# SeriouslySimpleAnalytics

> SeriouslySimpleAnalytics — lbesecker195-seriouslysimpleanalytics. Use this tool when you need to analyze and understand the performance of your AI agent, specifically with llms.txt data. It solves problems related to tracking and optimizing AI model effectiveness, providing insights through analytics. The tool takes in llms.txt data as input and outputs actionable metrics and reports to inform AI model improvements.

Canonical page: https://skillsregistry.net/skills/lbesecker195-seriouslysimpleanalytics  
JSON: https://api.skillsregistry.net/v1/skills/lbesecker195-seriouslysimpleanalytics

## Description

llms.txt Analytics -- Analytics for your AI Agent

## Trust

- **Trust score (0–1):** 0.81
- **Verification tier:** scanned
- **Last scanned:** 2026-09-28

## Facts

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

## Source

- **Source listing:** [GitHub](https://github.com/lbesecker195/SeriouslySimpleAnalytics)

## 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": "lbesecker195-seriouslysimpleanalytics"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/lbesecker195-seriouslysimpleanalytics` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/lbesecker195-seriouslysimpleanalytics/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