A professional diagnostic and extension tool for analyzing OpenClaw architecture and generating automated hooks.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install openclaw-self-analyzer
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 openclaw-self-analyzer 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 OpenClaw Self Analyzer?
The OpenClaw Self Analyzer is a powerful utility designed for developers who need to understand or extend the internal mechanics of the OpenClaw framework. It performs a deep scan of the system architecture to identify hook points and creates automated extensions, making it an essential tool for building robust Openclaw Skills. By mapping out the entire lifecycle from input reception to memory storage, it provides the transparency needed to build sophisticated AI agent behaviors and custom integrations.
OpenClaw Self Analyzer Use Cases
- Identifying extension points within the OpenClaw processing pipeline.
- Generating custom pre-processing logic for context gathering.
- Replacing default LLM submission logic with specialized handlers.
- Auditing the system architecture to ensure optimal performance and security.
- Automatically generating hook packages for rapid prototyping.
How OpenClaw Self Analyzer Works
- Execute the architecture analyzer script to map the 9-stage pipeline of the framework.
- Use the system to detect available hook points within the core processing logic.
- Utilize the HookGenerator to define specific logic for pre, post, or replace actions at any pipeline stage.
- Integrate the generated hook packages into your workspace to extend functionality.
- Use the ReportGenerator to export a complete map of your system's hooks and architectural modifications for documentation.
OpenClaw Self Analyzer Setup
To begin analyzing your environment for Openclaw Skills development, navigate to the skill directory and run the core analyzer:
cd /root/.openclaw/workspace/skills/openclaw-self-analyzer
python3 core/architecture_analyzer.py
You can also generate hooks programmatically by importing the HookGenerator in your Python scripts.
OpenClaw Self Analyzer Data Schema & Taxonomy
The skill maps the framework into the following stages, allowing for precise hook injection within Openclaw Skills:
| Stage | Function |
|---|---|
| input_receive | Handles initial user interaction |
| context_gather | Aggregates relevant historical data |
| memory_retrieve | Queries long-term and short-term memory |
| prompt_assemble | Constructs the final prompt string |
| token_check | Ensures payload fits model constraints |
| context_compress | Reduces context size via summarization |
| llm_submit | Manages the API call to the LLM |
| response_process | Parses and cleans the AI response |
| memory_store | Saves the interaction to the database |
Each stage supports three hook types: pre (pre-processing), post (post-processing), and replace (logic override).
OpenClaw Self Analyzer Advanced Features
- Automated package generation for complex hook logic across all pipeline stages.
- Support for complete logic replacement to override default framework behaviors.
- Programmatic API for generating and saving reports on system architecture.
- Deep code scanning to ensure architectural compliance when building new Openclaw Skills.
- Multi-stage hook support for creating complex, multi-layered agent automations.
SKILL.md
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