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`

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.

{
  "format": "aiskill/v1",
  "skill": {
    "name": "product-discovery",
    "slug": "product-discovery",
    "version": "1.0.0",
    "description": "Use when validating product opportunities, mapping assumptions, planning discovery sprints, or testing problem-solution fit before committing delivery resources.",
    "category": "Business",
    "tags": [
      "product-business",
      "testing"
    ],
    "platforms": [
      "chatgpt",
      "claude-code",
      "cursor"
    ],
    "tested_environment": "Agent Skills convention (SKILL.md) — works with Claude Code, Cursor, ChatGPT/Codex and compatible agents",
    "license": "MIT"
  },
  "prompts": {
    "system": "# Product Discovery\r\n\r\nRun structured discovery to identify high-value opportunities and de-risk product bets.\r\n\r\n## When To Use\r\n\r\nUse this skill for:\r\n- Opportunity Solution Tree facilitation\r\n- Assumption mapping and test planning\r\n- Problem validation interviews and evidence synthesis\r\n- Solution validation with prototypes/experiments\r\n- Discovery sprint planning and outputs\r\n\r\n## Core Discovery Workflow\r\n\r\n1. Define desired outcome\r\n- Set one measurable outcome to improve.\r\n- Establish baseline and target horizon.\r\n\r\n2. Build Opportunity Solution Tree (OST)\r\n- Outcome -> opportunities -> solution ideas -> experiments\r\n- Keep opportunities grounded in user evidence, not internal opinions.\r\n\r\n3. Map assumptions\r\n- Identify desirability, viability, feasibility, and usability assumptions.\r\n- Score assumptions by risk and certainty.\r\n\r\nUse:\r\n```bash\r\npython3 scripts/assumption_mapper.py assumptions.csv\r\n```\r\n\r\n4. Validate the problem\r\n- Conduct interviews and behavior analysis.\r\n- Confirm frequency, severity, and willingness to solve.\r\n- Reject weak opportunities early.\r\n\r\n5. Validate the solution\r\n- Prototype before building.\r\n- Run concept, usability, and value tests.\r\n- Measure behavior, not only stated preference.\r\n\r\n6. Plan discovery sprint\r\n- 1-2 week cycle with explicit hypotheses\r\n- Daily evidence reviews\r\n- End with decision: proceed, pivot, or stop\r\n\r\n## Opportunity Solution Tree (Teresa Torres)\r\n\r\nStructure:\r\n- Outcome: metric you want to move\r\n- Opportunities: unmet customer needs/pains\r\n- Solutions: candidate interventions\r\n- Experiments: fastest learning actions\r\n\r\nQuality checks:\r\n- At least 3 distinct opportunities before converging.\r\n- At least 2 experiments per top opportunity.\r\n- Tie every branch to evidence source.\r\n\r\n## Assumption Mapping\r\n\r\nAssumption categories:\r\n- Desirability: users want this\r\n- Viability: business value exists\r\n- Feasibility: team can build/operate it\r\n- Usability: users can successfully use it\r\n\r\nPrioritization rule:\r\n- High risk + low certainty assumptions are tested first.\r\n\r\n## Problem Validation Techniques\r\n\r\n- Problem interviews focused on current behavior\r\n- Journey friction mapping\r\n- Support ticket and sales-call synthesis\r\n- Behavioral analytics triangulation\r\n\r\nEvidence threshold examples:\r\n- Same pain repeated across multiple target users\r\n- Observable workaround behavior\r\n- Measurable cost of current pain\r\n\r\n## Solution Validation Techniques\r\n\r\n- Concept tests (value proposition comprehension)\r\n- Prototype usability tests (task success/time-to-complete)\r\n- Fake door or concierge tests (demand signal)\r\n- Limited beta cohorts (retention/activation signals)\r\n\r\n## Discovery Sprint Planning\r\n\r\nSuggested 10-day structure:\r\n- Day 1-2: Outcome + opportunity framing\r\n- Day 3-4: Assumption mapping + test design\r\n- Day 5-7: Problem and solution tests\r\n- Day 8-9: Evidence synthesis + decision options\r\n- Day 10: Stakeholder decision review\r\n\r\n## Tooling\r\n\r\n### `scripts/assumption_mapper.py`\r\n\r\nCLI utility that:\r\n- reads assumptions from CSV or inline input\r\n- scores risk/certainty priority\r\n- emits prioritized test plan with suggested test types\r\n\r\nSee `references/discovery-frameworks.md` for framework details.",
    "user_template": ""
  },
  "variables": [],
  "install": {
    "steps": [
      {
        "title": "Download & unzip",
        "detail": "Download the .zip package from SkillVault and unzip it. The product-discovery/ folder contains SKILL.md plus any companion scripts, references and assets."
      },
      {
        "title": "Copy into your agent skills directory",
        "detail": "Move the folder into your agent's skills directory — e.g. ~/.claude/skills/ (Claude Code), .codex/skills/, .cursor/skills/ or your project's .claude/skills/ folder — then restart or reload the session."
      },
      {
        "title": "Use it",
        "detail": "Describe the task in natural language; the agent loads the skill automatically when the request matches the skill description."
      }
    ]
  },
  "usage": {
    "steps": []
  },
  "metadata": {
    "author": "SkillVault",
    "source": "https://skillvault.top/skills/product-discovery",
    "exported_at": "2026-11-03T07:43:39.688Z"
  }
}

format: aiskill/v1
skill:
  name: product-discovery
  slug: product-discovery
  version: 1.0.0
  description: >-
    Use when validating product opportunities, mapping assumptions, planning discovery sprints, or
    testing problem-solution fit before committing delivery resources.
  category: Business
  tags:
    - product-business
    - testing
  platforms:
    - chatgpt
    - claude-code
    - cursor
  tested_environment: >-
    Agent Skills convention (SKILL.md) — works with Claude Code, Cursor, ChatGPT/Codex and
    compatible agents
  license: MIT
prompts:
  system: "# Product Discovery\r\n\r\nRun structured discovery to identify high-value opportunities and de-risk product bets.\r\n\r\n## When To Use\r\n\r\nUse this skill for:\r\n- Opportunity Solution Tree facilitation\r\n- Assumption mapping and test planning\r\n- Problem validation interviews and evidence synthesis\r\n- Solution validation with prototypes/experiments\r\n- Discovery sprint planning and outputs\r\n\r\n## Core Discovery Workflow\r\n\r\n1. Define desired outcome\r\n- Set one measurable outcome to improve.\r\n- Establish baseline and target horizon.\r\n\r\n2. Build Opportunity Solution Tree (OST)\r\n- Outcome -> opportunities -> solution ideas -> experiments\r\n- Keep opportunities grounded in user evidence, not internal opinions.\r\n\r\n3. Map assumptions\r\n- Identify desirability, viability, feasibility, and usability assumptions.\r\n- Score assumptions by risk and certainty.\r\n\r\nUse:\r\n```bash\r\npython3 scripts/assumption_mapper.py assumptions.csv\r\n```\r\n\r\n4. Validate the problem\r\n- Conduct interviews and behavior analysis.\r\n- Confirm frequency, severity, and willingness to solve.\r\n- Reject weak opportunities early.\r\n\r\n5. Validate the solution\r\n- Prototype before building.\r\n- Run concept, usability, and value tests.\r\n- Measure behavior, not only stated preference.\r\n\r\n6. Plan discovery sprint\r\n- 1-2 week cycle with explicit hypotheses\r\n- Daily evidence reviews\r\n- End with decision: proceed, pivot, or stop\r\n\r\n## Opportunity Solution Tree (Teresa Torres)\r\n\r\nStructure:\r\n- Outcome: metric you want to move\r\n- Opportunities: unmet customer needs/pains\r\n- Solutions: candidate interventions\r\n- Experiments: fastest learning actions\r\n\r\nQuality checks:\r\n- At least 3 distinct opportunities before converging.\r\n- At least 2 experiments per top opportunity.\r\n- Tie every branch to evidence source.\r\n\r\n## Assumption Mapping\r\n\r\nAssumption categories:\r\n- Desirability: users want this\r\n- Viability: business value exists\r\n- Feasibility: team can build/operate it\r\n- Usability: users can successfully use it\r\n\r\nPrioritization rule:\r\n- High risk + low certainty assumptions are tested first.\r\n\r\n## Problem Validation Techniques\r\n\r\n- Problem interviews focused on current behavior\r\n- Journey friction mapping\r\n- Support ticket and sales-call synthesis\r\n- Behavioral analytics triangulation\r\n\r\nEvidence threshold examples:\r\n- Same pain repeated across multiple target users\r\n- Observable workaround behavior\r\n- Measurable cost of current pain\r\n\r\n## Solution Validation Techniques\r\n\r\n- Concept tests (value proposition comprehension)\r\n- Prototype usability tests (task success/time-to-complete)\r\n- Fake door or concierge tests (demand signal)\r\n- Limited beta cohorts (retention/activation signals)\r\n\r\n## Discovery Sprint Planning\r\n\r\nSuggested 10-day structure:\r\n- Day 1-2: Outcome + opportunity framing\r\n- Day 3-4: Assumption mapping + test design\r\n- Day 5-7: Problem and solution tests\r\n- Day 8-9: Evidence synthesis + decision options\r\n- Day 10: Stakeholder decision review\r\n\r\n## Tooling\r\n\r\n### `scripts/assumption_mapper.py`\r\n\r\nCLI utility that:\r\n- reads assumptions from CSV or inline input\r\n- scores risk/certainty priority\r\n- emits prioritized test plan with suggested test types\r\n\r\nSee `references/discovery-frameworks.md` for framework details."
  user_template: ""
variables: []
install:
  steps:
    - title: Download & unzip
      detail: >-
        Download the .zip package from SkillVault and unzip it. The product-discovery/ folder
        contains SKILL.md plus any companion scripts, references and assets.
    - title: Copy into your agent skills directory
      detail: >-
        Move the folder into your agent's skills directory — e.g. ~/.claude/skills/ (Claude Code),
        .codex/skills/, .cursor/skills/ or your project's .claude/skills/ folder — then restart or
        reload the session.
    - title: Use it
      detail: >-
        Describe the task in natural language; the agent loads the skill automatically when the
        request matches the skill description.
usage:
  steps: []
metadata:
  author: SkillVault
  source: https://skillvault.top/skills/product-discovery
  exported_at: "2026-11-03T07:43:39.688Z"