Auto Skill Loader

Name: Auto Skill Loader
Author: divitkashyap

作者：divitkashyap版本 1.0.1i★ –i在你的电脑上运行

注册名称io.github.divitkashyap/auto-skill-loader

安装

概览

作者提供的说明（英文原文）

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

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
使用情况i非常新↓ 14/周 · ★ –

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

连接方式: 在你的电脑上运行
传输方式: stdio
分类: 搜索与 AI 记忆
你需要填写：MINIMAX_TOKEN_PLAN_KEY

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：1 项中通过 1 项
在你的电脑上运行，拥有你的用户账户的权限（文件、网络、应用）。
需要你自己填写： MINIMAX_TOKEN_PLAN_KEY

标为机密的值是你账户的钥匙。千万不要分享给别人，也不要粘贴到聊天里。

你需要

PyPI已安装 Python 的 uv（用于 uvx 命令）

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config，把下面的内容粘贴到 claude_desktop_config.json

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

在终端中运行这条命令

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

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

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

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

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

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

这个服务器在你的电脑上运行，而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上，无需账户。

需要你填写的值

MINIMAX_TOKEN_PLAN_KEY 机密必填 env
MiniMax Token Plan API key from https://platform.minimax.io

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

它在你的电脑上运行；安装后，工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月9日

✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 divitkashyap 验证，该账户拥有此仓库
… 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
… 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
– 不适用
端点有响应
它在你的电脑上运行，没有互联网端点
– 不适用
使用 OAuth 登录
在你的电脑上运行，改用本地设置
… 尚未检查
工具描述中没有隐藏指令
它在你的电脑上运行；我们只能读取互联网端点的工具列表

与“搜索与 AI 记忆”中的其他服务器比较i

星标数多于此分类中其他 5751 个服务器的 0%
发布到注册中心的时间比此分类中其他 5751 个服务器的 9% 更新
基础检查结果等于或优于此分类中其他 5751 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

常见问题

Auto Skill Loader 安全吗？

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。

它需要哪些密钥或代码？

你需要填写：MINIMAX_TOKEN_PLAN_KEY

它在我的电脑上运行还是在云端？

它在你的电脑上运行。你的 AI 应用会在需要时启动它，数据不会经过作者的服务器（除非它自己调用在线服务）。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（1.0.1）于7个月前进入注册中心。

哪些应用可以使用它？

支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv)。

有什么可以替代它？

同一分类中有：Context7, Codebase Memory, Exa

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：↓ 每周下载量i★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Context7

Up-to-date docs for AI agents

作者：upstash广泛使用↓ 97.3万/周★ 6.4万3天前更新

电脑或云端

安装

Codebase Memory

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

作者：DeusData广泛使用↓ 1.8万/周★ 4.8万23天前更新

在你的电脑上运行

安装

Exa

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

作者：exa-labs广泛使用★ 52132个月前更新

在云端运行✓ exa.ai

安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

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

概览

PopNx MCP 的三大支柱

它能帮你做什么

安装

安装之前

需要你填写的值

它能做什么

基础检查i

作者与仓库一致

仍在维护

有许可证

端点有响应

使用 OAuth 登录

工具描述中没有隐藏指令

与“搜索与 AI 记忆”中的其他服务器比较i

常见问题

替代选择

Context7

Codebase Memory

Exa

作者的 README

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