io.github.divitkashyap/auto-skill-loader

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

Open source Open in the app JSON README (API)

About

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

Details

Kind

MCP servers

Topic

No topic detected

Publisher

divitkashyap

Origin

official

Category

ferramentas

Transport

local

Version

1.0.1

Last push

2026-04-10T18:11:07Z

Repository state

ativo

Language

Python

License

MIT

Added

2026-08-29 03:02:43

Updated

2026-08-29 03:02:43

Origin id

io.github.divitkashyap/auto-skill-loader

README

# 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

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

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)

```bash
uvx auto-skill-loader
```

### Option 2: pip

```bash
pip install auto-skill-loader
auto-skill-loader
```

### Option 3: Build from source

```bash
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`:

```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`:

```bash
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):

```bash
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

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

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

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

```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 |

**Note:** For vision specifically, [mmx-cli](https://github.com/MiniMax-AI/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

1. Set your MiniMax Token Plan key in `~/.config/opencode/.env`:
```bash
MINIMAX_TOKEN_PLAN_KEY=sk-cp-your-key-here
```

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

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