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Local Coding

作者 Twin Geeks · GitHub ↗ · v1.0.1 · MIT-0
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在 OpenClaw 中安装
/install local-coding
功能描述
Local coding assistant — run DeepSeek-Coder, Codestral, StarCoder, and Qwen-Coder across your device fleet. Code generation, review, refactoring, and debuggi...
使用说明 (SKILL.md)

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:\
\
```python\
def process_payment(amount, card_number):\
    ...\
```"}]
  }'

Refactoring

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

# 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

curl http://localhost:11435/api/generate-image \
  -d '{"model": "z-image-turbo", "prompt": "developer workspace illustration", "width": 512, "height": 512}'

Speech-to-text

curl http://localhost:11435/api/transcribe -F "[email protected]" -F "model=qwen3-asr"

Full documentation

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.
安全使用建议
This skill appears to implement a local fleet router for code models, which can legitimately read fleet status and traces — but those traces may contain snippets of user code or secrets. Before installing or running: 1) Inspect the upstream repository (https://github.com/geeks-accelerator/ollama-herd) and review the ollama-herd PyPI package contents and recent commits; prefer a pinned release or source you trust. 2) Verify what data the local endpoints (/dashboard/api/traces, /api/ps, log paths) expose and who can access them on your network; restrict access if they contain sensitive code. 3) Confirm the listed config paths are correct and intended; the SKILL.md metadata mentioning ~/.fleet-manager conflicts with the registry summary. 4) If you will pip-install paket on multiple devices, audit the package and run in a controlled environment first. If you need a cleaner safety posture, ask for a version that documents exactly which local files/endpoints it reads and add network access controls for the router.
能力评估
Purpose & Capability
The name/description (local coding across a fleet) matches the instructions (start a router, run herd-node, expose an OpenAI-compatible local API). Requiring curl/wget and optionally python/pip is reasonable. However the SKILL.md metadata lists config paths under ~/.fleet-manager which implies the skill expects access to local fleet state; the registry summary earlier reported 'Required config paths: none' — this mismatch is noteworthy.
Instruction Scope
Runtime instructions instruct use of local endpoints that can return recent request traces and running-model state (e.g., /dashboard/api/traces, /api/ps). Those endpoints can contain user code and potentially secrets from prior requests. While accessing them is coherent for a fleet router, it raises clear privacy risk: the skill's operation includes reading sensitive artifacts (request traces/logs) from the fleet.
Install Mechanism
This is an instruction-only skill (no install spec). The docs show users should run 'pip install ollama-herd' (PyPI). Installing from PyPI is common, but pip-installed packages execute arbitrary code and should be audited before installation; no signed release or pinned URL is provided in the SKILL.md.
Credentials
The skill declares no required environment variables or credentials, which is good. However SKILL.md metadata lists configPaths (e.g., ~/.fleet-manager/latency.db and ~/.fleet-manager/logs/herd.jsonl) that expose local logs/state. Requesting access to logs/traces is proportionate to a fleet manager but also grants access to potentially sensitive user code; the manifest/registry inconsistency about config paths increases uncertainty.
Persistence & Privilege
The skill does not ask for always:true or elevated persistent privileges. It's user-invocable and allows autonomous invocation (platform default). There is no install spec that writes to system-wide locations in the skill bundle itself.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install local-coding
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /local-coding 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.1
Cross-platform support: macOS, Linux, and Windows. Updated OS metadata, descriptions, and hardware recommendations.
v1.0.0
- Initial release of the Local Coding skill. - Run DeepSeek-Coder, Codestral, StarCoder, and Qwen-Coder across your device fleet with optimized routing. - Supports code generation, review, refactoring, and debugging via a local OpenAI-compatible API. - Works seamlessly with Aider, Continue.dev, Cline, Open WebUI, LangChain, and other coding tools. - No cloud API costs, no code leaves your network; all model downloads require explicit user confirmation.
元数据
Slug local-coding
版本 1.0.1
许可证 MIT-0
累计安装 2
当前安装数 2
历史版本数 2
常见问题

Local Coding 是什么?

Local coding assistant — run DeepSeek-Coder, Codestral, StarCoder, and Qwen-Coder across your device fleet. Code generation, review, refactoring, and debuggi... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 125 次。

如何安装 Local Coding?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install local-coding」即可一键安装,无需额外配置。

Local Coding 是免费的吗?

是的,Local Coding 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Local Coding 支持哪些平台?

Local Coding 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(darwin, linux, windows)。

谁开发了 Local Coding?

由 Twin Geeks(@twinsgeeks)开发并维护,当前版本 v1.0.1。

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