# product-discovery

> Use when validating product opportunities, mapping assumptions, planning discovery sprints, or testing problem-solution fit before committing delivery resources.

- Skill: `neekware/product-discovery` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds add neekware/product-discovery`
- Raw SKILL.md: https://api.skillmd.com/api/skills/neekware/product-discovery/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Product & Planning
- Author: neekware (https://skillmd.com/u/neekware)
- Updated: 2026-08-19
- Page: https://skillmd.com/skills/neekware/product-discovery

---


# Product Discovery

Run structured discovery to identify high-value opportunities and de-risk product bets.

## When To Use

Use this skill for:

- Opportunity Solution Tree facilitation
- Assumption mapping and test planning
- Problem validation interviews and evidence synthesis
- Solution validation with prototypes/experiments
- Discovery sprint planning and outputs

## Core Discovery Workflow

1. Define desired outcome

- Set one measurable outcome to improve.
- Establish baseline and target horizon.

2. Build Opportunity Solution Tree (OST)

- Outcome -> opportunities -> solution ideas -> experiments
- Keep opportunities grounded in user evidence, not internal opinions.

3. Map assumptions

- Identify desirability, viability, feasibility, and usability assumptions.
- Score assumptions by risk and certainty.

Use:

```bash
python3 scripts/assumption_mapper.py assumptions.csv
```

4. Validate the problem

- Conduct interviews and behavior analysis.
- Confirm frequency, severity, and willingness to solve.
- Reject weak opportunities early.

5. Validate the solution

- Prototype before building.
- Run concept, usability, and value tests.
- Measure behavior, not only stated preference.

6. Plan discovery sprint

- 1-2 week cycle with explicit hypotheses
- Daily evidence reviews
- End with decision: proceed, pivot, or stop

## Opportunity Solution Tree (Teresa Torres)

Structure:

- Outcome: metric you want to move
- Opportunities: unmet customer needs/pains
- Solutions: candidate interventions
- Experiments: fastest learning actions

Quality checks:

- At least 3 distinct opportunities before converging.
- At least 2 experiments per top opportunity.
- Tie every branch to evidence source.

## Assumption Mapping

Assumption categories:

- Desirability: users want this
- Viability: business value exists
- Feasibility: team can build/operate it
- Usability: users can successfully use it

Prioritization rule:

- High risk + low certainty assumptions are tested first.

## Problem Validation Techniques

- Problem interviews focused on current behavior
- Journey friction mapping
- Support ticket and sales-call synthesis
- Behavioral analytics triangulation

Evidence threshold examples:

- Same pain repeated across multiple target users
- Observable workaround behavior
- Measurable cost of current pain

## Solution Validation Techniques

- Concept tests (value proposition comprehension)
- Prototype usability tests (task success/time-to-complete)
- Fake door or concierge tests (demand signal)
- Limited beta cohorts (retention/activation signals)

## Discovery Sprint Planning

Suggested 10-day structure:

- Day 1-2: Outcome + opportunity framing
- Day 3-4: Assumption mapping + test design
- Day 5-7: Problem and solution tests
- Day 8-9: Evidence synthesis + decision options
- Day 10: Stakeholder decision review

## Tooling

### `scripts/assumption_mapper.py.md`

> **Note:** Bundled scripts ship as Markdown reference (`.md`) — copy the code out of the `.md` file to run it.

CLI utility that:

- reads assumptions from CSV or inline input
- scores risk/certainty priority
- emits prioritized test plan with suggested test types

See `references/discovery-frameworks.md` for framework details.