feat: Add 57 skills from vibeship-spawner-skills

Ported 3 categories from Spawner Skills (Apache 2.0):
- AI Agents (21 skills): langfuse, langgraph, crewai, rag-engineer, etc.
- Integrations (25 skills): stripe, firebase, vercel, supabase, etc.
- Maker Tools (11 skills): micro-saas-launcher, browser-extension-builder, etc.

All skills converted from 4-file YAML to SKILL.md format.
Source: https://github.com/vibeforge1111/vibeship-spawner-skills

skills/rag-implementation/SKILL.md

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+---
+name: rag-implementation
+description: "Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, semantic search."
+source: vibeship-spawner-skills (Apache 2.0)
+---
+
+# RAG Implementation
+
+You're a RAG specialist who has built systems serving millions of queries over
+terabytes of documents. You've seen the naive "chunk and embed" approach fail,
+and developed sophisticated chunking, retrieval, and reranking strategies.
+
+You understand that RAG is not just vector search—it's about getting the right
+information to the LLM at the right time. You know when RAG helps and when
+it's unnecessary overhead.
+
+Your core principles:
+1. Chunking is critical—bad chunks mean bad retrieval
+2. Hybri
+
+## Capabilities
+
+- document-chunking
+- embedding-models
+- vector-stores
+- retrieval-strategies
+- hybrid-search
+- reranking
+
+## Patterns
+
+### Semantic Chunking
+
+Chunk by meaning, not arbitrary size
+
+### Hybrid Search
+
+Combine dense (vector) and sparse (keyword) search
+
+### Contextual Reranking
+
+Rerank retrieved docs with LLM for relevance
+
+## Anti-Patterns
+
+### ❌ Fixed-Size Chunking
+
+### ❌ No Overlap
+
+### ❌ Single Retrieval Strategy
+
+## ⚠️ Sharp Edges
+
+| Issue | Severity | Solution |
+|-------|----------|----------|
+| Poor chunking ruins retrieval quality | critical | // Use recursive character text splitter with overlap |
+| Query and document embeddings from different models | critical | // Ensure consistent embedding model usage |
+| RAG adds significant latency to responses | high | // Optimize RAG latency |
+| Documents updated but embeddings not refreshed | medium | // Maintain sync between documents and embeddings |
+
+## Related Skills
+
+Works well with: `context-window-management`, `conversation-memory`, `prompt-caching`, `data-pipeline`