/chat-format
Format prompts for different LLM providers with chat templates and HNSW-powered context retrieval
$ npx -y skills add ruvnet/ruflo --skill chat-format --agent claude-codeHow it fires
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- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
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- Slash command
/chat-format
Context preview
The summary Claude sees to decide when to auto-load this skill.
Format prompts for different LLM providers with chat templates and HNSW-powered context retrieval
SKILL.md
chat-format.SKILL.mdname: 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
1. **Format chat** — call `mcp__plugin_ruflo-core_ruflo__ruvllm_chat_format` with messages and target provider 2. **Create HNSW index** — call `mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_create` for context retrieval 3. **Add documents** — call `mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_add` to index documents 4. **Route query** — call `mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_route` to find relevant context 5. **Check status** — call `mcp__plugin_ruflo-core_ruflo__ruvllm_status` for 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
Read more
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
1. **Format chat** — call `mcp__plugin_ruflo-core_ruflo__ruvllm_chat_format` with messages and target provider 2. **Create HNSW index** — call `mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_create` for context retrieval 3. **Add documents** — call `mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_add` to index documents 4. **Route query** — call `mcp__plugin_ruflo-core_ruflo__ruvllm_hnsw_route` to find relevant context 5. **Check status** — call `mcp__plugin_ruflo-core_ruflo__ruvllm_status` for 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
An agent meta-harness for Claude Code and Codex. Agent = Model + Harness. The model writes; the harness gives it tools, memory, loops, sandboxes, and controls so it can actually work.
Repo: ruvnet/ruflo
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