ambiguous-request-resolver

Translate ambiguous product requests into concrete engineering specifications.

Updated Oct 15, 2021

Search Tags:#requirements #translation #clarification #engineering #ux #product-management

Ready to run

npx skills add https://github.com/toqoz/config --skill ambiguous-request-resolver

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ambiguous-request-resolver
Source: https://github.com/toqoz/config/tree/main/home/agents/skills/ambiguous-request-resolver
Command: npx skills add https://github.com/toqoz/config --skill ambiguous-request-resolver

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill bridges the communication gap between non-technical stakeholders and engineers by clarifying ambiguous requirements, mapping user-facing terminology to codebase concepts, and identifying missing implementation details.

Core Features & Use Cases

Intent Translation: Converts high-level behavioral descriptions into actionable engineering tasks.
Contextual Grounding: Searches the codebase to verify if requested terms align with existing UI, API, or data models.
Risk Assessment: Provides a structured framework to decide when to proceed with a change and when to ask clarifying questions to avoid destructive or incorrect modifications.

Quick Start

Invoke the ambiguous request resolver to analyze the latest feature request from the design team and map it to the relevant components in the codebase.

Frequently Asked Questions about ambiguous-request-resolver

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

FAQPage Schema

How do I translate vague product requests into concrete engineering specifications?▼

To translate vague product requests, you map high-level behavioral descriptions to existing codebase components and data models, identifying necessary assumptions to form actionable engineering tasks.

What is the best way to map UX requirements to an existing codebase?▼

Mapping UX requirements involves searching the codebase to verify if requested terms align with existing UI, API, or data models, grounding user-facing terminology in actual implementation components.

When do I need to ask for clarification before implementing a new feature?▼

You need to ask for clarification when a risk assessment identifies missing implementation details or potential destructive modifications, ensuring safe execution by determining when assumptions are insufficient.

How do I convert ambiguous stakeholder descriptions into actionable engineering tasks?▼

Converting ambiguous stakeholder descriptions requires intent translation, transforming high-level behavioral requests into structured engineering tasks while flagging missing details for stakeholder clarification.

Can I use this approach to assess implementation risks for non-technical feature requests?▼

Yes, this approach provides a structured framework to assess implementation risks by identifying necessary assumptions and deciding when to proceed with a change versus when to ask clarifying questions.

Why does translating design team requests to code components require identifying assumptions?▼

Identifying assumptions is required because non-technical requests often lack implementation details, and mapping them to codebase concepts safely demands knowing what is unspoken before execution.