A robust system for AI agents to enforce code quality red lines, manage progressive context loading, and maintain persistent plan tracking.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install ai-coding-standards
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 ai-coding-standards 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 AI Coding Standards?
The AI Coding Standards skill is a specialized framework designed to combat context entropy and maintain high technical standards in AI-driven development. Based on Claude Code best practices, it provides a set of quality red lines and self-correction mechanisms that ensure AI agents produce clean, modular, and maintainable code.
By implementing this skill within Openclaw Skills, developers can prevent the common pitfall of 'context bloating' where an agent's performance degrades as the conversation grows. It shifts the focus from broad context coverage to precise context hits, utilizing the file system as the single source of truth for persistent task state and using automated hooks to intercept and correct non-compliant code generation immediately.
AI Coding Standards Use Cases
- Enforcing hard technical thresholds such as maximum function length and file size during AI code generation.
- Reducing token usage and increasing accuracy by implementing progressive context loading for bug fixes and refactoring.
- Resuming complex development tasks across multiple AI sessions using persistent plan files and checklists.
- Automatically intercepting and rejecting non-compliant code commits via pre-configured hooks.
How AI Coding Standards Works
- The system monitors AI output against defined Quality Red Lines, such as ensuring functions remain under 30 lines and files under 800 lines.
- ContextManager dynamically adjusts the information provided to the LLM, loading only target modules for bugs or global context only for major architectural decisions.
- PlanTracker synchronizes complex tasks into a dedicated memory directory, allowing the agent to track progress and recover from breakpoints using the file system.
- HookRunner executes automated scripts that provide instant feedback to the AI when a coding standard is violated, effectively acting as a guardrail.
AI Coding Standards Setup
To integrate this into your workflow, first establish the quality hooks in your project root:
# Create quality check hooks
mkdir -p .git/hooks
cp pre-commit.sample .git/hooks/pre-commit
chmod +x .git/hooks/pre-commit
Then, create a CLAUDE.md file in your root directory to define the enforcement strategy for Openclaw Skills:
# Project Coding Standards
## Quality Red Lines
- Functions <= 30 lines
- Files <= 800 lines
## Loading Strategy
- Bug fixes: Load relevant files only
- Refactoring: Load full context
AI Coding Standards Data Schema & Taxonomy
The skill organizes metadata and state through a structured hierarchy to ensure persistence:
| Component | File Path / Format | Description |
|---|---|---|
| Memory State | /memory/tasks/*.md |
Persistent checklists and task progress tracking. |
| Rule Config | CLAUDE.md |
Primary definition of coding standards and loading strategies. |
| Validation | QualityChecker results |
JSON output detailing issues like function length or nesting depth. |
| Hooks | .git/hooks/ |
Script-based interception for real-time rule enforcement. |
AI Coding Standards Advanced Features
- Progressive Context Loading: Optimized hitting rate over coverage to prevent context overflow.
- Plan Persistence: Uses the file system as the single reliable source of truth for state recovery.
- Automated Interception: Moves beyond simple prompting by using executable hooks to enforce rules.
- Self-Correction System: Integrated Python-based QualityChecker for programmatic validation of code structure.
SKILL.md
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