continuous-discovery

Build weekly customer discovery cadences using Opportunity Solution Trees, interviews, and assumption testing.

Updated Jun 27, 2026
One-click install
npx skills add https://github.com/rachmadideni/ai-staff-assistant --skill continuous-discovery-rachmadideni
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: continuous-discovery
Source: https://github.com/rachmadideni/ai-staff-assistant/tree/main/.agents/skills/continuous-discovery
Command: npx skills add https://github.com/rachmadideni/ai-staff-assistant --skill continuous-discovery-rachmadideni

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Product teams often build features based on intuition, stakeholder opinions, or stale research, resulting in products nobody uses. This Skill establishes a sustainable weekly practice of customer discovery so every product decision is grounded in fresh evidence. ## Core Features & Use Cases - Opportunity Solution Trees: Visually connect business outcomes to customer opportunities, solutions, and experiments, making product strategy explicit and shared. - Story-Based Interviews & Snapshots: Conduct weekly customer interviews anchored in real past behavior, then synthesize each into a one-page snapshot that builds a growing evidence library. - Assumption Testing: Map desirability, viability, feasibility, and usability assumptions on an importance-vs-evidence grid, then run fast cheap tests on the riskiest ones before building. - Use Case: A product trio assigned to improve trial-to-paid conversion uses this Skill to set up automated interview recruitment, map the opportunity space from interview stories, test the riskiest assumption with a painted-door experiment, and ship only validated solutions. ## Quick Start Help me set up a continuous discovery practice for my product team, starting with an Opportunity Solution Tree for our retention outcome.

Frequently Asked Questions about continuous-discovery

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

FAQPage Schema
How do I build an Opportunity Solution Tree?▼

Start with a measurable outcome at the top, then populate the opportunity layer with customer needs discovered through interviews, framed from the customer's perspective. Add multiple solutions per opportunity and design small experiments to test each solution's riskiest assumption before building.

How do I run effective customer discovery interviews?▼

Use story-based interviewing: ask "Tell me about the last time you..." to capture specific past behavior rather than opinions or predictions. Follow up with sequence, emotion, and context questions, then synthesize each interview into a one-page snapshot with key quotes and opportunities.

What is assumption testing in product discovery?▼

Assumption testing identifies the desirability, viability, feasibility, and usability beliefs a solution depends on, maps them by importance and evidence, and tests the high-importance, low-evidence ones first. Tests like painted-door experiments or Wizard of Oz prototypes should take days, not weeks.

How often should product teams talk to customers?▼

The benchmark is at least one customer touchpoint per week, every week, with the full product trio participating. Automate recruitment through in-app intercepts and scheduling tools so the cadence sustains itself without heroic effort.

What is the difference between an experience map and a journey map?▼

An experience map captures the customer's entire experience of accomplishing a goal, including competitor tools and manual workarounds, while a journey map covers only interactions with your product. Use experience maps in discovery to find unmet needs and journey maps later to optimize existing flows.

When should I pivot away from a prioritized opportunity?▼

Pivot when assumption tests consistently fail, new interviews reveal a bigger opportunity, or shipped solutions are not moving the target outcome. Before pivoting, verify you actually tested assumptions and gave solutions enough time to show results.