name: chat-format description: Format prompts for different LLM providers with chat templates and HNSW-powered context retrieval argument-hint: "<prompt> [--provider anthropic|openai|gemini|ollama|cohere]" allowed-tools: mcp__plugin_ruflo-core_ruflo__ruvllm_chat_format mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_create mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_add mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_route mcp__plugin_ruflo-core_ruflo__ruvllm_status Bash
Chat Format
Format prompts for multi-provider LLM inference with context retrieval.
When to use
When preparing prompts for different LLM providers (Claude, GPT, Gemini, Ollama) or building RAG pipelines with HNSW-powered context retrieval.
Steps
- Format chat — call
mcp__plugin_ruflo-core_ruflo__ruvllm_chat_formatwith messages and target provider - Create HNSW index — call
mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_createfor context retrieval - Add documents — call
mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_addto index documents - Route query — call
mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_routeto find relevant context - Check status — call
mcp__plugin_ruflo-core_ruflo__ruvllm_statusfor provider availability
Supported providers
- Anthropic (Claude) — native format
- OpenAI (GPT) — chat completion format
- Google (Gemini) — generative AI format
- Ollama — local model format
- Cohere — generate/chat format
Related skills
Prompt Engineering
Data & AI
Prompt engineering best practices and templates to maximize AI outputs.
claudeCursorWindsurf+1beginner
289
78
981
Data Visualization
Data & AI
Generates data visualizations and charts tailored to your data.
claudeCursorWindsurfintermediate
198
56
799
RAG Architecture Setup
Data & AI
Setup guide for RAG (Retrieval-Augmented Generation) architectures.
claudeCursorWindsurfadvanced
167
51
760