# Sensei

> Use this tool when you need to maintain code quality standards and ensure architectural consistency across teams. Sensei transforms engineering standards documentation into an active development mentor, providing contextual guidance and validation tools against established standards. It analyzes code patterns, maintains session memory, and offers collaboration tools, making it ideal for development teams requiring intelligent support and adherence to comprehensive rulebooks.

Canonical page: https://skillsregistry.net/skills/amarodeabreu-sensei  
JSON: https://api.skillsregistry.net/v1/skills/amarodeabreu-sensei

## Description

Sensei transforms engineering standards documentation into an active development mentor by intelligently injecting relevant portions of a comprehensive 57-section rulebook based on file types, operations, and context. Built with FastMCP, it analyzes code patterns across 50+ file types to determine which engineering principles are most relevant, maintains session memory of architectural decisions and constraints, and provides validation tools against established standards. The implementation coordinates 64 specialized AI personas across 12 categories, offers tools for recording and validating agreed-upon patterns, and supports session collaboration through Architecture Decision Records (ADRs), making it valuable for maintaining code quality standards, ensuring architectural consistency across teams, and providing contextual engineering guidance during development.

## Trust

- **Trust score (0–1):** 0.80
- **Verification tier:** scanned
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** file-system
- **Updated:** 2026-09-19

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/amarodeabreu-sensei)
- **Repository:** <https://github.com/amarodeabreu/sensei-mcp>

## 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": "amarodeabreu-sensei"
    }
  }
}
```

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