# Rigour

> Use this tool when you need to ensure project quality and resolve operational issues efficiently. It automates checks, provides actionable resolution steps, and tracks failures to facilitate rapid debugging, accepting project inputs and generating detailed fix packets as output. Ideal for maintaining consistent development contexts across sessions, Rigour is suitable for projects requiring rigorous quality control and diagnostics.

Canonical page: https://skillsregistry.net/skills/rigour-labs-rigour  
JSON: https://api.skillsregistry.net/v1/skills/rigour-labs-rigour

## Description

Manage project quality gates and diagnostics with automated checks and actionable resolution steps. Tracks operation failures and provides detailed fix packets for rapid debugging. Persists project-specific instructions and preferences across sessions to maintain a consistent development context.

## Trust

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

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** other
- **Updated:** 2026-05-13

## Source

- **Source listing:** [Smithery](https://smithery.ai/server/rigour-labs/rigour)

## 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": "rigour-labs-rigour"
    }
  }
}
```

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