product-discovery

Name: product-discovery
Availability: InStock
Author: AbsolutelySkilled

Analyze product discovery workflows to map opportunities and design falsifiable experiments.

207|31|Updated Mar 14, 2026

Search Tags:#discovery #framework #assumptions #jtbd #continuous-discovery #opportunity-trees

Ready to run

npx skills add https://github.com/AbsolutelySkilled/AbsolutelySkilled --skill product-discovery-absolutelyskilled

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: product-discovery
Source: https://github.com/AbsolutelySkilled/AbsolutelySkilled/tree/main/skills/product-discovery
Command: npx skills add https://github.com/AbsolutelySkilled/AbsolutelySkilled --skill product-discovery-absolutelyskilled

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

The product discovery workflow is often fragmented across JTBD interviews, OST mapping, and experiment design. This skill provides a structured, self-contained guide to implement continuous discovery for AI agents, enabling teams to align on measurable outcomes and validate ideas before execution.

Core Features & Use Cases

JTBD framing and job statement syntax
Opportunity Solution Tree (OST) modeling and maintenance
Assumption mapping across desirability, viability, feasibility, usability
Design and document experiments with kill thresholds and decision rules
Companion references and templates to accelerate discovery cadence

Quick Start

Describe your desired outcome, and the skill will map opportunities, generate hypotheses, and propose experiments to validate them.

Frequently Asked Questions about product-discovery

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

FAQPage Schema

How do I map opportunities and design experiments for continuous product discovery?▼

Continuous product discovery involves defining measurable outcomes, mapping opportunities using Opportunity Solution Trees, generating hypotheses, and designing falsifiable experiments with kill thresholds to validate ideas before execution.

What is the best way to write job statements for JTBD interviews?▼

Job statements for JTBD interviews should follow a structured syntax that frames the desired outcome, enabling teams to map assumptions across desirability, viability, feasibility, and usability before building solutions.

How do I create an Opportunity Solution Tree to align product outcomes?▼

Creating an Opportunity Solution Tree requires mapping a target outcome to discovered opportunities, then branching into specific solutions and underlying assumptions to maintain a structured discovery workflow.

Can I use this framework to map assumptions across desirability and feasibility?▼

Yes, assumption mapping evaluates ideas across four dimensions: desirability, viability, feasibility, and usability, ensuring teams validate critical risks before committing to development.

Do I need predefined outcomes to start mapping product discovery workflows?▼

You need a desired outcome to start; the framework maps opportunities, generates solution ideas, and proposes experiments to validate them based on that initial outcome.

How do I design falsifiable experiments with kill thresholds for product ideas?▼

Designing falsifiable experiments involves documenting tests with explicit kill thresholds and decision rules, allowing teams to validate assumptions and kill ideas before full execution.