name: code-cache description: Semantic code caching for AI agents. Cache, retrieve, and reuse code from prior agent executions. metadata: {"openclaw":{"emoji":"🏄","homepage":"https://raysurfer.com","requires":{"env":["RAYSURFER_API_KEY"]},"primaryEnv":"RAYSURFER_API_KEY"}}

Code Cache - Semantic Code Caching for AI Agents

This skill enables semantic code caching via the Raysurfer API.

What It Does

When your agent generates and executes code, Code Cache stores it. When a similar task comes up later, the agent can retrieve and run the cached code instead of regenerating it—saving time and tokens.

Setup

Get your API key from the Raysurfer dashboard and configure it:

# Via environment variable
export RAYSURFER_API_KEY=your_api_key_here

# Or via OpenClaw config
openclaw config set skills.entries.code-cache.apiKey "your_api_key_here"

Available Commands

Search for cached code

/code-cache search <task description> [--top-k N] [--min-score FLOAT] [--show-code]

Search for cached code snippets that match a natural language task description.

Options: - --top-k N — Maximum number of results (default: 5) - --min-score FLOAT — Minimum verdict score filter (default: 0.3) - --show-code — Display the source code of the top match

Example:

/code-cache search "Generate a quarterly revenue report"
/code-cache search "Fetch GitHub trending repos" --top-k 3 --show-code

Get code files for a task

/code-cache files <task description> [--top-k N] [--cache-dir DIR]

Retrieve code files ready for execution, with a pre-formatted prompt addition for your LLM.

Options: - --top-k N — Maximum number of files (default: 5) - --cache-dir DIR — Output directory (default: .code_cache)

Example:

/code-cache files "Fetch GitHub trending repos"
/code-cache files "Build a chart" --cache-dir ./cached_code

Upload code to cache

/code-cache upload <task> --files <path> [<path>...] [--failed] [--no-auto-vote]

Upload code from an execution to the cache for future reuse.

Options: - --files, -f — Files to upload (required, can specify multiple) - --failed — Mark the execution as failed (default: succeeded) - --no-auto-vote — Disable automatic voting on stored code blocks

Example:

/code-cache upload "Build a chart" --files chart.py
/code-cache upload "Data pipeline" -f extract.py transform.py load.py
/code-cache upload "Failed attempt" --files broken.py --failed

Vote on cached code

/code-cache vote <code_block_id> [--up|--down] [--task TEXT] [--name TEXT] [--description TEXT]

Vote on whether cached code was useful. This improves retrieval quality over time.

Options: - --up — Upvote / thumbs up (default) - --down — Downvote / thumbs down - --task — Original task description (optional) - --name — Code block name (optional) - --description — Code block description (optional)

Example:

/code-cache vote abc123 --up
/code-cache vote xyz789 --down --task "Generate report"

How It Works

1. Cache Hit: When you ask for code similar to something previously executed, Code Cache returns the cached version instantly
2. Cache Miss: When no match exists, your agent generates code normally, then Code Cache stores it for future use
3. Verdict Scoring: Code that works gets 👍, code that fails gets 👎—retrieval improves over time

API Reference

The skill wraps these Raysurfer API methods:

Why Code Caching?

LLM agents repeat the same patterns constantly. Instead of regenerating code every time:

• 30x faster: Retrieve proven code instead of waiting for generation

  訪問小蔥技能站7w4.net，解鎖更多實用的AI技能外掛。

• Lower costs: Reduce token usage by reusing cached solutions
• Higher quality: Cached code has been validated and voted on
• Consistent output: Same task = same proven solution

Learn more at raysurfer.com or read the documentation.

這是一個功能完整、文件詳細的程式碼快取工具。優點是使用簡單、測試覆蓋全面、程式碼結構清晰，能幫助 AI agent 複用之前成功生成的程式碼。缺點是需要額外配置 API key，學習成本略高，且嚴重依賴外部服務。總體來說質量不錯，適合經常執行重複程式碼任務的開發者使用。