Skill Market

llm config

Configure RuVLLM local inference with model selection, MicroLoRA fine-tuning, and SONA adaptation

GitHub
githubcommunityclaudemcp
0.0
0 installs73.4K GitHub starsby ruvnet

Skill Introduction

Overview
Configure RuVLLM local inference with model selection, MicroLoRA fine-tuning, and SONA adaptation

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 llm-config-443fd8
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Detail Preview

SKILL.md

Primary filemarkdown2 KB

name: llm-config description: Configure RuVLLM local inference with model selection, MicroLoRA fine-tuning, and SONA adaptation argument-hint: "[--model MODEL] [--adapter microlora|sona]" allowed-tools: mcp__claude-flow__ruvllm_generate_config mcp__claude-flow__ruvllm_status mcp__claude-flow__ruvllm_microlora_create mcp__claude-flow__ruvllm_microlora_adapt mcp__claude-flow__ruvllm_sona_create mcp__claude-flow__ruvllm_sona_adapt Bash

LLM Configuration

Configure RuVLLM for local inference and fine-tuning.

When to use

When you need to configure local LLM inference, create MicroLoRA adapters for task-specific fine-tuning, or set up SONA for real-time adaptation.

Steps

  1. Check status — call mcp__claude-flow__ruvllm_status to see current model and adapter state
  2. Generate config — call mcp__claude-flow__ruvllm_generate_config with model parameters
  3. Create MicroLoRA — call mcp__claude-flow__ruvllm_microlora_create for task-specific adapters
  4. Adapt MicroLoRA — call mcp__claude-flow__ruvllm_microlora_adapt with training data
  5. Create SONA — call mcp__claude-flow__ruvllm_sona_create for real-time neural adaptation
  6. Adapt SONA — call mcp__claude-flow__ruvllm_sona_adapt with feedback signals

MicroLoRA vs SONA

FeatureMicroLoRASONA
SpeedMinutes to train<0.05ms adaptation
ScopeTask-specific fine-tuningReal-time micro-adjustments
PersistenceSaved as adapter weightsSession-scoped
Use caseSpecialized domain tasksContinuous feedback loops

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