An AI-driven distillation engine that converts raw wiki entries into high-density, structured knowledge points for advanced information retrieval.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install karpathy-compile
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 karpathy-compile 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 Karpathy Compile Skill - Phase 2?
The Karpathy Compile Skill - Phase 2 is a sophisticated knowledge engineering tool designed to bridge the gap between raw data collection and structured insight. As a core component within the ecosystem of Openclaw Skills, this tool takes the output of preliminary research (Phase 1 wiki entries) and applies LLM distillation techniques to refine the information.
It focuses on density and clarity, ensuring that disparate pieces of information are synthesized into a coherent knowledge base. By using this skill, developers can automate the creation of a high-fidelity knowledge repository that is optimized for both human reading and machine processing through structured metadata and confidence scoring.
Karpathy Compile Skill - Phase 2 Use Cases
- Distilling high volumes of technical documentation into core architectural principles.
- Synthesizing multiple research notes into a single, authoritative knowledge point.
- Preparing high-quality datasets for RAG (Retrieval-Augmented Generation) systems using Openclaw Skills.
- Automating the extraction of metadata and tags from unstructured markdown files.
How Karpathy Compile Skill - Phase 2 Works
- The pipeline initiates by reading the raw markdown wiki files generated during Phase 1.
- Information is grouped logically by subject matter or existing metadata tags to ensure contextual consistency.
- The skill utilizes a local LLM (specifically qwen2.5:14b via Ollama) to perform distillation on each group.
- Structured knowledge points are generated, including a core concept, detailed explanation, and confidence level.
- The refined output is saved into a dedicated directory structure for persistent storage and easy access.
Karpathy Compile Skill - Phase 2 Setup
To integrate this distillation capability into your local Openclaw Skills environment, follow these steps:
# Ensure you have the Phase 1 wiki files available in the project root
# Pull the required LLM model via Ollama
ollama pull qwen2.5:14b
# Install necessary dependencies and run the compilation script
python scripts/__init__.py
# Verify the output in the knowledge-points/ directory
ls knowledge-points/
Karpathy Compile Skill - Phase 2 Data Schema & Taxonomy
Each generated knowledge point follows a strict organizational schema to ensure compatibility across various Openclaw Skills:
| Attribute | Type | Description |
|---|---|---|
| Core Concept | String | A concise, one-sentence summary of the topic. |
| Source | String | Reference to the original wiki source. |
| Detailed Explanation | Markdown | The synthesized explanation produced by the LLM. |
| Tags | List | Categorical labels for indexing and search. |
| Confidence | Enum | Reliability rating: high, medium, or low. |
| Timestamp | Date | The date the knowledge point was distilled. |
Karpathy Compile Skill - Phase 2 Advanced Features
- Multi-agent distillation support for cross-referencing information between different LLM outputs.
- Automated linting integration to deduplicate or update existing knowledge points in Phase 3.
- Seamless integration with M-Flow for structured storage and version control of distilled data.
- Confidence-based filtering to flag entries that require manual human review or more source data.
SKILL.md
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