agent-management
Create, manage, and orchestrate AI agents using the AI Maestro CLI. Use when the user asks to "create agent", "list agents",…
Caching strategies for LLM prompts including Anthropic prompt caching, response caching, and CAG (Cache Augmented Generation) Use when: prompt caching, cache prompt, response cache, cag, cache augmented.
Claude Code
$ npx -y skills add davila7/claude-code-templates --skill prompt-caching --agent claude-code
Codex
$ npx skills add davila7/claude-code-templates --skill prompt-caching -a codex -y
Cursor
$ npx skills add davila7/claude-code-templates --skill prompt-caching -a cursor -y
Gemini CLI
$ npx skills add davila7/claude-code-templates --skill prompt-caching -a gemini-cli -y
GitHub Copilot
$ npx skills add davila7/claude-code-templates --skill prompt-caching -a github-copilot -y
OpenCode
$ npx skills add davila7/claude-code-templates --skill prompt-caching -a opencode -y
Windsurf
$ npx skills add davila7/claude-code-templates --skill prompt-caching -a windsurf -y
name: prompt-caching description: "Caching strategies for LLM prompts including Anthropic prompt caching, response caching, and CAG (Cache Augmented Generation) Use when: prompt caching, cache prompt, response cache, cag, cache augmented." metadata: source: vibeship-spawner-skills (Apache 2.0)
You're a caching specialist who has reduced LLM costs by 90% through strategic caching. You've implemented systems that cache at multiple levels: prompt prefixes, full responses, and semantic similarity matches.
You understand that LLM caching is different from traditional caching—prompts have prefixes that can be cached, responses vary with temperature, and semantic similarity often matters more than exact match.
Your core principles: 1. Cache at the right level—prefix, response, or both 2. K
Use Claude's native prompt caching for repeated prefixes
Cache full LLM responses for identical or similar queries
Pre-cache documents in prompt instead of RAG retrieval
| Issue | Severity | Solution | |-------|----------|----------| | Cache miss causes latency spike with additional overhead | high | // Optimize for cache misses, not just hits | | Cached responses become incorrect over time | high | // Implement proper cache invalidation | | Prompt caching doesn't work due to prefix changes | medium | // Structure prompts for optimal caching |
Works well with: `context-window-management`, `rag-implementation`, `conversation-memory`
Open-source registry and CLI for Claude Code components: agents, commands, skills, hooks, MCPs, settings and mods
Create, manage, and orchestrate AI agents using the AI Maestro CLI. Use when the user asks to "create agent", "list agents",…
Send and receive cryptographically signed messages between AI agents using the Agent Messaging Protocol (AMP). Use when the user…
Search auto-generated codebase documentation for function signatures, API docs, class definitions, and code comments. Use when…
Query the code graph database to understand component relationships, dependencies, and change impact. Use when the user asks to…
Search conversation history and semantic memory to recall previous discussions, decisions, and context. Use when the user asks to…
You MUST use this before any creative work - creating features, building components, adding functionality, modifying behavior, or…
Ask which skill or flow fits your situation. A router over the skills in this repo.