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08 · Business · Product Discovery

Product Discovery Sprint

Turn customer evidence into prioritized assumptions, experiments and proceed/pivot/stop decisions. Choose Customer Research and Synthesis when the evidence itself still needs synthesis.

product-discoveryassumptionsexperimentsopportunity-treevalidationproduktvalidierungannahmenpruefunghypothesentestdiscovery-sprint

Product Discovery Sprint · Original SKILL.md

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

# 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`

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.

When to use

Plan product discovery before committing delivery resources.

What you get

An opportunity tree, assumption table, experiment plan, evidence log and decision criteria.

How it works

Define an outcome, map evidence-backed opportunities, prioritize uncertain risks and plan ethical tests before deciding.

Requirements

Product outcome, target audience, existing customer evidence, delivery constraints and authority for any proposed participant or experiment activity.

Delivery and review notes

Full original, assumption_mapper.py and discovery-frameworks.md read at 19392f7a08264ed00486a251f5b2098321771f94. MIT copyright retained; original and supporting files unchanged. This provides a discovery plan and complete reference source, not an installed or runtime-certified tool. Prioritization scores are explicit judgment heuristics, not measured probabilities or statistically calibrated confidence. Explain who assigned risk/certainty, the evidence and uncertainty, and distinguish the numeric risk*(1-certainty) ranking from the reference matrix’s qualitative quadrant ordering; they need not produce the same order. The 1-2 week schedule and minimum opportunity/experiment counts are examples to adapt, not universal validity thresholds. Use authorized, minimized customer evidence and label assumptions separately from observations. Fake-door tests must not falsely claim a live product, collect payment for an unavailable offer, or hide material limitations; prefer transparent interest registration and a separate approved test plan. Recruiting, contacting participants, publishing experiments, spending money or collecting personal data requires task-scoped authorization. The bundled Python script only reads a supplied CSV and prints a ranking, but static review found validation/output gaps: float NaN passes its range comparisons, missing CSV scores default to zero, unknown categories receive generic advice, and interpolated output is not safely quoted CSV. Before any separately authorized execution, validate finite scores in [0,1], required nonempty statements/categories, an allowed input path, bounded file size and positive --top; use csv.writer for machine-readable output and appropriate spreadsheet formula handling if exporting user-controlled cells. No script was executed. A manual assumption table is a supported way to apply the method without installation. Preserve dissenting evidence and predefine decision criteria; a small interview sample or high score alone does not prove demand. Use Customer Research and Synthesis for synthesizing evidence; use this skill to turn evidence into testable product assumptions and proceed/pivot/stop decisions.

Starting prompt

Plan a discovery sprint for [PRODUCT OPPORTUNITY]. Inspect the existing customer evidence and constraints. Separate observations from assumptions, map opportunities, prioritize uncertain risks and define ethical tests with explicit decision criteria. Return a plan and assumption table; do not recruit participants, publish experiments, charge money or execute scripts. Treat scores and timelines as adaptable heuristics.

Not for

Invented customer evidence, statistical proof from small qualitative samples, deceptive fake-door offers, automatic participant outreach, or certifying the bundled script as production-ready.

What M11 added

Original authorship remains with alirezarezvani/claude-skills · Original source ↗

Included reference: scripts/assumption_mapper.py

Alireza Rezvani · MIT · SHA-256 158e0348d347f8877b6e791e20920596f45bcef1269cc836fdb68ccd83e9503d

#!/usr/bin/env python3
"""Prioritize product assumptions and suggest validation tests."""

import argparse
import csv
from dataclasses import dataclass


@dataclass
class Assumption:
    statement: str
    category: str
    risk: float
    certainty: float

    @property
    def priority_score(self) -> float:
        # High-risk, low-certainty assumptions should be tested first.
        return self.risk * (1.0 - self.certainty)


def parse_float(value: str, field: str) -> float:
    number = float(value)
    if number < 0 or number > 1:
        raise ValueError(f"{field} must be in [0, 1]")
    return number


def suggest_test(category: str) -> str:
    category = category.lower().strip()
    if category == "desirability":
        return "problem interviews or fake-door test"
    if category == "viability":
        return "pricing/willingness-to-pay test"
    if category == "feasibility":
        return "technical spike or architecture prototype"
    if category == "usability":
        return "moderated usability test"
    return "smallest possible experiment with clear success criteria"


def load_from_csv(path: str) -> list[Assumption]:
    assumptions: list[Assumption] = []
    with open(path, "r", encoding="utf-8", newline="") as handle:
        reader = csv.DictReader(handle)
        required = {"assumption", "category", "risk", "certainty"}
        missing = required - set(reader.fieldnames or [])
        if missing:
            missing_str = ", ".join(sorted(missing))
            raise ValueError(f"Missing required columns: {missing_str}")

        for row in reader:
            assumptions.append(
                Assumption(
                    statement=(row.get("assumption") or "").strip(),
                    category=(row.get("category") or "").strip(),
                    risk=parse_float(row.get("risk") or "0", "risk"),
                    certainty=parse_float(row.get("certainty") or "0", "certainty"),
                )
            )
    return assumptions


def parse_inline(items: list[str]) -> list[Assumption]:
    assumptions: list[Assumption] = []
    for item in items:
        # format: statement|category|risk|certainty
        parts = [part.strip() for part in item.split("|")]
        if len(parts) != 4:
            raise ValueError("Inline assumption must be: statement|category|risk|certainty")
        assumptions.append(
            Assumption(
                statement=parts[0],
                category=parts[1],
                risk=parse_float(parts[2], "risk"),
                certainty=parse_float(parts[3], "certainty"),
            )
        )
    return assumptions


def build_parser() -> argparse.ArgumentParser:
    parser = argparse.ArgumentParser(description="Prioritize assumptions and generate test plan.")
    parser.add_argument("input", nargs="?", help="CSV file path")
    parser.add_argument(
        "--assumption",
        action="append",
        default=[],
        help="Inline assumption: statement|category|risk|certainty",
    )
    parser.add_argument("--top", type=int, default=10, help="Maximum assumptions to print")
    return parser


def main() -> int:
    parser = build_parser()
    args = parser.parse_args()

    assumptions: list[Assumption] = []
    if args.input:
        assumptions.extend(load_from_csv(args.input))
    if args.assumption:
        assumptions.extend(parse_inline(args.assumption))

    if not assumptions:
        parser.error("Provide a CSV input file or at least one --assumption value.")

    assumptions.sort(key=lambda item: item.priority_score, reverse=True)

    print("prioritized_assumption_test_plan")
    print("rank,priority_score,category,risk,certainty,test,assumption")
    for rank, item in enumerate(assumptions[: args.top], start=1):
        test = suggest_test(item.category)
        print(
            f"{rank},{item.priority_score:.4f},{item.category},{item.risk:.2f},"
            f"{item.certainty:.2f},{test},{item.statement}"
        )

    return 0


if __name__ == "__main__":
    raise SystemExit(main())

MIT License

Copyright (c) 2025 Alireza Rezvani

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

Included reference: references/discovery-frameworks.md

Alireza Rezvani · MIT · SHA-256 2d1e59f597c9d1f09a201ec7c12bdc9a816c0707b585f32080db31dfd6b44bab

# Discovery Frameworks

## Opportunity Solution Tree (OST)

Purpose: continuously connect product outcomes to validated opportunities and tested solutions.

Core structure:
- Outcome (metric)
- Opportunity nodes (needs/pains)
- Solution ideas
- Experiments

OST practice tips:
- Keep tree live; update after each interview or test.
- Separate opportunity evidence from solution proposals.
- Avoid single-branch trees that force one solution.

## Jobs-to-be-Done (JTBD)

Use JTBD to understand progress users seek.

JTBD template:
"When [situation], I want to [motivation], so I can [expected outcome]."

JTBD interview focus:
- Trigger moments
- Current alternatives and workarounds
- Purchase/adoption anxieties
- Desired progress and success criteria

## Kano Model

Classify features by impact on satisfaction:
- Must-be: expected baseline features
- Performance: more is better
- Delighters: unexpected value multipliers
- Indifferent: low impact
- Reverse: can reduce satisfaction for some users

Use Kano when prioritizing solution concepts after problem validation.

## Design Sprint Methodology

Typical phases:
1. Understand
2. Sketch
3. Decide
4. Prototype
5. Test

Discovery usage:
- Compress learning cycle into one week.
- Best for high-ambiguity opportunities requiring cross-functional alignment.

## Assumption Prioritization Matrix

Map assumptions on two axes:
- Risk if wrong (low -> high)
- Certainty (low -> high)

Priority order:
1. High risk, low certainty (test first)
2. High risk, high certainty (validate quickly)
3. Low risk, low certainty (defer)
4. Low risk, high certainty (document)

## Discovery Evidence Rules

- One source is not enough for major decisions.
- Triangulate qualitative and quantitative signals.
- Predefine decision criteria before test execution.
- Archive evidence with date, segment, and method.

MIT License

Copyright (c) 2025 Alireza Rezvani

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

Original license & copyright

MIT License

Copyright (c) 2025 Alireza Rezvani

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

Source SHA-256: 52d492cb9eefe2136b21a295a9041bc60e11d7261f3f8ce84f3b58d83c8e75e4
Snapshot checked: 2026-09-28T14:35:54.358Z

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