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twinsgeeks

Local Coding

by Twin Geeks · GitHub ↗ · v1.0.1 · MIT-0
darwinlinuxwindows ⚠ suspicious
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Install in OpenClaw
/install local-coding
Description
Local coding assistant — run DeepSeek-Coder, Codestral, StarCoder, and Qwen-Coder across your device fleet. Code generation, review, refactoring, and debuggi...
README (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.
Usage Guidance
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.
Capability Assessment
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.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install local-coding
  3. After installation, invoke the skill by name or use /local-coding
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
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.
Metadata
Slug local-coding
Version 1.0.1
License MIT-0
All-time Installs 2
Active Installs 2
Total Versions 2
Frequently Asked Questions

What is Local Coding?

Local coding assistant — run DeepSeek-Coder, Codestral, StarCoder, and Qwen-Coder across your device fleet. Code generation, review, refactoring, and debuggi... It is an AI Agent Skill for Claude Code / OpenClaw, with 125 downloads so far.

How do I install Local Coding?

Run "/install local-coding" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Local Coding free?

Yes, Local Coding is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Local Coding support?

Local Coding is cross-platform and runs anywhere OpenClaw / Claude Code is available (darwin, linux, windows).

Who created Local Coding?

It is built and maintained by Twin Geeks (@twinsgeeks); the current version is v1.0.1.

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