# dead-letter

> dead-letter — bigcactuslabs-dead-letter. Use this tool when you need to convert email archives into a structured format for further analysis or processing. The dead-letter tool takes .eml email exports as input and outputs clean Markdown with YAML front matter, solving problems such as reply thread splitting, signature stripping, and attachment extraction. It is particularly useful when feeding archived email into RAG and LLM pipelines, providing a streamlined interface for converting unstructured email data into a usable format.

Canonical page: https://skillsregistry.net/skills/bigcactuslabs-dead-letter  
JSON: https://api.skillsregistry.net/v1/skills/bigcactuslabs-dead-letter

## Description

MCP server that converts .eml email exports (Gmail, Outlook, and other archives) into clean Markdown with YAML front matter — splitting reply threads, stripping signatures and quoted history, extracting attachments, and parsing calendar invites. Built for feeding archived email into RAG and LLM pipelines.

## Trust

- **Trust score (0–1):** 0.58
- **Verification tier:** scanned
- **Last scanned:** 2026-08-30

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** devops-ci
- **Updated:** 2026-08-30

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/qhl8r1r44h)
- **Repository:** <https://github.com/BigCactusLabs/dead-letter>

## 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": "bigcactuslabs-dead-letter"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/bigcactuslabs-dead-letter` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/bigcactuslabs-dead-letter/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