Skill Market

chat format

Format prompts for different LLM providers with chat templates and HNSW-powered context retrieval

GitHub
githubcommunityclaudemcp
0.0
0 installs73.4K GitHub starsby ruvnet

Skill Introduction

Overview
Format prompts for different LLM providers with chat templates and HNSW-powered context retrieval

Core value

Turns reusable Other know-how into an installable skill, helping users complete github, community, claude, mcp work faster.

Target users

  • Developers, testers, and maintainers who handle Other tasks in Focus Code.
  • Teams that already trust workflows or content from ruvnet.
  • Users who want standardized prompts, steps, or conventions instead of repeating setup work.

Best practices

  • Read the skill content first to confirm required inputs, expected outputs, and dependencies.
  • Try it on a small task before relying on it for critical work.
  • Add project-specific constraints such as coding style, target platform, test expectations, and delivery format.
  • For external sources, verify the source link, version, and recent maintenance activity.

Best use cases

  • Tasks related to github, community, claude, mcp that need a reusable execution flow.
  • Converting a community repo, team convention, or personal workflow into day-to-day assistance.
  • Starting from a proven skill instead of writing prompts or procedures from scratch.

Limits and boundaries

  • Results depend on the quality of the original skill content and may need human correction.
  • It does not replace code review, tests, security review, or professional judgment.
  • External tools, APIs, account permissions, and local dependencies still need separate setup.

Differentiation

  • Structured around Other, making it easier to discover and reuse than loose prompt snippets.
  • Marked as GitHub, which helps users judge trust and maintenance expectations.
  • Keeps the original source link available for repository, documentation, or discussion follow-up.
  • Tagged with github, community, claude, mcp, so it can be filtered by concrete task intent.

Install and use

Install
Copy Install Command
focus install chat-format-e3ca5d
View source

Detail Preview

SKILL.md

Primary filemarkdown2 KB

name: chat-format description: Format prompts for different LLM providers with chat templates and HNSW-powered context retrieval argument-hint: "<prompt> [--provider anthropic|openai|local]" allowed-tools: mcp__claude-flow__ruvllm_chat_format mcp__claude-flow__ruvllm_hnsw_create mcp__claude-flow__ruvllm_hnsw_add mcp__claude-flow__ruvllm_hnsw_route mcp__claude-flow__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__claude-flow__ruvllm_chat_format with messages and target provider
  2. Create HNSW index — call mcp__claude-flow__ruvllm_hnsw_create for context retrieval
  3. Add documents — call mcp__claude-flow__ruvllm_hnsw_add to index documents
  4. Route query — call mcp__claude-flow__ruvllm_hnsw_route to find relevant context
  5. Check status — call mcp__claude-flow__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

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