# Prompts Library

> Use this tool when you need to systematically organize and manage AI prompt templates across different applications and use cases. It solves problems related to AI workflow automation, prompt library management, and team collaboration by providing a file-based prompt management system with YAML frontmatter support. The Prompts Library takes markdown files with structured metadata as input and outputs organized prompt templates, making it ideal for scenarios where versioning and sharing prompts are crucial.

Canonical page: https://skillsregistry.net/skills/tanker327-prompts  
JSON: https://api.skillsregistry.net/v1/skills/tanker327-prompts

## Description

This MCP server provides file-based prompt management with YAML frontmatter support for organizing and retrieving AI prompt templates. Built by tanker327 using TypeScript with gray-matter for metadata parsing and chokidar for real-time file watching, it stores prompts as markdown files with structured metadata including title, description, category, tags, difficulty level, and author information. The implementation features in-memory caching with automatic cache updates when files change, tools for adding prompts with automatic metadata generation, structured prompt creation with guided metadata input, and comprehensive CRUD operations for prompt collections. It's valuable for AI workflow automation, prompt library management, and team collaboration scenarios where users need to systematically organize, version, and share prompt templates across different AI applications and use cases.

## Trust

- **Trust score (0–1):** 0.95
- **Verification tier:** verified
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-19

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/tanker327-prompts)
- **Repository:** <https://github.com/tanker327/prompts-mcp-server>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "tanker327-prompts"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/tanker327-prompts` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/tanker327-prompts/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt