Product Discovery

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

Security Scan

The skill's code and instructions align with its stated purpose (assumption mapping and test planning); it contains a small local Python CLI with no network or credential access.

Purpose & Capability

The name/description (product discovery, assumption mapping) match the provided files and behavior. Minor mismatch: SKILL.md instructs running `python3 scripts/assumption_mapper.py` but the skill metadata does not declare a required binary; the script requires a Python 3 runtime to be present.

Instruction Scope

SKILL.md stays on-topic (OST, assumption mapping, tests, discovery sprints) and only instructs running the included assumption_mapper.py or performing interviews/experiments. The instructions do not request unrelated files, credentials, or external endpoints.

Install Mechanism

There is no install specification (instruction-only), and the repo includes a small Python script. No downloads, external packages, or extraction steps are present. Runtime depends on a local Python 3 interpreter which is not declared in required binaries.

Credentials

The skill requests no environment variables, credentials, or config paths. The included script reads only user-supplied CSV files or inline arguments and prints a prioritized plan to stdout.

Persistence & Privilege

always is false and the skill is user-invocable. It does not request persistent presence or modify other skills or system-wide settings.

Assessment

This skill appears to do exactly what it claims: a small local Python CLI to prioritize assumptions and suggest tests. Before installing: ensure you have Python 3 available (SKILL.md calls `python3` but the metadata doesn't declare it). Review the included script (scripts/assumption_mapper.py) — it only reads CSV/CLI input and prints results, and makes no network calls or secret access. When using untrusted CSVs, be cautious about CSV-injection (if you open the output in Excel/Sheets, cells that begin with '=' or similar may be treated as formulas). If you plan to let the agent invoke skills autonomously, note this skill itself has no external access, but always verify future updates or added files for network/credential usage.

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

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

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.