# tare

> tare — mstuart-tare. Use this tool when you need to efficiently compress and manage context for large language models, solving issues of data redundancy and storage. It provides lossless compression, local-first caching, and reversible operations through a CLI, library, or server interface, accepting git-based inputs and producing optimized outputs. Ideal for use cases where data integrity and minimal storage footprint are crucial.

Canonical page: https://skillsregistry.net/skills/mstuart-tare  
JSON: https://api.skillsregistry.net/v1/skills/mstuart-tare

## Description

Lossless-by-default context compression for LLM coding agents — proxy, library, CLI, and MCP server. Local-first, cache-correct, reversible.

## Trust

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

## 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/mstuart/tare)

## 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": "mstuart-tare"
    }
  }
}
```

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