# logzip

> logzip — nailshakurov-logzip. Use this tool when you need to compress large logs for efficient analysis with Large Language Models (LLMs), reducing token count by 40-60%. It provides a Python API for easy integration and utilizes Rust for high-performance compression. Ideal for applications with extensive logging data, such as git repositories, to optimize storage and analysis.

Canonical page: https://skillsregistry.net/skills/nailshakurov-logzip  
JSON: https://api.skillsregistry.net/v1/skills/nailshakurov-logzip

## Description

Compress logs for LLM analysis — Rust-powered, Python API. 40-60% token savings.

## Trust

- **Trust score (0–1):** 0.64
- **Verification tier:** scanned
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Category:** ai-ml
- **Updated:** 2026-09-21

## Source

- **Source listing:** [GitHub](https://github.com/NailShakurov/logzip)

## 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": "nailshakurov-logzip"
    }
  }
}
```

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