# sigmacheck

> sigmacheck — cognis-digital-sigmacheck. Use this tool when you need to validate and refine Sigma detection rules, as it lints and unit-tests them against sample events to ensure accuracy and effectiveness. It solves problems related to rule quality and reliability, providing outputs such as test results and code quality assessments. It accepts Sigma rules and sample events as inputs, making it ideal for use in development and testing contexts, especially when integrated with git version control.

Canonical page: https://skillsregistry.net/skills/cognis-digital-sigmacheck  
JSON: https://api.skillsregistry.net/v1/skills/cognis-digital-sigmacheck

## Description

Lint and unit-test Sigma detection rules against sample events

## Trust

- **Trust score (0–1):** 0.85
- **Verification tier:** verified
- **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/cognis-digital/sigmacheck)

## 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": "cognis-digital-sigmacheck"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/cognis-digital-sigmacheck` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/cognis-digital-sigmacheck/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