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🦀 ClawHub

Local Coding

by @twinsgeeks

Local coding assistant — run DeepSeek-Coder, Codestral, StarCoder, and Qwen-Coder across your device fleet. Code generation, review, refactoring, and debuggi...

Versionv1.0.1
Downloads321
Installs2
Stars⭐ 1
#clawhub
TERMINAL
clawhub install local-coding

📖 About This Skill

name: local-coding description: Local coding assistant — run DeepSeek-Coder, Codestral, StarCoder, and Qwen-Coder across your device fleet. Code generation, review, refactoring, and debugging routed to the best available machine. Works with Aider, Continue.dev, Cline, and any OpenAI-compatible coding tool. No cloud API costs, all code stays local. version: 1.0.1 homepage: https://github.com/geeks-accelerator/ollama-herd metadata: {"openclaw":{"emoji":"keyboard","requires":{"anyBins":["curl","wget"],"optionalBins":["python3","pip"]},"configPaths":["~/.fleet-manager/latency.db","~/.fleet-manager/logs/herd.jsonl"],"os":["darwin","linux","windows"]}}

Local Coding Assistant — Code Models Across Your Fleet

Run the best open-source coding models on your own hardware. DeepSeek-Coder, Codestral, StarCoder, and Qwen-Coder routed across your devices — the fleet picks the best machine for every code generation request.

Your code never leaves your network. No GitHub Copilot subscription, no cloud API costs.

Coding models available

| Model | Parameters | Ollama name | Strengths | |-------|-----------|-------------|-----------| | Codestral | 22B | codestral | 80+ languages, fill-in-the-middle, Mistral's code specialist | | DeepSeek-Coder-V2 | 236B MoE (21B active) | deepseek-coder-v2 | Matches GPT-4 Turbo on code tasks | | DeepSeek-Coder | 6.7B, 33B | deepseek-coder:33b | Purpose-built for code (87% code training data) | | Qwen2.5-Coder | 7B, 32B | qwen2.5-coder:32b | Strong multi-language code generation | | StarCoder2 | 3B, 7B, 15B | starcoder2:15b | Trained on The Stack v2, 600+ languages | | CodeGemma | 7B | codegemma | Google's code-focused Gemma variant |

Quick start

pip install ollama-herd    # PyPI: https://pypi.org/project/ollama-herd/
herd                       # start the router (port 11435)
herd-node                  # run on each device — finds the router automatically

No models are downloaded during installation. All pulls require user confirmation.

Code generation

Write new code

from openai import OpenAI
client = OpenAI(base_url="http://localhost:11435/v1", api_key="not-needed")
response = client.chat.completions.create(
    model="codestral",
    messages=[{"role": "user", "content": "Write a thread-safe LRU cache in Python with TTL support"}],
)
print(response.choices[0].message.content)

Code review

curl http://localhost:11435/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-coder-v2:16b",
    "messages": [{"role": "user", "content": "Review this code for bugs and security issues:\n\n
python\ndef process_payment(amount, card_number):\n ...\n``"}] }' 


Refactoring
bash curl http://localhost:11435/api/chat -d '{ "model": "qwen2.5-coder:32b", "messages": [{"role": "user", "content": "Refactor this to use async/await: ..."}], "stream": false }' 


Works with your IDE tools
The fleet exposes an OpenAI-compatible API at http://localhost:11435/v1. Point any coding tool at it:
| Tool | Config |
|------|--------|
| Aider | aider --openai-api-base http://localhost:11435/v1 --model codestral |
| Continue.dev | Set API base to http://localhost:11435/v1 in VS Code settings |
| Cline | Set provider to OpenAI-compatible, base URL http://localhost:11435/v1 |
| Open WebUI | Set Ollama URL to http://localhost:11435 |
| LangChain | ChatOpenAI(base_url="http://localhost:11435/v1", model="codestral") |
Pick the right model for your RAM
> Cross-platform: These are example configurations. Any device (Mac, Linux, Windows) with equivalent RAM works.
| Device | RAM | Best coding model |
|--------|-----|------------------|
| MacBook Air (8GB) | 8GB | starcoder2:3b or deepseek-coder:6.7b |
| Mac Mini (16GB) | 16GB | codestral or starcoder2:15b |
| Mac Mini (32GB) | 32GB | qwen2.5-coder:32b or deepseek-coder:33b |
| Mac Studio (128GB) | 128GB | deepseek-coder-v2 — frontier code quality |
Check what's running
bash

Models loaded in memory

curl -s http://localhost:11435/api/ps | python3 -m json.tool

All available models

curl -s http://localhost:11435/api/tags | python3 -m json.tool

Recent coding request traces

curl -s "http://localhost:11435/dashboard/api/traces?limit=5" | python3 -m json.tool 


Also available on this fleet
General-purpose LLMs

Llama 3.3, Qwen 3.5, DeepSeek-R1, Mistral Large — for non-code tasks through the same endpoint.
Image generation
bash curl http://localhost:11435/api/generate-image \ -d '{"model": "z-image-turbo", "prompt": "developer workspace illustration", "width": 512, "height": 512}' 


Speech-to-text
bash curl http://localhost:11435/api/transcribe -F "file=@standup.wav" -F "model=qwen3-asr" `

Full documentation

  • Agent Setup Guide — all 4 model types
  • API Reference — complete endpoint docs

    • Guardrails

  • Model downloads require explicit user confirmation — coding models range from 2GB to 130GB+. Always confirm before pulling.
  • Model deletion requires explicit user confirmation.
  • Never delete or modify files in ~/.fleet-manager/`.
  • No models are downloaded automatically — all pulls are user-initiated or require opt-in.
  • Your code stays local — no prompts or generated code leave your network.

    • 💡 Examples

    pip install ollama-herd    # PyPI: https://pypi.org/project/ollama-herd/
herd                       # start the router (port 11435)
herd-node                  # run on each device — finds the router automatically

    No models are downloaded during installation. All pulls require user confirmation.