What can you do with this server?

This server is an MCP-based skill library that lets you discover, search, organize, fetch, and manage reusable AI skill guidelines and engineering best practices. * Omni-search skills using semantic expansion, fuzzy matching, weighted scoring, multi-dimensional filters, and pagination (find_skills). * Browse all skills and explore the taxonomy of domains, occupations, categories, and uninitialized skills. * Fetch full skill markdown guidelines and detailed metadata/file breakdowns. * Rescan local skill directories and sync the taxonomy cache without restarting. * Update skill metadata (domain, occupation, category, tags) in SKILL.md frontmatter and the master registry. * Install skills from repositories, URLs, or custom markdown content, and uninstall existing ones. * Manage persistent AI session state with TTL expiration (admin only). * Issue, regenerate, delete, validate, and list OAuth API keys with roles and audit logs (admin only). * View system status, network IPs, recent activity logs, and background PM2 services. * Base tools include list_skills and fetch_skill_rule for lightweight clients.

How do I use MCP-skill-library-dynamic?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP-skill-library-dynamic List the available skills and fetch the code-review instructions." That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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MCP-skill-library-dynamic

by Guslaier

Overview Schema Related Servers Score Discussions

TypeScript

Local

Skill Library MCP Server

An enterprise-grade Model Context Protocol (MCP) server for automatically discovering, indexing, and serving 580+ AI Skill Guidelines and Engineering Best Practices. Features an Omni-Search Multi-Dimensional Taxonomy Engine, Semantic Query Expansion, Fuzzy Matching, Weighted Relevance Scoring, PM2 Daemon Management, an interactive TUI Control Dashboard, OAuth Token Lifecycle Management, and a Persistent AI Session Memory Store.

Quick Start

1. Install and Build

npm install
npm run build

2. Launch Interactive Control Dashboard (TUI)

npm run menu

Note: Navigate using Arrow Keys (Up / Down) + Enter or press numeric shortcut keys [0-6] directly. Mouse tracking is disabled to guarantee native terminal copy/paste.

Related MCP server: docubridge

IMPORTANT: First-Time Setup and Taxonomy Sync

IMPORTANT

When setting up the server for the first time, or after adding/cloning new skills, you must instruct the AI to synchronize and organize the taxonomy metadata to enable full Omni-Search capabilities.

Example AI Prompt:

"Please call sync_skills and organize all uninitialized skills using update_skill_metadata."

The 7-Step AI Librarian Workflow:

sync_skills: Rescan local skill directories to discover newly added skills immediately without restarting the server.
explore_taxonomy: Inspect the current breakdown of Domains, Occupations, Categories, and the uninitialized skills count (noninit_count).
find_skills({ category: "noninit", limit: 100 }): Retrieve unclassified skills page by page.
Analyze Skill: Identify target domains and occupations based on the skill purpose.
Create or Reuse Taxonomy: Reuse existing domain/occupation names if applicable, or create broad new ones (e.g. gaming, finance, healthcare).
Define Specific Tags: Assign specific technology and problem keywords (e.g. ["ecs", "dots", "burst", "optimization"]).
update_skill_metadata: Write updates directly to the local SKILL.md frontmatter and the master registry simultaneously.

Omni-Search and Taxonomy Discovery Architecture

Skill Library MCP utilizes a 4-dimensional taxonomy model paired with an intelligent relevance engine:

Dimension	Description	Example Values
`domain`	Target industry or broad domain	`gaming`, `finance`, `healthcare`, `ecommerce`, `education`
`occupation`	Target role or persona	`game-developer`, `backend-developer`, `data-scientist`, `qa-engineer`
`category`	Technical classification	`frontend`, `backend`, `database`, `devops`, `ai-ml`, `testing`, `security`
`tags`	Specific technologies, libraries, or problem areas	`typeorm`, `postgres`, `playwright`, `jwt-auth`, `memory-leak`, `ecs`

3-Layer Search Intelligence:

Semantic Query Expansion: Automatically expands search terms to include domain synonyms and related keywords (e.g. searching game expands to [game, gaming, gamedev, unity, unreal, godot]).
Fuzzy String Matching: Tolerates typographical errors and partial spellings using string similarity metrics (e.g. gamming, postgre, optimizaton).
Weighted Scoring and 40% Relative Cutoff:
- Dynamically scores candidates (Exact Name: +15, Tag match: +10-12, Domain match: +8, Category match: +4).
- Automatically filters out candidates scoring below 40% of the top match score to eliminate noise and save token usage.

MCP Client Configuration

1. Remote HTTP / SSE Mode (Recommended for Antigravity, Gemini IDE, Claude Desktop, Cursor)

Add to your IDE configuration (mcp_config.json):

{
  "mcpServers": {
    "skill-library": {
      "url": "http://localhost:8787/sse",
      "headers": {
        "Authorization": "Bearer <YOUR_API_TOKEN>"
      }
    }
  }
}

(Generate Bearer Tokens from option [3] API Keys and Auth in npm run menu)

2. Local Stdio Mode (Direct Process Spawn)

{
  "mcpServers": {
    "skill-library": {
      "command": "node",
      "args": ["<ROOT_DIR>/dist/index.js"],
      "env": {
        "SKILLS_DIR": "<ROOT_DIR>/.agents/skills",
        "SKILL_LIBRARY_DATA_DIR": "<ROOT_DIR>/.data"
      }
    }
  }
}

(Replace <ROOT_DIR> with the absolute path to the project directory, e.g. C:/Users/admin/Desktop/skill-library-mcp)

Available MCP Tools and Role Permissions (RBAC)

The server enforces Role-Based Access Control (RBAC):

Category	Tool Name	Permission Role	Description
Discovery and Search	`find_skills`	Public AI	Omni-Search with weighted scoring, fuzzy matching, multi-dimensional filters, and pagination.
	`list_skills`	Public AI	Browse skills with pagination (`limit`, `page`) and category filtering.
	`explore_taxonomy`	Public AI	View taxonomy summary (Domains, Occupations, Categories) and unclassified (`noninit`) counts.
	`fetch_skill_rule`	Public AI	Fetch full markdown guidelines and best practices for a skill.
	`get_skill_info`	Public AI	Get detailed file breakdown and metadata for a skill.
Management and Sync	`sync_skills`	Public AI	Rescan local folders and rebuild taxonomy cache without server restart.
	`update_skill_metadata`	Public AI	Assign Domain, Occupation, Category, and Tags with auto-canonicalization.
	`install_skill`	Public AI	Install skills from repository, URL, or custom markdown content.
	`uninstall_skill`	Public AI	Remove a skill from the library.
AI Sessions	`session_create`	Admin Only	Persist arbitrary JSON session state with TTL expiration.
	`session_get`	Admin Only	Retrieve stored session data by name.
	`session_list`	Admin Only	List all active sessions (auto-purges expired records).
	`session_delete`	Admin Only	Delete session record.
Auth and Keys	`oauth_generate_key`	Admin Only	Issue new API tokens with custom roles and TTL expiration.
	`oauth_regen_key`	Admin Only	Re-issue new token for an existing key ID.
	`oauth_delete_key`	Admin Only	Delete an API key permanently.
	`oauth_validate_key`	Admin Only	Verify token validity, expiration, and associated role.
	`oauth_list_keys`	Admin Only	List stored API key metadata and usage audit logs.
System Status	`system_status`	Admin Only	View network IPs (Tailscale/LAN/Localhost) and recent activity logs.
	`service_list`	Admin Only	List all registered and active background services (read-only).

PM2 Background Daemon Management

The project includes built-in PM2 orchestration for running as a background service:

# Start server daemon in background
npm run pm2:start

# View process table, memory usage, and uptime
npm run pm2:status

# Stream realtime logs
npm run pm2:logs

# Restart or stop the daemon
npm run pm2:restart
npm run pm2:stop

Mandatory AI Agent System Prompt

To ensure AI agents utilize this system effectively, include this protocol in your system prompt or custom rules:

CRITICAL DIRECTIVE: YOUR VERY FIRST ACTION BEFORE DOING ANYTHING ELSE MUST BE TO CALL THE find_skills TOOL.

You are equipped with the Skill Library MCP (Dynamic Skills and Best Practice Registry).
Before analyzing the request, reading files, or planning, you MUST strictly follow this 3-step protocol:
1. Discover (find_skills({ query: "<task_or_framework>", domain: "<domain>", occupation: "<role>" }))
2. Ensure Available and Load Rules (fetch_skill_rule({ skill_name: "<skill_name>" }) or install_skill)
3. Comply and Implement

License

MIT

Available Tools

2 tools

fetch_skill_ruleA

Read-onlyIdempotent

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

ParametersJSON Schema

Name	Required	Description	Default
`skill_name`	Yes	Exact name of the skill folder (e.g., 'nestjs-best-practices')

Output Schema

ParametersJSON Schema

Name	Required	Description
`files`	No
`content`	No
`skill_name`	No

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.

ParametersJSON Schema

Name	Required	Description	Default
`query`	No	Optional keyword to filter skills (e.g., 'react', 'python', 'review')

Output Schema

ParametersJSON Schema

Name	Required	Description
`count`	No
`skills`	No

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. Dates show when Glama detected each change.

2 tool updatesv1.0.0Aug 27, 2026
- First observedfetch_skill_rule
- First observedlist_skills

Install Server

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TDQS

A4.2/5.0

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

ActivityMaintained

ResponsivenessNo issues

Resources

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Skill Library MCP Server

Quick Start

1. Install and Build

2. Launch Interactive Control Dashboard (TUI)

IMPORTANT: First-Time Setup and Taxonomy Sync

Example AI Prompt:

The 7-Step AI Librarian Workflow:

Omni-Search and Taxonomy Discovery Architecture

3-Layer Search Intelligence:

MCP Client Configuration

1. Remote HTTP / SSE Mode (Recommended for Antigravity, Gemini IDE, Claude Desktop, Cursor)

2. Local Stdio Mode (Direct Process Spawn)

Available MCP Tools and Role Permissions (RBAC)

PM2 Background Daemon Management

Mandatory AI Agent System Prompt

License

Available Tools

Output Schema

TDQS

Output Schema

TDQS

Tool Schema Changelog

TDQS

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

Instruction MCP Server

docubridge

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skills2mcp

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