Auto Skill Loader

Name: Auto Skill Loader
Author: divitkashyap

by divitkashyapversion 1.0.1i★ –iRuns on your computer

Registry nameio.github.divitkashyap/auto-skill-loader

Install

Overview

Description from the maker (original English)

Auto-loads skills into agent sessions. MiniMax vision and search proxy.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory
You need to fill in: MINIMAX_TOKEN_PLAN_KEY

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: MINIMAX_TOKEN_PLAN_KEY

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "auto-skill-loader": {
      "command": "uvx",
      "args": [
        "auto-skill-loader"
      ],
      "env": {
        "MINIMAX_TOKEN_PLAN_KEY": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add auto-skill-loader --env MINIMAX_TOKEN_PLAN_KEY=your-value -- uvx auto-skill-loader

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "auto-skill-loader": {
      "command": "uvx",
      "args": [
        "auto-skill-loader"
      ],
      "env": {
        "MINIMAX_TOKEN_PLAN_KEY": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "auto-skill-loader": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "auto-skill-loader"
      ],
      "env": {
        "MINIMAX_TOKEN_PLAN_KEY": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

MINIMAX_TOKEN_PLAN_KEY secret required env
MiniMax Token Plan API key from https://platform.minimax.io

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account divitkashyap, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 7251 servers in this category
Published to the registry more recently than 8% of the other 7251 servers in this category
Basic checks equal to or better than 100% of the other 7251 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Auto Skill Loader safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: MINIMAX_TOKEN_PLAN_KEY

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.0.1) reached the registry 7 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated today

Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 20 days ago

Install

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated last month

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

auto-skill-loader

Give your AI agent a persistent skill library it auto-loads at session start.

OpenCode image note: OpenCode doesn't yet expose real file paths when you paste images — the agent sees a filename instead. Workaround: give the agent an actual file path instead of pasting. Skills auto-load at session start as expected.

mcp-name: io.github.divitkashyap/auto-skill-loader

How it works

You configure which skills to auto-load in ~/.config/auto-skill-loader/config.yaml
The MCP server reads skill files from your skills directory and exposes them via skills://active
At session start your agent reads skills://active and gets all approved skills auto-injected
No explicit triggers needed — the agent already knows your skills

Why

The main problem: In OpenCode, skills don't auto-load — the agent has to explicitly invoke them or rely on unreliable fuzzy matching. auto-skill-loader solves this by exposing your skill library as an MCP resource that gets read at session start. Deterministic, no guessing.

Bonus: Our proxy tools also work around OpenCode's broken MCP stdio transport for the MiniMax vision and web search tools.

Installation

Option 1: uvx (recommended — no install needed)

uvx auto-skill-loader

Option 2: pip

pip install auto-skill-loader

auto-skill-loader

Option 3: Build from source

git clone https://github.com/divitkashyap/auto-skill-loader.git

cd auto-skill-loader pip install -e . auto-skill-loader

Configuration

OpenCode

Add to ~/.config/opencode/opencode.json:

"mcp": { "auto-skill-loader": { "type": "local", "command": ["uvx", "auto-skill-loader"], "enabled": true } } }

Claude Code

Note: Claude Code requires a specific JSON format via add-json:

claude mcp add-json -s user auto-skill-loader '{"type":"stdio","command":"/FULL/PATH/TO/python","args":["-m","server"],"env":{"MINIMAX_TOKEN_PLAN_KEY":"sk-cp-YOUR-KEY-HERE"}}'

Replace /FULL/PATH/TO/python with the path to your Python (e.g. /Users/YOU/auto-skill-loader/.venv/bin/python).

Or for uvx (requires network on first run):

claude mcp add -s user --transport stdio -e MINIMAX_TOKEN_PLAN_KEY=sk-cp-YOUR-KEY auto-skill-loader -- uvx auto-skill-loader

Tested On

Host	Skill Auto-Load	Vision Proxy	Image Paste	Notes
Claude Code (macOS)	✅	❌ Not needed	✅	Use `minimax-token-plan` MCP directly instead
OpenCode (macOS)	✅	✅ Works	❌ Broken	Give file paths instead of pasting

Skill auto-loading confirmed working on both hosts. The vision proxy bypasses OpenCode's broken stdio transport, but image paste-to-path is a separate rendering issue in OpenCode that requires using file paths.

Other MCP-compatible hosts (Cursor, Zed, etc.) should work with the same configuration — contributions welcome.

Platform Differences & Known Issues

Image Input: OpenCode vs Claude Code

Both hosts work with auto-skill-loader vision tools, but image input behaves differently:

Host	How images are passed	Recommended workflow
Claude Code	Images uploaded to URL automatically → tool receives URL	Paste image directly ✅ works
OpenCode	Inline images render visually but may not give tools a real path	Give a file path instead of pasting

OpenCode note: When you paste an image in OpenCode, it may render inline but the agent sees it as a filename string (e.g. logo.png) rather than a real filesystem path. This is a known OpenCode rendering behavior.

Workaround for OpenCode: Instead of pasting, give the agent the actual file path:

analyze this image: /path/to/your/image.png

The agent can access local files directly in OpenCode. If the image is only in your clipboard, the agent can extract it to /tmp/ first.

What We're Monitoring

We actively track the following OpenCode issues:

Inline image rendering (images pasted don't expose real paths to tools)
MCP stdio transport for local servers (our proxy tools work around this)
Session persistence of skills across restarts

If OpenCode releases a fix for inline image paths, this documentation and the vision-analysis skill will be updated to support direct paste workflow. We monitor OpenCode's changelog and release notes — check this repo for updates.

Other Known Issues

Issue	Severity	Workaround
OpenCode inline images show as filename, not path	Medium — affects paste workflow	Use file paths instead
Claude Code auth conflict (ANTHROPIC_AUTH_TOKEN vs managed key)	Low — cosmetic warning	Harmless, can be ignored
First vision call may take 3-5s (uvx download)	Low — one-time	Subsequent calls are ~200ms

Setup

Create skills directory (symlink to your existing skills):

mkdir -p ~/.config/auto-skill-loader

ln -sf ~/.config/opencode/skills ~/.config/auto-skill-loader/skills

Edit ~/.config/auto-skill-loader/config.yaml:

active_skills:

vision-analysis
context-maintainer
markdown-mcp

skills_dir: ~/.config/auto-skill-loader/skills

Restart your agent. It will now auto-load all listed skills at session start.

Tools

Tool	What it does
`list_skills`	List all available skills in skills_dir with descriptions
`activate_skill`	Add a skill to your approved list (persists to config.yaml)
`deactivate_skill`	Remove a skill from your approved list
`get_skill_info`	Get details about a specific skill
`get_active_skills`	List currently active skill names
`suggest_skills`	If no skills are active, suggests common ones to get started
`check_prerequisites`	Validate a skill's dependencies (MCP tools, API keys, env vars)

Bonus: MiniMax Vision & Web Search Proxy

auto-skill-loader also exposes two tools that proxy to minimax-coding-plan-mcp with a working stdio transport:

Tool	What it does
`minimax_understand_image`	Analyze images (JPEG, PNG, GIF, WebP up to 20MB)
`minimax_web_search`	Web search using MiniMax

Note: For vision specifically, mmx-cli is now the recommended approach — it's a direct REST call to MiniMax VLM, no MCP transport issues, and handles URLs natively. These proxy tools are useful if you want a unified MCP interface for both skill loading and vision.

The OpenCode MCP Bug

When OpenCode's built-in minimax-coding-plan-mcp MCP integration (minimax-token-plan) is configured, the understand_image tool fails with:

API Error: login fail: Please carry the API secret key in the 'Authorization' field

This happens even when:

✅ MINIMAX_API_KEY / MINIMAX_TOKEN_PLAN_KEY is set correctly
✅ API key is valid (same key works via direct API calls)
✅ Token Plan has available vision quota

Root cause: OpenCode's stdio transport for local MCP servers sends messages in a way that breaks the MCP protocol — likely batched writes without proper flush between JSON-RPC messages. Direct subprocess tests with sequential writes + flush() work fine.

The fix: Our proxy tools in auto-skill-loader use proper sequential stdio communication, bypassing OpenCode's broken transport layer.

Setup

Set your MiniMax Token Plan key in ~/.config/opencode/.env:

MINIMAX_TOKEN_PLAN_KEY=sk-cp-your-key-here

Add auto-skill-loader to ~/.config/opencode/opencode.json:

"mcp": { "auto-skill-loader": { "type": "local", "command": ["/path/to/venv/bin/python", "-m", "server"], "enabled": true } } }

Critical: If you have minimax-coding-plan-mcp configured directly in opencode.json (the minimax-token-plan entry), remove or disable it — its broken stdio transport will cause "login fail" errors. The proxy tools in auto-skill-loader replace it entirely.

Restart OpenCode and verify: /ask Do you have auto-skill-loader_minimax_understand_image available?

Diagnosis

If you see "login fail" errors after setup:

Disable the broken minimax MCP — ensure "minimax-token-plan": { "enabled": false } or remove it entirely
Restart OpenCode completely — MCP servers are re-spawned on each session
Check with: /ask Call minimax_understand_image with image_source="/any/real/image.png" and prompt="test"

Resources

Resource	What it does
`skills://active`	All approved skill contents concatenated — read by host at session init
`skills://config`	Your current config.yaml content

Security

User-controlled — only skills in config.yaml are loaded
No network fetches — everything is local
No prompt injection — skills come from your own configured directory

Repo Structure

auto-skill-loader/

├── src/ │ └── server.py # MCP server (Python stdlib + mcp package) ├── pyproject.toml # Package config ├── README.md # This file ├── SKILL.md # For agent onboarding └── LICENSE # MIT

Requirements

Python 3.9+
mcp package (pip install mcp)
pyyaml package (pip install pyyaml)

Or just use uvx auto-skill-loader which fetches dependencies automatically.

Auto Skill Loader

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

auto-skill-loader

How it works

Why

Installation

Option 1: uvx (recommended — no install needed)

Option 2: pip

Option 3: Build from source

Configuration

OpenCode

Claude Code

Tested On

Platform Differences & Known Issues

Image Input: OpenCode vs Claude Code

What We're Monitoring

Other Known Issues

Setup

Tools

Bonus: MiniMax Vision & Web Search Proxy

The OpenCode MCP Bug

Setup

Diagnosis

Resources

Security

Repo Structure

Requirements