product-discovery

name	product-discovery
description	Manage — Use when validating product opportunities, mapping assumptions, planning discovery sprints, or testing problem-solution fit before committing delivery resources.
executor	HYBRID
skill_id	business.product-team.product-discovery
status	ADOPTED
security	{"level":"standard","pii":false,"approval_required":false}
anchors	["business","testing"]
tier	2
input_schema	[{"name":"code_or_task","type":"string","description":"Code snippet, script, or task description to process","required":true}]
output_schema	[{"name":"plan","type":"string","description":"Strategic plan or design document"},{"name":"next_steps","type":"array","description":"List of recommended next steps"}]

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:

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