A distributed local inference engine that routes code generation, review, and refactoring tasks across your personal hardware fleet without cloud dependencies.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install local-coding
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install local-coding using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Local Coding Assistant?
The Local Coding Assistant is a powerful Openclaw Skills integration designed to turn your hardware—from MacBooks to powerful workstations—into a private, distributed AI coding cluster. By leveraging the ollama-herd backbone, this skill allows developers to run frontier-level coding models like DeepSeek-Coder-V2 and Codestral on their own network. This ensures complete data privacy and eliminates the need for expensive GitHub Copilot or cloud API subscriptions.
Built for high-performance developer workflows, it provides an OpenAI-compatible endpoint that integrates seamlessly with your existing tools. Whether you are generating complex thread-safe algorithms or performing deep security audits on legacy codebases, the Local Coding Assistant intelligently routes requests to the best available machine in your fleet based on RAM and processing power.
Local Coding Assistant Use Cases
- Running high-parameter code models across multiple local machines to save on cloud API costs.
- Ensuring maximum privacy by keeping sensitive proprietary source code within your local network during AI refactoring.
- Integrating local LLMs with IDE extensions like Aider, Continue.dev, and Cline via a standardized API.
- Building a cross-platform dev environment that utilizes spare RAM on various devices (Mac, Linux, Windows) for inference.
How Local Coding Assistant Works
- The user installs the ollama-herd utility which acts as the core engine for the Local Coding Assistant.
- A central router is started on a primary machine to manage incoming coding requests.
- Fleet nodes are initialized on other networked devices, which automatically discover and register with the central router.
- Developers point their IDE tools or scripts to the local OpenAI-compatible endpoint (port 11435).
- The skill analyzes incoming requests and routes them to the node with the appropriate model and available VRAM for execution.
- Generated code, reviews, or refactored blocks are returned to the user's interface instantly.
Local Coding Assistant Setup
Install the required tools and start your local fleet with these commands:
# Install the core fleet manager
pip install ollama-herd
# Start the central router on your main machine
herd
# Run on every other device in your network to join the fleet
herd-node
Note: All model pulls require manual confirmation to prevent unexpected bandwidth or storage usage.
Local Coding Assistant Data Schema & Taxonomy
The skill manages its operations through a specific set of local files and endpoints:
| Type | Path / Endpoint | Description |
|---|---|---|
| Config | ~/.fleet-manager/latency.db |
Tracks performance and response times across nodes. |
| Logs | ~/.fleet-manager/logs/herd.jsonl |
Audit trail of all coding requests and routing decisions. |
| API | http://localhost:11435/v1 |
OpenAI-compatible endpoint for completions and chat. |
| State | http://localhost:11435/api/ps |
Real-time view of models currently loaded in memory. |
Local Coding Assistant Advanced Features
- Multi-agent support: Route different parts of a coding task to specialized models (e.g., Codestral for generation, DeepSeek for review).
- Cross-Platform Clustering: Seamlessly mix MacOS, Linux, and Windows devices within a single Openclaw Skills environment.
- Multimodal Capabilities: Beyond code, the fleet supports image generation (Z-Image-Turbo) and speech-to-text (Qwen3-ASR) via the same unified endpoint.
- Resource Optimization: Intelligent model selection based on device RAM, ranging from 3B parameters for 8GB devices to 236B MoE models for 128GB workstations.
SKILL.md
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