prompt-caching
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.
Works with
--- 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. license: MIT --- # Prompt Caching 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 ## Capabilities - prompt-cache - response-cache - kv-cache - cag-patterns - cache-invalidation ## Patterns ### Anthropic Prompt Caching Use Claude's native prompt caching for repeated prefixes ### Response Caching Cache full LLM responses for identical or similar queries ### Cache Augmented Generation (CAG) Pre-cache documents in prompt instead of RAG retrieval ## Anti-Patterns ### ❌ Caching with High Temperature ### ❌ No Cache Invalidation ### ❌ Caching Everything ## ⚠️ Sharp Edges | 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 | ## Related Skills Works well with: `context-window-management`, `rag-implementation`, `conversation-memory`
More Performance skills
seo-audit
coreyhaines31/marketingskills
When the user wants to audit, review, or diagnose SEO issues on their site. Also use when the user mentions "SEO audit," "technical SEO," "why am I not ranking," "SEO issues," "on-page SEO," "meta tags review," "SEO health check," "my traffic dropped," "lost rankings," "not showing up in Google," "site isn't ranking," "Google update hit me," "page speed," "core web vitals," "crawl errors," or "indexing issues." Use this even if the user just says something vague like "my SEO is bad" or "help with SEO" — start with an audit. For building pages at scale to target keywords, see programmatic-seo. For adding structured data, see schema. For AI search optimization, see ai-seo.
competitor-profiling
coreyhaines31/marketingskills
When the user wants to research, profile, or analyze competitors from their URLs. Also use when the user mentions 'competitor profile,' 'competitor research,' 'competitor analysis,' 'profile this competitor,' 'analyze competitor,' 'competitive intelligence,' 'competitor deep dive,' 'who are my competitors,' 'competitor landscape,' 'competitor dossier,' 'competitive audit,' or 'research these competitors.' Input is a list of competitor URLs. Output is structured competitor profile markdown files. For creating comparison/alternative pages from profiles, see competitors. For sales-specific battle cards, see sales-enablement.
vercel-optimize
vercel-labs/agent-skills
Use for Vercel cost and performance optimization on deployed projects, especially Next.js, SvelteKit, Nuxt, and limited Astro apps. Collect Vercel metrics, usage, project config, and code scan results first; investigate only metric-backed candidates; produce ranked recommendations grounded in verified files and version-aware Vercel/framework docs. Trigger for Vercel bill reduction, slow or expensive routes, caching opportunities, Function Invocations, Build Minutes, Fast Data Transfer, Core Web Vitals, Bot Management, Fluid compute, or cost breakdown requests.
