# casefile

> casefile — azimov777-casefile. Use this tool when you need to track and manage tasks across multiple AI agents, ensuring seamless handovers and continuity. It solves the problem of lost context and duplicated efforts by maintaining a case file for each task, accessible through a live board and MCP server. Ideal for self-hosted environments, it provides a one-line install solution with git capabilities.

Canonical page: https://skillsregistry.net/skills/azimov777-casefile  
JSON: https://api.skillsregistry.net/v1/skills/azimov777-casefile

## Description

AI agents forget between sessions. Casefile gives every task a case file — decisions, dead ends, open questions — so the next agent picks up where the last one stopped. Self-hosted MCP server + live board.

## Trust

- **Trust score (0–1):** 0.77
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **License:** MIT
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/azimov777/casefile)

## 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": "azimov777-casefile"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/azimov777-casefile` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/azimov777-casefile/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