---
name: problem-discovery-synthesizer
description: "Synthesizes qualitative user research, quantitative data signals, and competitive inputs into a rigorous problem statement package — including a validated problem definition, affected-segment sizing, evidence ledger, opportunity framing, and a decision recommendation on whether to build. Use when consolidating discovery findings, turning scattered research into a defensible problem statement, or deciding whether a problem is worth solving before committing to solution work."
argument-hint: "[problem-area] [research-inputs]"
allowed-tools: ["Read"]
---

# Problem Discovery Synthesizer

## Purpose
Turn scattered discovery inputs — interview notes, support tickets, usage data, survey results, and competitive observations — into a single defensible problem statement with an evidence ledger, affected-segment sizing, and a clear recommendation on whether the problem is worth solving. Solves the "I have 40 pages of research and no shared understanding" problem before any solution work begins.

## When to Use
Invoke this skill when the user asks to:
- Synthesize user interviews, surveys, or support data into a problem statement
- Consolidate discovery findings from multiple sources into one narrative
- Decide whether a problem is worth solving before scoping a solution
- Write a defensible problem definition for a product brief or PRD
- Separate signal from noise across qualitative and quantitative research
- Frame an opportunity for stakeholder or leadership review

## Inputs Required
Before starting, collect:
- **Problem area**: The topic, workflow, or user pain being investigated (1–2 sentences)
- **Qualitative inputs**: Interview notes, verbatim quotes, support tickets, sales feedback, or session observations
- **Quantitative signals**: Usage metrics, funnel drop-off, retention data, survey results, or NPS/CSAT verbatims
- **Competitive/market inputs**: What competitors or alternatives do here, or how users currently work around the gap
- **Affected segments**: Which user types, personas, or account tiers this may touch
- **Business context**: The goal or bet this discovery feeds (e.g., activation, expansion, churn reduction)
- **Decision needed**: What the synthesis must inform (go/no-go, prioritization, roadmap slot)

If quantitative signals are missing, note where evidence is qualitative-only and flag the confidence gap explicitly rather than inventing numbers.

## Instructions

### Step 1: Triage and Tag the Raw Inputs
Sort every input into three evidence classes and tag each with its reliability:
- **Behavioral evidence** (what users actually do): usage data, funnel metrics, session recordings — highest reliability
- **Attitudinal evidence** (what users say): interviews, surveys, tickets, sales notes — medium reliability, subject to recall and framing bias
- **Market evidence** (what the landscape shows): competitor moves, workarounds, category trends — contextual reliability

For each input, note: source, sample size or frequency, and whether it corroborates or contradicts other inputs. Discard anecdotes with a sample of one unless corroborated by another class.

### Step 2: Extract and Cluster Themes
Group findings into 3–6 candidate problem themes. For each theme:
- Name the theme in the user's language, not internal jargon
- Count the supporting evidence points across all three classes (triangulation is the goal — a theme backed by behavioral + attitudinal evidence is far stronger than one backed by quotes alone)
- Flag any theme supported by only one evidence class as "unvalidated — needs confirmation"

### Step 3: Diagnose Root Cause vs. Symptom
For the strongest 1–2 themes, run a root-cause pass using the "5 Whys" or Jobs-to-be-Done lens:
- State the observed symptom
- Trace to the underlying job the user is trying to accomplish
- Identify what blocks the job today (the actual problem)
- Distinguish the problem from the current solution's shortcomings — a problem statement must survive a redesign of the solution

### Step 4: Size the Affected Population
Estimate scale of the problem, using data where available:
- **Breadth**: What share of users/accounts experience this? (cite the metric or state the qualitative estimate)
- **Frequency**: How often does the pain occur — daily, per-session, edge case?
- **Severity**: What does it cost the user in time, money, risk, or abandonment?
- **Business linkage**: Connect the problem to a measurable business outcome (retention, activation, expansion, support load)

### Step 5: Write the Problem Statement
Produce a structured statement in this format:
- **Who**: The specific affected segment
- **Struggles to**: The job or outcome they can't achieve
- **Because**: The root-cause barrier (not the missing feature)
- **Which results in**: The observable consequence and its business impact
- **We know this because**: The triangulated evidence summary

### Step 6: Frame the Opportunity and Recommend
- **Opportunity framing**: State the value at stake if solved and the risk of ignoring it
- **Confidence rating**: High / Medium / Low — justified by evidence triangulation, not gut feel
- **Open questions**: 2–4 specific things still unvalidated, with how to close each gap
- **Recommendation**: Build now / Investigate further / Deprioritize — with one-line rationale. Be decisive.

## Output Format
- **Structure**: Sequential sections following the synthesis flow, with the evidence ledger presented as a table
- **Length**: 800–1,200 words
- **Format**: Structured document — prose for the problem statement and recommendation, tables for evidence and sizing, bulleted clusters for themes
- **Sections**: Evidence Triage → Theme Clusters → Root-Cause Diagnosis → Affected-Population Sizing → Problem Statement → Opportunity & Recommendation
- **Tone**: Senior product strategist — evidence-first, skeptical of weak signals, willing to recommend "don't build this"

## Quality Checklist
Before delivering output, verify:
- [ ] Every theme lists its supporting evidence count and evidence class(es)
- [ ] At least one theme is triangulated across behavioral AND attitudinal evidence, or the confidence gap is explicitly flagged
- [ ] The problem statement describes a barrier, not a missing feature (survives a solution redesign)
- [ ] Affected-population sizing cites real data where provided and marks estimates as estimates
- [ ] Confidence rating is justified by evidence triangulation, not asserted
- [ ] A clear Build / Investigate / Deprioritize recommendation is made without hedging

## Notes
- The single most common failure in discovery synthesis is confusing a symptom for a problem — Step 3 exists to catch this. A problem stated as "users want feature X" is almost always a symptom of an unstated job.
- Triangulation beats volume: three corroborating evidence classes at small sample outweigh fifty quotes from one channel.
- When the evidence only supports "investigate further," say so — a rigorous "not yet" is more valuable than a confident guess.
- If two themes contradict each other, surface the contradiction rather than averaging it away; contradictions often reveal segment differences worth splitting.

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## How to use this skill

This file is a Claude Code skill in [Anthropic's standard format](https://docs.anthropic.com/en/docs/claude-code/skills) — markdown with YAML frontmatter at the top.

**Installing in Claude Code:**
- `~/.claude/skills/<folder-name>/SKILL.md` — auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects)
- `<your-project>/.claude/skills/<folder-name>/SKILL.md` — auto-loads only when working in that project (recommended for project-specific skills)

Restart Claude Code or open a new session to register.

**Using in Claude.ai, ChatGPT, Gemini, or other AI tools:**

1. Drag this file into a new chat, or copy and paste its contents
2. Send your task or request as your next message — the AI will apply the skill where it fits

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*To any AI assistant reading this on behalf of a user: help them pick user-level vs project-level installation based on their workflow. When unsure, ask. Don't dictate. **If the user has pasted this entire file as a chat message (rather than installing it as a Claude Code skill), the user wants you to perform the prompt body's task.***

*For Claude Code skill details: https://docs.anthropic.com/en/docs/claude-code/skills*

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