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MCP-skill-library-dynamic

by Guslaier
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MCP-skill-library-dynamic 🚀

Сервер MCP (Model Context Protocol) корпоративного уровня, который предоставляет файлы навыков и правил из локального каталога AI-агентам по запросу. Он позволяет AI-агентам динамически перечислять и получать конкретные инструкции по навыкам, отформатированные в Markdown, без раздувания контекста.

✨ Возможности (v1.2.0)

  • Нативный TypeScript: 100% типобезопасный, компилируется в оптимизированные ES-модули.

  • Курируемая база из 200+ основных навыков: высококачественный, дедуплицированный и соответствующий стандартам сообщества набор навыков, предварительно индексированный в skills-base.json.

  • Детерминированный загрузчик с офлайн-приоритетом: быстрые и надежные пакетные загрузки непосредственно из курируемого базового списка с помощью npx skills add и экспоненциальной задержкой при повторных попытках.

  • Умный поиск и фильтрация: list_skills поддерживает необязательный параметр query (например, list_skills({ query: "react" })) для непосредственной фильтрации навыков и экономии токенов.

  • Рекурсивная агрегация правил из нескольких файлов: fetch_skill_rule автоматически сканирует и объединяет все вложенные Markdown-файлы (например, rules/*.md, references/*.md), не пропуская более глубокую документацию.

  • Автоматическое разрешение путей без настройки: интеллектуально определяет .agents/skills относительно скомпилированного расположения сервера, устраняя проблемы ручной настройки SKILLS_DIR.

  • Устранение призрачных навыков: автоматически игнорирует пустые каталоги и обслуживает только папки с проверенными файлами правил .md.

  • Безопасное кэширование памяти: использует TTL-кэш для списка каталогов и асинхронный ввод-вывод для чтения файлов, что гарантирует 0% вероятность сбоев из-за нехватки памяти (OOM), даже при наличии 100 000+ навыков.

  • Защита от обхода пути: криптографическое разрешение путей для строгой изоляции AI в каталоге .agents/skills/.

  • Корректное завершение работы: правильно обрабатывает SIGINT/SIGTERM и неперехваченные исключения для обеспечения чистого закрытия сокетов MCP.

  • Архитектура экономии токенов (снижение затрат):

    • list_skills возвращает только имена папок (слаги), сводя вводимый контекст к ~1 500 токенам даже при наличии 1 000+ навыков.

    • fetch_skill_rule строго получает контент по запросу, по одному навыку за раз, предотвращая галлюцинации AI и огромные счета за API.

Related MCP server: docubridge

📦 Установка и сборка

  1. Клонируйте или скачайте репозиторий:

git clone https://github.com/Guslaier/MCP-skill-library-dynamic.git
cd skill-library-mcp

  1. Установите зависимости:

npm install

  1. Скомпилируйте исходный код TypeScript:

npm run build

🧠 Загрузка и управление навыками

Навыки загружаются в .agents/skills/, расположенный в корне проекта (или настраивается через SKILLS_DIR).

Курируемая база навыков (skills-base.json)

Загрузчик использует skills-base.json как единственный источник истины, содержащий 200+ курируемых и дедуплицированных навыков из официальных и ведущих репозиториев (anthropics/skills, obra/superpowers, affaan-m/ecc, browser-use и т.д.).

Команды CLI

Команда

Описание

npm run download

Загрузить и установить все 200+ базовых навыков

npm run download -- <skill-name>

Загрузить только навыки, соответствующие указанному фильтру имени

Примеры

# Download all 200+ curated base skills
npm run download

# Download only specific skills matching 'caveman'
npm run download -- caveman

# Download all React / Frontend related skills
npm run download -- react

📁 Структура каталога

skill-library-mcp/
├── package.json
├── tsconfig.json
├── skills-base.json        # Curated index of 200+ skills (name, repo url, description)
├── src/
│   ├── index.ts            # MCP Server entry point
│   └── download-skills.ts  # Batch downloader from skills-base.json
├── dist/
│   ├── index.js
│   └── download-skills.js
└── .agents/
    └── skills/             # Local skill files served to AI agents
        ├── code-review/
        │   └── SKILL.md
        ├── test-driven-development/
        │   └── SKILL.md
        └── ...

🚀 Запуск сервера

Запустите сервер, используя:

npm start

⚙️ Конфигурация MCP

Чтобы подключить этот сервер к MCP-клиенту (например, Antigravity, Cursor, Roo Code или Cline), добавьте следующую конфигурацию в файл настроек вашего клиента:

{
  "mcpServers": {
    "skill-library": {
      "command": "node",
      "args": [
        "C:/path/to/skill-library-mcp/dist/index.js"
      ],
      "env": {
        "SKILLS_DIR": "C:/path/to/skill-library-mcp/.agents/skills"
      }
    }
  }
}

Важно: Замените C:/path/to/skill-library-mcp на фактический путь к вашему репозиторию, указывающий на dist/index.js.

🤖 Системный промпт (протокол базы знаний)

Добавьте следующий промпт в правила вашего AI-агента (.clinerules, .cursorrules или пользовательские инструкции), чтобы агент автоматически использовал библиотеку навыков:

# UNIVERSAL KNOWLEDGE BASE PROTOCOL
You are equipped with the Enterprise "Skill Library MCP". Before starting any architectural planning, refactoring, or feature implementation, you MUST:
1. Use `list_skills` (or `list_skills({ query: "keyword" })`) to check for relevant domain rules or coding standards.
2. If found, use `fetch_skill_rule({ skill_name: "..." })` to read the full context and nested rule guidelines.
3. Explicitly acknowledge the rules and apply them strictly to your code generation.

📜 Лицензия

MIT

Available Tools

2 tools
fetch_skill_ruleA
Read-onlyIdempotent

Fetch the full markdown content and nested rule guidelines of a specific skill.

NameRequiredDescriptionDefault
skill_nameYesExact name of the skill folder (e.g., 'nestjs-best-practices')

Output Schema

NameRequiredDescription
filesNo
contentNo
skill_nameNo

TDQS

A4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds value by specifying exactly what is returned (full markdown content and nested rule guidelines), which is beyond the annotations. It does not claim any side effects, and the read-only nature is consistent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, concise sentence of 12 words. It is front-loaded with the verb and clearly states the output. No unnecessary words or repetition; every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter read tool, the description is quite complete given the presence of an output schema (which presumably explains the return format) and annotations covering safety. It does not mention error behavior (e.g., missing skill), but that is likely handled by the output schema. Overall, adequate for the tool's simplicity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the schema provides a clear description for skill_name. The tool description does not add any additional meaning to the parameter, so it relies on the schema. Baseline 3 is appropriate when the schema already documents the parameter fully.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific verb 'Fetch' and the resource (full markdown content and nested rule guidelines of a skill). It distinguishes this from list_skills, which likely only provides an overview. The purpose is unambiguous and distinct from its sibling.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The usage context is implied by the description and the presence of list_skills as a sibling, but it does not explicitly state when to use this tool versus list_skills. No exclusions or alternative conditions are mentioned, so guidance is only implicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_skillsA
Read-onlyIdempotent

List all available skills/rules in the knowledge base. Supports optional search query for token efficiency.

NameRequiredDescriptionDefault
queryNoOptional keyword to filter skills (e.g., 'react', 'python', 'review')

Output Schema

NameRequiredDescription
countNo
skillsNo

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and idempotentHint=true, and the description adds the behavioral context that it returns the full list unless filtered, plus the token-efficiency rationale. This supplements the annotations without contradicting them.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single sentence packs the core purpose and the optional usage hint with zero redundancy or filler. The most important information is front-loaded, and every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple nature of the tool, the presence of an output schema, and comprehensive annotations, the description covers the essential aspects. It does not explicitly address when to use fetch_skill_rule, but that is a minor omission for a list operation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% for the single optional 'query' parameter, so the description's addition of 'token efficiency' provides extra semantic value beyond the schema, explaining why one would use the filter. This meaningfully enhances the parameter's purpose.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists all available skills/rules in the knowledge base, using a specific verb ('List') and resource. It distinguishes from sibling fetch_skill_rule by semantic contrast (list vs fetch), making the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description mentions an optional search query for token efficiency, which hints at usage for large lists, but does not explicitly contrast with fetch_skill_rule or state when to use one over the other. The guidance is implied rather than explicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 2 tool updatesv1.0.0
    • First observedfetch_skill_rule
    • First observedlist_skills
Deploy Server
Shareable, User-Owned Context BackendPeople, applications, and agents read, write, and coordinate together in one context lake. Reach it over MCP, a CLI, a Python SDK, or REST.fulcradynamics.comAd

TDQS

A4.2/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have completely distinct purposes: one lists available skills, the other fetches a specific skill's content. There is no overlap or ambiguity between them.

Naming Consistency5/5

Both tools follow the same verb_noun pattern (list_skills, fetch_skill_rule), making the naming predictable and consistent.

Tool Count3/5

With only 2 tools, the set is on the thin side, but it fits the narrow scope of a read-only skill library. It feels borderline rather than clearly inadequate.

Completeness4/5

For a read-only knowledge base, list and fetch cover the essential operations. The search option on list_skills adds flexibility. Minor gaps like versioning or metadata retrieval are not critical.

Maintenance

ActivityStale
ResponsivenessNo issues

Resources

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