# AI Conversation Logger

> Use this tool when you need to automatically capture and organize AI interactions across multiple platforms, solving the problem of manual conversation logging and lost context. It takes in AI conversations as input and outputs structured markdown logs with project-based organization and automatic tagging. Ideal for developers who require persistent conversation history, it is particularly useful for maintaining context across development sessions and tracking AI-assisted coding workflows over time.

Canonical page: https://skillsregistry.net/skills/fablefang-ai-conversation-logger  
JSON: https://api.skillsregistry.net/v1/skills/fablefang-ai-conversation-logger

## Description

AI Conversation Logger MCP server that automatically captures and organizes AI interactions across multiple platforms including Claude Desktop and Cursor, storing conversations in structured markdown format with project-based organization and automatic tagging. The implementation features intelligent project detection through common indicators like package.json or .git directories, creates daily log files with conversation metadata, and includes hook-based automation that triggers logging when conversations end. Built with TypeScript and designed for developers who want persistent conversation history without manual logging, it supports both local project-based storage and global home directory fallbacks, making it valuable for maintaining context across development sessions and tracking AI-assisted coding workflows over time.

## Trust

- **Trust score (0–1):** 0.92
- **Verification tier:** verified
- **Last scanned:** 2026-09-19

## Facts

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

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/fablefang-ai-conversation-logger)
- **Repository:** <https://github.com/fablefang/ai-conversation-logger-mcp>

## 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": "fablefang-ai-conversation-logger"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/fablefang-ai-conversation-logger` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/fablefang-ai-conversation-logger/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