Continuous DiscoveryCAUTION

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The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: continuous-discovery
description: 'Build a weekly cadence of customer touchpoints using Opportunity Solution Trees, assumption mapping, and interview snapshots. Use when the user mentions "continuous discovery", "opportunity solution tree", "weekly interviews", "assumption testing", "discovery habits", "product trio", "outcome-based roadmap", "how do I talk to customers regularly", "we keep building things nobody uses", or "connect research to the roadmap". Also trigger when setting up regular customer feedback loops, prioritizing which experiments to run, or tying discovery insights to delivery work. Covers experience mapping, co-creation, and prioritizing opportunities. For interview technique, see mom-test. For team structure, see inspired-product.'
license: MIT
metadata:
  author: wondelai
  version: "1.4.0"
---

# Continuous Discovery Habits Framework

Framework for building a sustainable weekly practice of customer discovery that keeps product teams progressing toward desired outcomes. Discovery is not a phase before development — it is embedded in the ongoing rhythm of product work so every decision is informed by fresh evidence.

## Core Principle

**Good product discovery requires a continuous cadence, not a one-time event.** Teams that talk to customers every week, map opportunities visually, and test assumptions before building consistently outperform teams that rely on intuition, stakeholder opinions, or quarterly research cycles. The benchmark: at least one customer touchpoint per week, every week, by the product trio (product manager, designer, engineer).

## Scoring

**Goal: 10/10.** Score a discovery practice by the seven Quick Diagnostic rows below — start at 3, add 1 point per row answered "yes" (max 10). Bands: **9-10** = weekly cadence, a living Opportunity Solution Tree, systematic assumption testing, and every shipped feature traceable to a customer opportunity; **5-6** = some discovery happening but ad hoc, PM-only, or disconnected from delivery; **≤3** = intuition- and stakeholder-driven with no regular customer contact. Report the current score, the failing rows, and the specific fix for each.

## Framework

### 1. Opportunity Solution Trees

**Core concept:** An Opportunity Solution Tree (OST) visually connects a desired outcome (top) to customer opportunities (middle) to potential solutions and experiments (bottom), making implicit product thinking explicit and shared.

**Why it works:** Most teams jump from business outcome straight to solutions, skipping the customer need entirely; the OST forces understanding of the opportunity space first, preventing features nobody wants.

**Key insights:**
- Four layers: Outcome > Opportunities > Solutions > Experiments
- Opportunities are customer needs, pain points, and desires — framed from the customer's perspective
- The tree is a living artifact, updated weekly as the team learns
- Break large opportunities into smaller sub-opportunities to make them actionable
- Pursue multiple opportunities simultaneously — don't bet everything on one

**Product applications:**

| Context | Application | Example |
|---------|-------------|---------|
| Quarterly planning | Map the opportunity space before committing to features | "Increase trial-to-paid conversion" → discover why users don't convert |
| Feature prioritization | Compare solutions across opportunities for the highest-leverage bet | Three solutions for "can't find content" vs. two for "confusing onboarding" |
| Stakeholder alignment | Use the tree as the shared strategy visual | Walk leadership through why you chose opportunity X over Y |

**Ethical boundary:** Never cherry-pick opportunities to justify a predetermined solution — the tree must reflect needs discovered through research.

See [references/opportunity-trees.md](references/opportunity-trees.md) when building or auditing a tree — adds the 4-layer diagram, good-vs-poor outcome tables, solution-generation techniques, a weekly update rhythm, healthy/dying-tree signals, two worked examples, and four anti-patterns.

### 2. Experience Mapping

**Core concept:** Current-state experience maps capture how customers accomplish a goal today, step by step, revealing pain points that become opportunities on the tree.

**Why it works:** Teams assume they understand the customer's current experience; mapping it from interview data exposes gaps, workarounds, and emotions invisible from inside the building.

**Key insights:**
- Map the current state, not a future ideal — understand reality first
- Include actions, thoughts, and feelings at each step
- Build collaboratively with the full trio, sourced from interview data, not assumptions
- Experience maps cover the customer's full experience; journey maps cover only your product's touchpoints
- Pain points and high-emotion moments become OST opportunities

**Product applications:**

| Context | Application | Example |
|---------|-------------|---------|
| New problem space | Map end-to-end before designing | How a small business owner handles invoicing, from creation to chasing payment |
| Churn analysis | Map churned users' experience to find failure points | Users abandon onboarding at step 4 — they lack data they need on hand |
| Cross-functional alignment | Build the map together | A three-hour collaborative session produces one shared reference artifact |

See [references/experience-mapping.md](references/experience-mapping.md) when mapping a new problem space or churn flow — adds the current-state map template, the experience-vs-journey-map distinction, and the collaborative mapping exercise.

### 3. Interview Snapshots

**Core concept:** Story-based interviews capture specific past experiences (not opinions or predictions), and each interview is synthesized into a one-page snapshot the whole team can absorb and reference.

**Why it works:** Customers are poor predictors of their own future behavior; grounding insights in real past events reveals what they ac

CAUTIONgrade B · trust 89/100 Install with care. The audit found things worth knowing before you trust its output.

What the source does

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none-observed

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none-observed

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Findings (5)

.agents/skills/37signals-way

.agents/skills/architecture-optimization

.agents/skills/blue-ocean-strategy

.agents/skills/clean-architecture

.agents/skills/clean-code

Gates applied: no_behavioural_pass.