A specialized code caching system that enables AI agents to search, retrieve, and store successful code executions for future reuse.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install raysurfer
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 raysurfer 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 Raysurfer Code Caching?
Raysurfer is a sophisticated caching layer designed to streamline the AI development lifecycle. By acting as a persistent memory for code generation, it allows AI agents to check for existing solutions before writing new code from scratch. This skill is a vital component of the Openclaw Skills ecosystem, providing a mechanism to reduce token consumption and improve code reliability by leveraging previously verified snippets.
The skill connects to a centralized API to perform semantic searches across private and public code repositories. It ensures that once an agent solves a specific problem, that solution is indexed and made available for future tasks, effectively building a growing library of reusable logic tailored to your specific project needs.
Raysurfer Code Caching Use Cases
- Searching for existing solutions to complex coding tasks to avoid redundant generation.
- Accessing community-curated GitHub snippets to implement standard patterns quickly.
- Automatically uploading successful script executions to build a private knowledge base.
- Providing feedback on cached code quality via a voting system to improve future search results.
How Raysurfer Code Caching Works
- The AI agent initiates a search by sending a natural language task description to the Raysurfer API.
- The system performs a semantic search and returns matches with metadata like confidence scores and community votes.
- The agent evaluates the top match; if the score is high enough (typically >= 0.7), it retrieves the code block.
- If no high-quality match exists, the agent generates new code and verifies it through local execution.
- Upon successful execution, the agent either votes on the existing cache hit or uploads the new code as a new cache entry for future Openclaw Skills sessions.
Raysurfer Code Caching Setup
To use Raysurfer within your environment, you must provide an API key. Obtain your token from the Raysurfer dashboard and set it as an environment variable:
export RAYSURFER_API_KEY="your_secret_api_key"
Ensure that your agent has access to curl or the provided Python/TypeScript helper scripts included in the skill directory.
Raysurfer Code Caching Data Schema & Taxonomy
Raysurfer manages code and execution metadata through a structured schema to ensure high-quality retrieval.
| Attribute | Description |
|---|---|
task |
A descriptive string explaining the code's functionality |
code_block |
Object containing id, name, source (raw code), and language |
combined_score |
A float representing the semantic relevance of the code to the task |
succeeded |
A boolean flag indicating if the code executed correctly during storage |
votes |
Quantitative feedback tracked via thumbs_up and thumbs_down counts |
Raysurfer Code Caching Advanced Features
- Support for public snippet crawling by enabling the X-Raysurfer-Public-Snips header.
- Threshold-based decision logic to differentiate between high-confidence hits and starting points.
- Multi-runtime support with pre-built scripts for Python, Bun (TypeScript), and Bash.
- Automated AI voting integration to maintain cache health without manual intervention.
SKILL.md
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