Skill: twinsgeeks/local-coding (version 1.0.1)

--- 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 ```bash 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 ```python from openai import OpenAI client = OpenAI(base_url="http://localhost:11435/v1", [REDACTED_SECRET]") 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 ```bash 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](https://github.com/geeks-accelerator/ollama-herd/blob/main/docs/guides/agent-setup-guide.md) — all 4 model types - [API Reference](https://github.com/geeks-accelerator/ollama-herd/blob/main/docs/api-reference.md) — 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.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal:  | SkillSpector: clean (LOW)