# mathema

> mathema — tetrionlabs-mathema. Use this tool when you need to turn software intent into verifiable evidence, solving problems of code reliability and trustworthiness by providing a clear link between claims and implementation. It takes in software code and intent as input, and outputs verifiable evidence, facilitating Claim-Driven Development. Ideal for use in git-based development workflows where code validation and verification are crucial.

Canonical page: https://skillsregistry.net/skills/tetrionlabs-mathema  
JSON: https://api.skillsregistry.net/v1/skills/tetrionlabs-mathema

## Description

Claim-Driven Development: turn software intent into verifiable evidence.

## Trust

- **Trust score (0–1):** 0.48
- **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/tetrionlabs/mathema)

## 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": "tetrionlabs-mathema"
    }
  }
}
```

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