opportunity-discovery

name

opportunity-discovery

description

Top-level evidence-aware orchestrator and router for product discovery. Classifies user input (product ideas, feedback, interview notes, or survey data), evaluates evidence readiness, routes to downstream skills, and outputs a Decision Brief detailing what can be concluded, what is only a hypothesis, and the smallest next validation step. Use as the primary entry point when given a new product idea, customer quote, or discovery material and asked what to validate next.

Opportunity Discovery & Decision Brief Generator

Determine what you can and cannot conclude before building or investing, and get the smallest next validation step.

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Use this when

• You have a new product idea, feature suggestion, customer quote, interview transcript, or survey dataset and want an evidence-aware decision brief.
• You don't know which specific downstream JTBD skill (switch-interview, context-explorer, forces-analyzer, job-definer, opportunity-calculator, growth-strategist) to start with.
• You want to avoid spending time or money building an idea before verifying whether real customer evidence exists.

Don't use this when

• You already know you need a specific downstream skill (e.g., you explicitly want an 8-stage job map -> use jtbd-job-mapper).
• You want an AI to make a speculative binary investment prediction ("Build it" or "Don't build it") without customer data.

Minimum input

• Minimum Input: Any text input—from a raw 1-sentence product idea ("I want to build an AI diary app") to a full interview transcript or quantitative survey file. Supports 6 input modes:
  1. idea_only: Raw product/feature idea with zero customer facts.
  2. customer_signal: Raw customer quotes, support tickets, reviews, or sales feedback.
  3. research_evidence: Interview transcripts or structured research notes.
  4. ready_for_outcome_ranking: Outcome survey ratings (1-10).
  5. ready_for_strategy_assessment: Ranked outcomes + segment + price/cost/performance data.
  6. partial_market_evidence: Price/cost materials without outcome survey ratings.

What you get

1. Decision Brief: A clear 5-part summary of current readiness stage, direct evidence vs hypotheses, what can and cannot be concluded, and the recommended action.
2. Smallest Next Validation Step: A concrete, actionable research task (e.g., "Interview 5 target users who faced this problem in the last 30 days").
3. Skill Routing Recommendation: Identification of the exact downstream skill to execute next.

Quick prompt

"I have this product idea/feedback: '[Paste idea or quote]'. Evaluate evidence readiness, tell me what I can conclude, and give me the smallest next validation step."

What to do next

• Output recommends an interview? Run jtbd-switch-interview.
• Output recommends context extraction? Run jtbd-context-explorer.
• Output recommends analyzing switching inertia? Run jtbd-forces-analyzer.
• Output recommends calculating scores? Run jtbd-opportunity-calculator.

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🚦 Input Classification & Routing Matrix

Input Tier	Source Characteristics	System Assessment & Readiness Stage	Downstream Skill Routing
idea_only	Solution idea or feature proposal; no customer facts.	idea_only (Do not build yet; validate problem existence).	jtbd-switch-interview (Formulate interview questions for target users).
customer_signal	Reviews, support tickets, complaints, or feature requests.	anecdotal_signal (Extract real-world context & workarounds).	jtbd-context-explorer (Extract context, constraints, and workarounds).
research_evidence	Interview transcripts containing current tool & prospective tool.	evidence_emerging (Map customer switching forces or functional jobs).	jtbd-forces-analyzer or jtbd-job-definer.
ready_for_outcome_ranking	Structured 1-10 Importance & Satisfaction outcome survey ratings.	ready_for_outcome_ranking (Compute opportunity rankings).	jtbd-opportunity-calculator (Compute mathematical Opportunity Scores).
ready_for_strategy_assessment	Ranked outcomes + target segment + price/cost/performance evidence.	ready_for_strategy_assessment (Evaluate growth strategy matrix).	jtbd-growth-strategist (Evaluate growth strategy prerequisites).
partial_market_evidence	Price, cost, or competitor materials without outcome survey ratings or target segment.

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Output Format (Decision Brief)

## 📋 Opportunity Decision Brief

### 🚦 Current Assessment
- **Readiness Stage**: [idea_only | anecdotal_signal | evidence_emerging | ready_for_outcome_ranking | ready_for_strategy_assessment | not_decision_ready]
- **Current Assessment**: [Actionable assessment statement, e.g., "Do not start development yet; validate problem frequency and current workarounds."]
- **Confidence Rating**: [low | medium | high] (Evaluated across Traceability, Relevance, Coverage, Consistency, Decision Alignment)

### ✅ What is Known (Direct Evidence)
- [Source-linked customer quote or verified fact]

### 💡 What is Only a Hypothesis
- [Unverified assumption or solution proposal]

### 🔍 What You CAN and CANNOT Conclude Right Now
- **CAN Conclude**: [Explicit valid conclusion based on current data]
- **CANNOT Conclude**: [Explicit boundary warning of unverified aspects]

### 🚀 Recommended Smallest Next Validation Step
"[Concrete, actionable validation task, e.g., 'Interview 5 target users who encountered this situation in the past 30 days.']"

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### 📊 Structured Decision Brief Metadata

```yaml
decision_brief:
  current_stage: idea_only | anecdotal_signal | evidence_emerging | ready_for_outcome_ranking | ready_for_strategy_assessment | not_decision_ready
  decision_scope: switch_interview | context_exploration | switching_forces | job_definition | outcome_ranking | strategy_assessment | none
  evidence:
    direct: []
    inferred: []
  hypotheses: []
  unknowns: []
  current_assessment: ""
  confidence: low | medium | high
  what_you_can_conclude_now: []
  what_you_cannot_conclude_yet: []
  recommended_action: ""
  smallest_next_validation_step: ""
  recommended_skill: "jtbd-switch-interview | jtbd-context-explorer | jtbd-forces-analyzer | jtbd-job-definer | jtbd-opportunity-calculator | jtbd-growth-strategist"

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## Reference

Read `principles/evidence-model.md` before:
- Classifying an input into an evidence tier
- Formulating a `decision_brief`
- Blocking premature strategic or build verdicts when evidence is insufficient