lm-studio-private
- Repo stars 0
- Author updated Live
- Author repo nano-core
- Domain
- AI
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @0-CYBERDYNE-SYSTEMS-0 · no license declared
- Token usage
- Lean
- Setup complexity
- Plug-and-play
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- Python
- Permissions
-
- Read-only
- Write / modify
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools，默认拥有全部工具权限。
---
name: lm-studio-private
description: Private LLM and vision via LM Studio. Auto-loads models. For text chat, code, image analysis. De…
category: ai
runtime: Python
---
# lm-studio-private output preview
## PART A: Task fit
- Use case: Private LLM and vision via LM Studio. Auto-loads models. For text chat, code, image analysis. Default: mistralai/ministral-3-3b Use when the user request matches this skill's domain and capabilities. runs entirely locally; runs on Python. Works with Claude Code, Cursor, Cline and 23 more..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “When to use this skill / When not to use this skill / Quick Start” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Private LLM and vision via LM Studio. Auto-loads models. For text chat, code, image analysis. Default: mistralai/ministral-3-3b Use when the user request matches this skill's domain and capabilities. runs entirely locally; runs on Python. Works with Claude Code, Cursor, Cline and 23 more.”.
- **02** When the source has headings, the agent prioritizes “When to use this skill / When not to use this skill / Quick Start” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. Choose the trigger
The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Provide inputs and boundaries
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files.
Validate with a small run
Start with a small task and check whether the result follows “When to use this skill / When not to use this skill / Quick Start”. Inspect diffs, logs, previews, or tests before expanding scope.
Review before delivery
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: lm-studio-private
description: Private LLM and vision via LM Studio. Auto-loads models. For text chat, code, image analysis. De…
category: ai
source: 0-CYBERDYNE-SYSTEMS-0/nano-core
---
# lm-studio-private
## When to use
- Private LLM and vision via LM Studio. Auto-loads models. For text chat, code, image analysis. Default: mistralai/minis…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “When to use this skill / When not to use this skill / Quick Start” and keep inference separate from source facts.
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. Evidence boundary and execution chain
The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "lm-studio-private" {
input -> user goal + target files + boundaries + acceptance criteria
context -> When to use this skill / When not to use this skill / Quick Start
rules -> SKILL.md triggers / order / output contract
runtime -> Python | read files, write/modify files | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
}LM Studio Private Integration
When to use this skill
- Use when the user request matches this skill's domain and capabilities.
- Use when this workflow or toolchain is explicitly requested.
When not to use this skill
- Do not use when another skill is a better direct match for the task.
- Do not use when the request is outside this skill's scope.
Private LLM and vision via LM Studio. Auto-loads models automatically!
Quick Start
from client import lm_studio_chat, lm_studio_vision, lm_studio_health
# Chat - auto-loads model if needed
lm_studio_chat("Hello!")
# Vision - auto-loads model if needed
lm_studio_vision("/path/to/image.jpg", "What's in this image?")
# Check status
lm_studio_health()Features
- Auto-loading of models
- Auto-starts LM Studio if not running
- Vision analysis support
- Remote via Tailscale
Configuration
| Setting | Value |
|---|---|
| Host | 100.72.41.118 |
| Port | 1234 |
| Auto-load | Enabled |
| Default Model | mistralai/ministral-3-3b |
Functions
lm_studio_chat(prompt, model=None)- Chat with auto-loadlm_studio_vision(image_path, prompt, model=None)- Vision with auto-loadlm_studio_health()- Check statuslm_studio_models()- List models
Decide Fit First
Design Intent
How To Use It
Boundaries And Review