discover

Create a discovery.md with JTBD canvas, assumption map, and experiment backlog.

Updated Mar 12, 2026
One-click install
npx skills add https://github.com/Nerfherder16/BrickLayer --skill discover-nerfherder16
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: discover
Source: https://github.com/Nerfherder16/BrickLayer/tree/main/.claude/skills/discover
Command: npx skills add https://github.com/Nerfherder16/BrickLayer --skill discover-nerfherder16

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Structured discovery to validate feature ideas before building, combining JTBD analysis, assumption mapping, and experiment design.

Core Features & Use Cases

  • JTBD canvas generation for a feature idea
  • Five-assumption mapping with prioritization
  • End-to-end discovery.md output with an experiment backlog
  • Output path guidance and timestamped discovery file handling

Quick Start

Run /discover on a feature idea to generate the discovery.md with a JTBD canvas, assumption map, and an experiment backlog.

Frequently Asked Questions about discover

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

FAQPage Schema
How do I validate feature ideas before adding them to the product backlog?▼

To validate feature ideas before backlog creation, you can use structured JTBD discovery and assumption mapping to identify risks. This process generates a discovery file detailing user jobs, prioritized assumptions, and an experiment backlog to test viability.

What is JTBD discovery and how does it work for software feature ideation?▼

JTBD discovery is a product management framework analyzing what users are trying to accomplish rather than their demographics. It works for software feature ideation by generating a canvas that maps user jobs, assumptions, and experiments to validate early concepts.

How do I map and prioritize assumptions during product discovery?▼

To map and prioritize assumptions during product discovery, you generate an assumption map of five items within a discovery file. This identifies the riskiest assumptions surrounding a feature idea so you can design targeted experiments to validate them.

Can I generate an experiment backlog directly from early feature concepts?▼

Yes, you can generate an experiment backlog directly from early feature concepts by running a structured discovery process. This combines JTBD analysis and assumption mapping to output a discovery file containing a ready-to-use experiment backlog for your team.

Does this discovery process require any specific frameworks or dependencies to run?▼

No external frameworks or dependencies are required to run this discovery process. It operates independently to generate a timestamped discovery.md file within a designated directory containing the JTBD canvas, assumption map, and experiment backlog.