auto-skill-loader 1.0.1

MCP server that auto-loads approved skills into agent sessions and provides MiniMax vision + search proxy tools

auto-skill-loader

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

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

auto-skill-loader is an MCP server that exposes your pre-approved skills via a skills://active resource. Instead of manually invoking skills or relying on fuzzy pattern matching, your agent reads this resource at session startup and automatically has all your approved skills in context.

How it works

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

Why

Most skill systems require the agent to:

• Explicitly call a use_skill tool, or
• Guess based on conversation patterns (unreliable)

auto-skill-loader solves this by using the MCP resource at session init pattern — deterministic, no guessing.

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	Status	Verified
Claude Code (macOS)	✅ Working	Vision tool + skill loading + MiniMax-M2.7 model
OpenCode (macOS)	✅ Working	Vision tool + skill loading + MiniMax Token Plan

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 will be updated.

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

1. Create skills directory (symlink to your existing skills):

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

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

active_skills:
  - vision-analysis
  - context-maintainer
  - markdown-mcp
skills_dir: ~/.config/auto-skill-loader/skills

3. 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

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

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

MINIMAX_TOKEN_PLAN_KEY=sk-cp-your-key-here

2. 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
    }
  }
}

3. 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.

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

Diagnosis

If you see "login fail" errors after setup:

1. Disable the broken minimax MCP — ensure "minimax-token-plan": { "enabled": false } or remove it entirely
2. Restart OpenCode completely — MCP servers are re-spawned on each session
3. 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.