Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Code Cache
Semantic code caching for AI agents. Cache, retrieve, and reuse code from prior agent executions.
skill openclawclawhub Free
Semantic code caching for AI agents. Cache, retrieve, and reuse code from prior agent executions.
β¬ 0 downloads β
0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- README.md, SKILL.md, code_cache.py, tests/test_skill.py
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- ryx2
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
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 itβsaving 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"
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 β Maximum number of results (default: 5) --min-score FLOAT β Minimum verdict score filter (default: 0.3) --show-code β 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 β Maximum number of files (default: 5) --cache-dir DIR β 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 β Files to upload (required, can specify multiple) --failed β Mark the execution as failed (default: succeeded) --no-auto-vote β 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 β Upvote / thumbs up (default) --down β Downvote / thumbs down --task β Original task description (optional) --name β Code block name (optional) --description β 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 π, code that fails gets πβretrieval improves over time
API Reference
The skill wraps these Raysurfer API methods: MethodDescriptionsearch(task, top_k, min_verdict_score)Unified search for cached code snippetsget_code_files(task, top_k, cache_dir)Get code files ready for sandbox executionupload_new_code_snips(task, files_written, succeeded, auto_vote)Store new code after executionvote_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.
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs2 Scripts
- SKILL.md
- README.md
- code_cache.py
- tests/test_skill.py