# quesen

> quesen — shxnque-quesen. Use this tool when you need to evaluate autonomous agent risks with deterministic AI decisions, ensuring consistent outputs from the same inputs. Quesen solves problems related to uncertain decision-making in autonomous systems, providing a reliable and predictable risk assessment. It takes in specific inputs and outputs evaluated risks through its native MCP server and Agent Settlement Protocol (ASP/1.0) interface.

Canonical page: https://skillsregistry.net/skills/shxnque-quesen  
JSON: https://api.skillsregistry.net/v1/skills/shxnque-quesen

## Description

Quesen — Deterministic AI decision engine for autonomous-agent risk evaluation. Native MCP server + Agent Settlement Protocol (ASP/1.0). Zero-LLM. Same inputs → same output. Live: web-production-aa5ba.up.railway.app/mcp

## Trust

- **Trust score (0–1):** 0.56
- **Verification tier:** scanned
- **Last scanned:** 2026-09-28

## Facts

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

## Source

- **Source listing:** [GitHub](https://github.com/Shxnque/quesen)

## 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": "shxnque-quesen"
    }
  }
}
```

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