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Continuous Discovery Habits Framework

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Tell us your goal and what customers have been saying; get back a one-page map of what people actually struggle with, ranked by what to tackle first.

Originally by wondelai · MIT

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npx agentalley add continuous-discovery

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Who is stuck, and on what

You keep building or offering things based on gut feeling and whoever complains loudest, and half of it goes nowhere. You want a clear picture of what your customers actually struggle with so you stop wasting time and money on stuff nobody wants.

What it gives you

A one-page plan connecting your goal to your customers' real needs, ranked by which to tackle first, plus a few small, cheap tests to run before you commit to any of them.

When NOT to use it

It won't talk to your customers for you or run the tests — it turns what you already know and have heard into a clear, prioritized plan.

The whole source

No sign-in, no blur, nothing truncated
continuous-discovery/SKILL.md203 lines14.9 KBRawView on GitHub
Frontmatter — 4 properties
namecontinuous-discovery
descriptionBuild 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.
licenseMIT
metadata author: wondelai version: "1.4.0
1---
2name: continuous-discovery
3description: '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.'B1 — Line is 741 characters — unreadable by eye
4license: MIT
5metadata:
6 author: wondelai
7 version: "1.4.0"
8---A5 — No allowed-tools declared — no way to tell what this skill may touch
9 
10# Continuous Discovery Habits Framework
11 
12Framework 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.
13 
14## Core Principle
15 
16**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).
17 
18## Scoring
19 
20**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.B1 — Line is 546 characters — unreadable by eye
21 
22## Framework
23 
24### 1. Opportunity Solution Trees
25 
26**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.
27 
28**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.
29 
30**Key insights:**
31- Four layers: Outcome > Opportunities > Solutions > Experiments
32- Opportunities are customer needs, pain points, and desires — framed from the customer's perspective
33- The tree is a living artifact, updated weekly as the team learns
34- Break large opportunities into smaller sub-opportunities to make them actionable
35- Pursue multiple opportunities simultaneously — don't bet everything on one
36 
37**Product applications:**
38 
39| Context | Application | Example |
40|---------|-------------|---------|
41| Quarterly planning | Map the opportunity space before committing to features | "Increase trial-to-paid conversion" → discover why users don't convert |
42| Feature prioritization | Compare solutions across opportunities for the highest-leverage bet | Three solutions for "can't find content" vs. two for "confusing onboarding" |
43| Stakeholder alignment | Use the tree as the shared strategy visual | Walk leadership through why you chose opportunity X over Y |
44 
45**Ethical boundary:** Never cherry-pick opportunities to justify a predetermined solution — the tree must reflect needs discovered through research.
46 
47See [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.
48 
49### 2. Experience Mapping
50 
51**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.
52 
53**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.
54 
55**Key insights:**
56- Map the current state, not a future ideal — understand reality first
57- Include actions, thoughts, and feelings at each step
58- Build collaboratively with the full trio, sourced from interview data, not assumptions
59- Experience maps cover the customer's full experience; journey maps cover only your product's touchpoints
60- Pain points and high-emotion moments become OST opportunities
61 
62**Product applications:**
63 
64| Context | Application | Example |
65|---------|-------------|---------|
66| New problem space | Map end-to-end before designing | How a small business owner handles invoicing, from creation to chasing payment |
67| Churn analysis | Map churned users' experience to find failure points | Users abandon onboarding at step 4 — they lack data they need on hand |
68| Cross-functional alignment | Build the map together | A three-hour collaborative session produces one shared reference artifact |
69 
70See [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.
71 
72### 3. Interview Snapshots
73 
74**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.
75 
76**Why it works:** Customers are poor predictors of their own future behavior; grounding insights in real past events reveals what they actually did and felt, and snapshots turn each interview into a growing library of evidence.
77 
78**Key insights:**
79- Ask about specific past behavior: "Tell me about the last time you..." not "Would you use...?"
80- Each snapshot captures the story, key quotes, opportunities identified, and an identifier
81- The trio interviews together so insights aren't lost in translation
82- Automate recruitment so interviews happen weekly without heroic effort
83- Patterns across snapshots reveal opportunities; single interviews only reveal stories
84 
85**Product applications:**
86 
87| Context | Application | Example |
88|---------|-------------|---------|
89| Weekly cadence | Standing 30-minute interview slots | Recruit via in-app prompt; rotate who leads |
90| Opportunity discovery | Extract needs from stories onto the OST | A data-export workaround becomes an opportunity node |
91| Team alignment | Share snapshots visibly | A board where snapshots accumulate and patterns emerge |
92 
93**Ethical boundary:** Never lead participants toward conclusions — ask open-ended questions about past behavior and let the story reveal what matters.
94 
95See [references/interview-snapshots.md](references/interview-snapshots.md) when running interviews or setting up recruitment — adds story-based interview structure, the one-page snapshot format, synthesis across snapshots, and how to automate weekly recruitment.
96 
97### 4. Assumption Testing
98 
99**Core concept:** Before building, identify the assumptions a solution depends on, map them by importance and evidence, then run small fast tests on the riskiest ones first.
100 
101**Why it works:** Every solution sits on a stack of desirability, viability, feasibility, and usability assumptions; most teams test none — or only the easy ones — and invest months in solutions built on false premises.
102 
103**Key insights:**
104- Four assumption types: desirability (do they want it?), viability (can we sustain it?), feasibility (can we build it?), usability (can they use it?)
105- Map on a 2x2: importance vs. evidence; high-importance, low-evidence = leap-of-faith assumptions to test first
106- Design the smallest test that generates evidence: one-question surveys, painted-door tests, prototypes, data mining
107- Set success criteria before running the test: "validated if..."
108- One assumption test should take days, not weeks
109 
110**Product applications:**
111 
112| Context | Application | Example |
113|---------|-------------|---------|
114| Before building | Test the riskiest assumption of the top candidates | "Users will share reports with their manager" → painted-door button before building sharing |
115| Comparing solutions | Test each candidate's riskiest assumption to eliminate weak options fast | A's riskiest assumption fails, B's passes → pursue B |
116| De-risking a roadmap | Find untested assumptions hiding in committed features | Q3 feature assumes users want real-time notifications — no evidence yet |
117 
118**Ethical boundary:** Never deceive participants — painted-door tests should say the feature is coming soon, not fake functionality without disclosure.
119 
120See [references/assumption-mapping.md](references/assumption-mapping.md) when designing a test for a risky assumption — adds the four assumption types in depth, the importance-vs-evidence 2x2, the test-design menu, and how to set success criteria for leap-of-faith assumptions.
121 
122### 5. Prioritizing Opportunities
123 
124**Core concept:** Compare opportunities against each other — not in isolation — using opportunity size, market, company, and customer factors to find the highest-leverage bets.
125 
126**Why it works:** Teams default to the loudest stakeholder, recency bias, or gut feel; structured head-to-head comparison forces explicit tradeoff discussions and surfaces disagreements before implementation.
127 
128**Key insights:**
129- Relative comparison beats independent scoring
130- Size opportunities by how many customers are affected, how often, how severely
131- Weigh strategy alignment, team capability, and existing evidence
132- Make a good-enough decision quickly, then learn fast — avoid analysis paralysis
133- Revisit the ranking as new evidence arrives
134 
135**Product applications:**
136 
137| Context | Application | Example |
138|---------|-------------|---------|
139| Quarterly planning | Rank the top 5-7 OST opportunities | "Can't find content" vs. "no real-time collaboration" via structured criteria |
140| Sprint planning | Pick the opportunity with the strongest current evidence | Choose where you have the most interview data and a testable solution |
141| Portfolio decisions | Spread effort by risk and impact | 60% high-confidence, 30% medium, 10% exploratory |
142 
143See [references/prioritization-methods.md](references/prioritization-methods.md) when ranking your top opportunities — adds the opportunity-sizing method, the compare-and-contrast technique, how to weigh data, and how to avoid analysis paralysis.
144 
145### 6. Building the Habit
146 
147**Core concept:** Continuous discovery only works as a sustainable weekly habit for the trio — automate recruitment, create lightweight rituals, and embed discovery into the existing workflow rather than treating it as extra work.
148 
149**Why it works:** Discovery that depends on "finding time" loses to delivery pressure every week; structural support (automated recruitment, standing slots, shared artifacts) removes the per-week decision so the habit survives and compounds.
150 
151**Key insights:**
152- The whole trio participates — not just the PM
153- Automate recruitment: in-app intercepts, advisory panels, scheduling tools that fill slots
154- Block recurring calendar time — discovery that depends on "finding time" never happens
155- Fill in the snapshot immediately after the interview, not days later
156- Start with one interview per week; connect insights to the OST and from there into sprint planning
157 
158**Product applications:**
159 
160| Context | Application | Example |
161|---------|-------------|---------|
162| Team kickoff | Establish cadence in week one | Automated recruitment, blocked Thursday slot, snapshot template |
163| Scaling discovery | Grow from one to three interviews weekly | Add a churned-user slot and a prospect slot |
164| Manager support | Leaders protect time and ask for evidence | "What did you learn from interviews this week?" in every 1:1 |
165 
166**Ethical boundary:** Respect participant time — keep interviews to 30 minutes, compensate fairly, and never disguise a sales pitch as discovery.
167 
168See [references/case-studies.md](references/case-studies.md) when adapting the habit to your context — worked walkthroughs of continuous discovery in B2B SaaS, consumer mobile, platform, and growth teams.
169 
170## Common Mistakes
171 
172| Mistake | Why It Fails | Fix |
173|---------|-------------|-----|
174| Discovery as a phase before development | Insights go stale; team builds on old assumptions | Embed discovery into every week alongside delivery |
175| Only the PM talks to customers | Designer and engineer lose context in translation | The full trio interviews together |
176| Jumping from outcome to solutions | Skips the opportunity space | Build an OST to make it explicit |
177| Asking customers what they want | You get feature requests, not needs | Story-based interviewing: "Tell me about the last time..." |
178| Testing easy assumptions, not risky ones | False confidence; the fatal assumption goes untested | Map by importance and evidence; test high-risk first |
179| Scoring opportunities in isolation | Everything looks important | Compare head-to-head with structured criteria |
180| Interview burst, then stopping | No compounding learning | Automate recruitment; block recurring time |
181 
182## Quick Diagnostic
183 
184| Question | If No | Action |
185|----------|-------|--------|
186| One customer conversation per week minimum? | Decisions lack fresh evidence | Automate recruitment; block a weekly slot |
187| A living Opportunity Solution Tree? | Strategy is implicit and unshared | Build an OST from your outcome and interview data |
188| Full trio in interviews? | Insights filtered through one person | Invite the designer and engineer to the next one |
189| Testing assumptions before building? | Betting on untested premises | Map your next feature's assumptions; test the riskiest |
190| Can you trace a shipped feature to a customer opportunity? | Delivery disconnected from discovery | Link backlog items to OST opportunities |
191| Interview snapshots visible to the whole team? | Knowledge trapped in one head | Shared snapshot board, filled after each interview |
192| Comparing opportunities, not just listing them? | Prioritization by opinion | Run a structured comparison on your top 5 |
193 
194## Further Reading
195 
196Based on the continuous discovery framework developed by Teresa Torres:
197 
198- [*"Continuous Discovery Habits: Discover Products that Create Customer Value and Business Value"*](https://www.amazon.com/Continuous-Discovery-Habits-Discover-Products/dp/1736633309?tag=wondelai00-20) by Teresa Torres
199 
200## About the Author
201 
202**Teresa Torres** is an author, speaker, and coach who has helped hundreds of product teams — from startups to Capital One and Calendly — adopt continuous discovery. She created the Opportunity Solution Tree, writes the widely read Product Talk blog, and distilled her coaching practice into *Continuous Discovery Habits*.
203 

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