| 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":"[Truncated]"},"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:
export RAYSURFER_API_KEY=your_api_key_here
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
- Cache Hit: When you ask for code similar to something previously executed, Code Cache returns the cached version instantly
- Cache Miss: When no match exists, your agent generates code normally, then Code Cache stores it for future use
- Verdict Scoring: Code that works gets 👍, code that fails gets 👎—retrieval improves over time
API Reference
The skill wraps these Raysurfer API methods:
| Method | Description |
|---|
search(task, top_k, min_verdict_score) | Unified search for cached code snippets |
get_code_files(task, top_k, cache_dir) | Get code files ready for sandbox execution |
upload_new_code_snips(task, files_written, succeeded, auto_vote) | Store new code after execution |
vote_code_snip(task, code_block_id, code_block_name, code_block_description, succeeded) | Vote on snippet usefulness |
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
- 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.
