: Use pre-defined assets for web apps, APIs, or automation tools to speed up the initialization process.
- Multi-Turn Clarification Logic: A built-in framework that ensures the AI understands the user's intent before execution begins.
SKILL.md
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