agent-explorer-role

Enforce explorer role guidelines for scope definition, evidence gathering, and structured output.

Updated Aug 27, 2026

Search Tags:#validation #guidance #scope definition #evidence gathering #explorer role #output contract

Ready to run

npx skills add https://github.com/AmirTlinov/magray-marketplace --skill agent-explorer-role

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agent-explorer-role
Source: https://github.com/AmirTlinov/magray-marketplace/tree/main/flagship-team/skills/agent-explorer-role
Command: npx skills add https://github.com/AmirTlinov/magray-marketplace --skill agent-explorer-role

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides mandatory rules and guidance for explorer roles, ensuring consistent and effective scope definition, evidence gathering, and context pack output.

Core Features & Use Cases

Scope Definition: Clearly defines what is in and out of scope, along with assumptions and unknowns.
Evidence Gathering: Guides the collection of checks across happy paths, error paths, and boundaries.
Output Contract: Ensures all outputs are supported by anchors/counter-checks and adhere to a defined verdict, verified information, and summary format.
Use Case: An explorer role needs to validate a new feature. This Skill ensures they meticulously define the boundaries of their testing, gather all necessary evidence, and report their findings in a structured, verifiable manner.

Quick Start

Follow the explorer role contract to define scope, gather evidence, and produce a structured output.

Frequently Asked Questions about agent-explorer-role

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I define scope and gather evidence for AI-driven validation tasks?▼

To define scope and gather evidence for AI-driven validation tasks, establish clear boundaries, assumptions, and unknowns before execution. Collect comprehensive checks across happy paths, error paths, and boundaries to ensure all outputs are supported by anchors or counter-checks.

What is an output contract for explorer roles in AI-driven tasks?▼

An output contract for explorer roles in AI-driven tasks is a structured reporting format. It mandates that findings include a final verdict, verified information, and a summary of facts and risks, ensuring all conclusions are verifiable.

How do I structure validation reports to include verified facts and risks?▼

Structure validation reports by adhering to a defined output contract that separates the verdict, verified information, and a summary of facts and risks. This ensures structured, verifiable reporting supported by evidence anchors and counter-checks.

When do I need to enforce mandatory explorer role guidelines for feature validation?▼

Enforce mandatory explorer role guidelines when an explorer needs to validate a new feature. This ensures they meticulously define testing boundaries, gather necessary evidence across paths, and report findings in a structured, verifiable manner.

What boundaries and assumptions should I define before executing AI-driven tasks?▼

Before executing AI-driven tasks, define what is explicitly in and out of scope, along with all assumptions and unknowns. This scope definition phase is mandatory to guide subsequent comprehensive evidence gathering and validation.

Does evidence gathering for AI tasks require checking error paths and boundaries?▼

Evidence gathering for AI tasks requires checking error paths and boundaries. It mandates comprehensive collection across happy paths, error paths, and boundaries, with each output supported by anchors or counter-checks for verifiable validation.