# DoneThat

> Use this tool when you need to track and analyze work sessions, generate performance reports, and receive coaching insights while maintaining privacy. DoneThat solves problems related to work logging, summary generation, and long-term memory access across AI agent interactions. It takes in logged work data and outputs summaries, reports, and insights, making it ideal for use cases requiring efficient work tracking and analysis.

Canonical page: https://skillsregistry.net/skills/donethat  
JSON: https://api.skillsregistry.net/v1/skills/donethat

## Description

DoneThat is a privacy-first work tracking platform that generates summaries, performance reports, and coaching insights from logged work. The MCP server exposes tools for tracking work sessions and accessing long-term memory across AI agent interactions.

## Trust

- **Trust score (0–1):** 0.50
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-04-25

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/donethat)

## 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": "donethat"
    }
  }
}
```

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