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Hierarchical Skills For AI Server

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A hierarchical MCP server for managing skill definitions with a browsable tree structure and full-text search. It allows AI agents to efficiently discover and u

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Описание

A hierarchical MCP server for managing skill definitions with a browsable tree structure and full-text search. It allows AI agents to efficiently discover and use skills without consuming context tokens.

README

A hierarchical MCP (Model Context Protocol) server for managing skill definitions. Skills are stored as folders containing SKILL.md with YAML frontmatter, organized in a browsable tree structure with full-text search and a web UI.

When to Use / When Not to Use

Use this when

  • You have many skills (100+) and including all their frontmatter in the AI's context window would waste tokens
  • You want structured, hierarchical organization of skills by domain (coding, security, writing...)
  • You want search + browse so the AI can find the right skill without knowing the exact path
  • You want usage tracking so popular skills naturally rank higher over time
  • You want a web UI for humans to browse the same skill tree

Don't use this when

  • You have few skills (< 15) — just install them as per your agents' instructions, no server needed
  • Your skills are ephemeral or one-shot — the server setup overhead isn't worth it
  • You need real-time editing or CRUD from the web UI — it's read-only by design
  • You need multi-user auth or permissions — not built for that
  • You need relational queries or a database backend — this is filesystem + in-memory index

Architecture

  Agent (MCP client)             Human (browser)
       ↓                              ↓
Skills MCP Server ← python mcp_server.py --port 8080
       │                              │
       ├── MCP (stdio)                ├── HTTP (web UI)
       │   browse(path, depth=1)      │   GET /api/browse?path=&depth=
       │   read(path)                 │   GET /api/search?q=
       │   search(query)              │   GET /api/read?path=
       │   info(path)                 │   GET /api/info?path=
       │   use(path)                  │
       │   steps(path)                │
       │   reload()                   │
       │                              │
       └──────────────────────────────┘
                      ↓
          ┌───────────┼───────────┐
          ↓           ↓           ↓
     File system    Index     Usage stats
     (SKILL.md)   (tags,     (uses count,
                   aliases,   last_used)
                   body,
                   relations)

Quick Start

# Install
python3 -m venv venv
source venv/bin/activate
pip install -e .

# Run with sample skills (activate venv first)
python mcp_server.py --port 8080

# Open web UI
open http://localhost:8080

CLI Arguments

Argument Required Default Description
--repo No sample_skills Path to root folder containing grouped skills
--max-depth No 4 Maximum folder depth to walk
--port No 8080 Port for web UI
--host No localhost Host for web UI

MCP Tools

Tool Input Output Description
browse(path, depth=1) "", "coding", "coding/python" Child groups + skills (nested if depth>1) Navigate the skill hierarchy
read(path) "coding/python/fastapi" Full SKILL.md body Load skill content
search(query) "fastapi", "web" Ranked paths Search by name, tags, aliases, description, path segments
info(path) "coding/python/fastapi" YAML metadata (no body) Lightweight skill inspection
use(path) "coding/python/fastapi" Updated metadata Track skill usage (boosts search ranking)
steps(path) "coding/python/fastapi" Ordered step list Get workflow sub-skills
reload() Status Re-scan repo, re-validate, re-index

Skill File Format

Each skill lives in its own folder containing a SKILL.md file with YAML frontmatter delimited by ---.

Folder structure rules

skills/
  coding/                      # group folder (has subfolders, no SKILL.md)
    python/
      fastapi/                 # skill folder → contains SKILL.md
        SKILL.md
      pytest/
        SKILL.md
    rust/
      tokio/
        SKILL.md
  • A folder is a skill if it contains SKILL.md
  • A folder is a group if it has subfolders but no SKILL.md
  • A folder can be dual (both group + skill) — has SKILL.md AND subfolders
  • Folders beyond --max-depth are silently ignored
  • Empty folders (no SKILL.md, no subfolders) are silently ignored
  • Use lowercase with hyphens for folder names: web-scraping
  • All path lookups are case-insensitive"Coding/Python" and "coding/python" resolve to the same node
  • Case collisions are rejected at startup: having both Coding/ and coding/ folders will abort the server — use consistent casing

Frontmatter fields

Field Required Type Description
name Yes string Unique identifier across the entire tree. Does not need to match folder name.
tags No list Keywords for search: [python, api, web]
aliases No list Alternative names an AI might search by: [fast api, fastapi framework]
description No string One or two sentences explaining the skill. Indexed for search.
depends_on No list of paths Prerequisites the AI should know first: [python/basics]
related No list of paths Conceptually similar skills: [flask, starlette]
followed_by No list of paths Natural next skills after mastering this one
steps No list of paths Ordered sub-skill paths for multi-step workflows
uses Auto integer Usage counter — auto-incremented by use() tool
last_used Auto string or null ISO timestamp — auto-set by use() tool

Example

---
name: fastapi
tags: [python, api, web]
aliases: [fast api, fastapi framework]
description: Python web framework for building APIs
depends_on: [python/basics]
related: [flask, starlette]
followed_by: [sqlalchemy, pytest]
uses: 0
last_used: null
steps: [setup/project-scaffold, coding/python/fastapi/routing]
---

After the closing ---, write standard Markdown body content. The server never modifies the body — only uses and last_used in the frontmatter are updated automatically.

Validation rules

On startup and reload() the server validates every skill:

  • SKILL.md must exist and have valid YAML frontmatter
  • name field is required and must be unique across the entire tree
  • Duplicate names cause an abort with per-skill error logging
  • Folders with case-colliding paths (e.g. Coding/ and coding/) cause an abort — use consistent casing
  • Folders beyond --max-depth are silently ignored

Hierarchy guidelines

  • Keep --max-depth between 3 and 5 levels
  • Aim for 5–15 children per group node; split into sub-groups if exceeding 20
  • Use dual nodes (folder with SKILL.md + subfolders) when a group has general content applying to all children
  • Prefer 3–8 tags per skill, lowercase, singular form

Project Structure

skills-mcp-server/
├── mcp_server.py           # Root-level entry point
├── pyproject.toml          # Project configuration
├── src/
│   ├── __init__.py         # Package init
│   ├── __main__.py         # Entry point
│   ├── main.py             # CLI + server orchestration
│   ├── models.py           # Data models
│   ├── frontmatter.py      # YAML frontmatter parser
│   ├── discovery.py        # Folder walker
│   ├── tree.py             # In-memory skill tree
│   ├── index.py            # Search index
│   ├── mcp_server.py       # MCP tool definitions
│   ├── http_server.py      # HTTP server for web UI
│   └── static/
│       └── index.html      # Web UI (vanilla HTML/CSS/JS)
├── tests/
│   ├── test_discovery.py   # Tests for discovery
│   ├── test_frontmatter.py # Tests for YAML parsing
│   └── test_tools.py       # Tests for tree tools & search
├── sample_skills/          # Demo skills
├── HANDOFF.md              # Session handoff notes
├── AGENTS.md               # Agent conventions
└── IMPLEMENTATION_PLAN.md  # Full implementation plan

Design Principles

  • Hierarchy for humans, search for models — tree browsing and full-text search coexist
  • Filesystem as source of truth — no database needed
  • Minimal dependencies — only mcp and pyyaml
  • Read-only web UI — no editing, no auth, no build step
  • Self-improving index — usage tracking boosts popular/recent skills in search results

Tests

python -m pytest tests/ -v

from github.com/Aunxfb/HierarchicalSkillsForAI

Установка Hierarchical Skills For AI Server

У этого сервера нет опубликованного пакета — он собирается из исходников. Открой репозиторий и следуй инструкции в README.

▸ github.com/Aunxfb/HierarchicalSkillsForAI

FAQ

Hierarchical Skills For AI Server MCP бесплатный?

Да, Hierarchical Skills For AI Server MCP бесплатный — установка в пару кликов через Unyly без оплаты.

Нужен ли API-ключ для Hierarchical Skills For AI Server?

Нет, Hierarchical Skills For AI Server работает без API-ключей и переменных окружения.

Hierarchical Skills For AI Server — hosted или self-hosted?

Self-hosted: сервер запускается локально на твоей машине командой из раздела установки.

Как установить Hierarchical Skills For AI Server в Claude Desktop, Claude Code или Cursor?

Открой Hierarchical Skills For AI Server на unyly.org, выбери вкладку своего клиента (Claude Desktop, Claude Code, Cursor) и нажми Install — конфиг сгенерируется автоматически, без правки JSON.

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