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Continuous Discovery

Community
menkesu
continuous-discovery

Implements Teresa Torres' continuous discovery habits for weekly customer contact, opportunity solution trees, and assumption testing. Use when building discovery processes, conducting user research, validating assumptions, or establishing product trio workflows.

Overview

Publishermenkesu
Repositoryawesome-pm-skills
Skill namecontinuous-discovery
Stars
406
Forks
120
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by menkesu on GitHub. Read the source before you install it.

Installation

Install the Continuous Discovery AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/menkesu/awesome-pm-skills.git /tmp/awesome-pm-skills
mkdir -p .claude/skills
cp -r /tmp/awesome-pm-skills/continuous-discovery .claude/skills/continuous-discovery
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Continuous Discovery in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Continuous Discovery on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Continuous Discovery is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Continuous Discovery Habits

When This Skill Activates

Claude uses this skill when:

  • Setting up discovery processes
  • Planning weekly user research
  • Creating opportunity solution trees
  • Testing assumptions
  • Building product trio workflows
  • Prioritizing discovery activities

Core Frameworks

1. Continuous Discovery Habits (Source: Teresa Torres)

The Core Principle:

"At a minimum, weekly touchpoints with customers by the team building the product, where they conduct small research activities in pursuit of a desired outcome."

The Three Pillars:

  1. Weekly customer contact by the product trio (PM, designer, engineer)
  2. Opportunity solution trees to visualize discovery
  3. Assumption testing before building

Use when: Establishing discovery processes or improving product decisions


2. The Product Trio

The Team:

  • Product Manager: Ensures business viability
  • Designer: Ensures usability and desirability
  • Engineer: Ensures feasibility

Why Together:

"When a designer, engineer, and PM collaborate on discovery, you get better decisions faster. Each brings a unique lens."

How:

  • All three participate in customer interviews
  • All three analyze research together
  • All three generate solutions together
  • All three test assumptions together

3. Opportunity Solution Trees

The Structure:

Outcome (top)
Opportunities (customer needs/pain points)
Solutions (possible ways to address)
Assumptions (what needs to be true)
Experiments (how to test)

Use when: Need to visualize the path from outcome to solution

Example:

Outcome: Increase retention to 80%
Opportunity: Users forget to use the product
Solution: Daily email reminder
Assumption: Users check email daily
Experiment: Survey 20 users about email habits

4. Interview Snapshot

The One-Pager: After each interview, create a snapshot capturing:

  • Date & Participant: Who and when
  • Key Insights: 3-5 main takeaways
  • Opportunities: Customer needs/pain points discovered
  • Quotes: Verbatim customer language
  • Next Steps: What to test or explore next

Why: Keeps learning accessible to the whole team


5. Assumption Testing

The Progression:

Story → Assumptions → Tests → Evidence

Question to Ask:

"What needs to be true for this solution to work?"

Test Types (by risk/cost):

  1. One-question surveys (lowest risk)
  2. Customer interviews
  3. Prototypes/mockups
  4. Concierge tests (manual behind-the-scenes)
  5. Wizard of Oz (fake the feature)
  6. Live data tests (build and measure)

Rule: Test highest-risk assumptions first with lowest-cost method


Decision Trees

Should I Build This Feature?

Do we have a clear outcome?
├─ No → Define outcome first
└─ Yes → Have we interviewed 6+ customers?
    ├─ No → Do discovery first
    └─ Yes → Have we identified opportunities?
        ├─ No → Map opportunities
        └─ Yes → Have we tested key assumptions?
            ├─ No → Test assumptions first
            └─ Yes → Build it!

How Should I Test This Assumption?

What's the risk if we're wrong?
├─ Low risk → Build and ship (reversible)
└─ High risk → How much does testing cost?
    ├─ Low cost → Interview 5 users
    ├─ Medium cost → Prototype test
    └─ High cost → Still cheaper than building wrong thing

Action Templates

Template: Weekly Discovery Plan

markdown
# Weekly Discovery Plan - Week of [Date]

## Outcome We're Pursuing
[e.g., Increase activation rate to 50%]

## This Week's Focus
**Opportunity:** [Which pain point are we exploring?]
**Solution:** [Which solution are we considering?]
**Key Assumption:** [What needs to be true?]

## Discovery Activities (Minimum 1 per week)

### Monday-Wednesday: Research
- [ ] Interview 1: [Participant profile] - [PM/Designer/Engineer attending]
- [ ] Interview 2: [Participant profile] - [PM/Designer/Engineer attending]
- [ ] Interview 3: [Participant profile] - [PM/Designer/Engineer attending]

### Thursday: Synthesis
- [ ] Product trio synthesis session (30 min)
- [ ] Create/update interview snapshots
- [ ] Update opportunity solution tree
- [ ] Identify new assumptions to test

### Friday: Planning
- [ ] Review evidence collected
- [ ] Decide: build, test more, or pivot?
- [ ] Plan next week's discovery activities

## Interview Snapshots
[Link to snapshots folder]

## Opportunity Solution Tree
[Link to latest tree]

Template: Interview Snapshot

markdown
# Interview Snapshot - [Date]

## Participant
- **Name/ID:** [Anonymized if needed]
- **Role:** [Job title/persona]
- **Context:** [Relevant background]

## Interview Focus
[What we were trying to learn]

## Key Insights
1. [First major insight]
2. [Second major insight]
3. [Third major insight]

## Opportunities Discovered
- 📍 [Pain point or unmet need #1]
- 📍 [Pain point or unmet need #2]
- 📍 [Pain point or unmet need #3]

## Memorable Quotes
> "[Exact customer words that capture key point]"

> "[Another powerful quote]"

## Updated Assumptions
- ✅ Validated: [What we confirmed]
- ❌ Invalidated: [What we disproved]
- ❓ New: [New assumptions to test]

## Next Steps
- [ ] [Specific action based on learning]
- [ ] [Another action]

## Attending
- [PM name]
- [Designer name]
- [Engineer name]

Template: Opportunity Solution Tree

markdown
# Opportunity Solution Tree - [Product/Feature Name]

## Outcome
🎯 **[Business outcome we're driving]**
[Specific, measurable, time-bound]

---

## Opportunities (Customer Needs/Pain Points)

### Opportunity 1: [Customer problem]
**Evidence:** [3-5 customer interviews, usage data, etc.]
**Impact:** [How big is this problem?]

#### Solutions Being Considered:
1. **[Solution A]**
   - Assumptions:
     - [ ] Assumption 1
     - [ ] Assumption 2
   - Tests: [How we'll validate]
   - Status: [Testing/Building/Shipped]

2. **[Solution B]**
   - Assumptions:
     - [ ] Assumption 1
     - [ ] Assumption 2
   - Tests: [How we'll validate]
   - Status: [Testing/Building/Shipped]

### Opportunity 2: [Another customer problem]
**Evidence:** [3-5 customer interviews, usage data, etc.]
**Impact:** [How big is this problem?]

[Continue for each opportunity...]

---

## Decision Log
- **[Date]:** Chose Solution A for Opportunity 1 because [evidence]
- **[Date]:** Decided to test Assumption X before building
- **[Date]:** Pivoted from Solution B to Solution C based on [learning]

Template: Assumption Test Plan

markdown
# Assumption Test Plan - [Feature/Solution Name]

## Solution Statement
[Brief description of what we're considering building]

## Key Assumptions

### Assumption 1: [High Risk]
**Statement:** [What needs to be true]
**If wrong:** [What's the impact?]
**Confidence:** [Low/Medium/High]

**Test Method:** [Interview/Survey/Prototype/etc.]
**Success Criteria:** [What would validate this?]
**Timeline:** [When we'll test]
**Owner:** [Who's running the test]

---

### Assumption 2: [Medium Risk]
**Statement:** [What needs to be true]
**If wrong:** [What's the impact?]
**Confidence:** [Low/Medium/High]

**Test Method:** [Interview/Survey/Prototype/etc.]
**Success Criteria:** [What would validate this?]
**Timeline:** [When we'll test]
**Owner:** [Who's running the test]

---

## Test Results

### Assumption 1 Results
**Date Tested:** [Date]
**Method Used:** [What we did]
**Sample Size:** [How many participants]

**Findings:**
- [Key finding 1]
- [Key finding 2]
- [Key finding 3]

**Decision:** ✅ Validated / ❌ Invalidated / ❓ Needs more testing
**Next Steps:** [What we'll do based on results]

---

### Assumption 2 Results
[Same structure as above]

Quick Reference

📅 Weekly Discovery Cadence

Every Week Minimum:

  • 3-5 customer touchpoints (interviews, observation, etc.)
  • Product trio participates together
  • Create interview snapshots
  • Update opportunity solution tree
  • Test at least 1 assumption

Every Month:

  • Review all evidence collected
  • Update outcomes if needed
  • Celebrate learning (not just building)

🎯 Discovery vs Delivery Balance

Good Discovery Practice:

  • ✅ Discovery happens weekly (not just quarterly)
  • ✅ Product trio does discovery together
  • ✅ Small tests before big builds
  • ✅ Evidence-based decisions
  • ✅ Comfortable saying "we learned that won't work"

Signs of Insufficient Discovery:

  • ❌ Only talking to customers after shipping
  • ❌ PM does all research alone
  • ❌ Building first, validating later
  • ❌ Opinion-based decisions
  • ❌ Fear of "wasting time" on research

🌳 Opportunity Solution Tree Checklist

Before Creating:

  • Clear outcome defined
  • Conducted 6+ customer interviews
  • Identified multiple opportunities

When Building Tree:

  • Start with ONE outcome (top)
  • Map opportunities (not solutions)
  • Generate multiple solutions per opportunity
  • List assumptions for each solution
  • Plan tests for assumptions

Using the Tree:

  • Update weekly with new learning
  • Share with stakeholders
  • Use to explain why you're building what
  • Reference when prioritizing work

🧪 Assumption Testing Hierarchy

Test in This Order:

  1. Desirability - Do customers want this?
  2. Usability - Can they use it?
  3. Feasibility - Can we build it?
  4. Viability - Should we build it?

Use Cheapest Test First:

Interview < Survey < Prototype < Concierge < Build

Real-World Examples

Example: Spotify's Discovery Process

Outcome: Increase music discovery engagement

Opportunity: Users don't know what to listen to

  • Evidence: Interviews showed decision fatigue
  • Solution considered: Algorithmic playlists
  • Assumption: Users trust algorithmic recommendations
  • Test: Created Discover Weekly, measured engagement
  • Result: Massive success, became core feature

Key Learning: They tested the algorithm assumption before building fancy UX


Example: Netflix's Continue Watching

Outcome: Reduce time to content consumption

Opportunity: Users forget what they were watching

  • Evidence: Drop-off analysis + customer interviews
  • Solution: "Continue Watching" row
  • Assumption: Users want to resume (not restart)
  • Test: A/B test with 5% of users
  • Result: Validated, rolled to 100%

Key Learning: Small test before full build saved months of work


Common Pitfalls

❌ Discovery Theater

Problem: Doing research but not changing decisions Solution: Explicitly decide what you'll do if assumptions are wrong

❌ Outsourcing Discovery

Problem: PM does research, then "throws it over the wall" Solution: Product trio interviews together

❌ Building Multiple Solutions at Once

Problem: Spreading resources too thin Solution: Test assumptions first, build one at a time

❌ Skipping Discovery "To Move Fast"

Problem: Building wrong thing is slowest path Solution: Small tests are faster than big rebuilds

❌ Only Talking to Happy Customers

Problem: Missing problems and churn reasons Solution: Interview across the spectrum (new, power, churned users)


Key Quotes

Teresa Torres on Weekly Contact:

"If you're not talking to customers every week, you're not doing continuous discovery."

On Product Trios:

"The best product decisions come from diverse perspectives. A PM, designer, and engineer will see different things in the same customer interview."

On Opportunity Solution Trees:

"The tree makes your thinking visible. It shows how you got from an outcome to a solution, which builds stakeholder trust."

On Assumption Testing:

"Don't ask customers what to build. Test assumptions about what will work."

On Discovery vs Delivery:

"Discovery and delivery should happen continuously. Discovery doesn't end when you start building."


Related Skills

Use together with:

  • user-feedback-system - For ongoing feedback collection
  • jtbd-building - For understanding customer motivations
  • exp-driven-dev - For testing assumptions with data
  • metrics-frameworks - For defining outcomes
  • strategic-build - For deciding what's worth discovering

Comes before:

  • zero-to-launch - Discover before building
  • design-first-dev - Design based on discovery

Comes after:

  • strategy-frameworks - Define strategy, then discover how

Quick Start Guide

Week 1: Set Up Discovery Process

  1. Form product trio (PM, designer, engineer)
  2. Define one clear outcome to pursue
  3. Schedule first 3 customer interviews
  4. Create interview snapshot template

Week 2: Start Discovery Habit

  1. Conduct 3 interviews together
  2. Create interview snapshots
  3. Begin opportunity solution tree
  4. Identify opportunities from interviews

Week 3: Map Solutions

  1. Generate 3+ solutions per opportunity
  2. List assumptions for each solution
  3. Prioritize which assumptions to test
  4. Plan assumption tests

Week 4: Test Assumptions

  1. Run first assumption tests
  2. Update opportunity solution tree
  3. Decide: build, test more, or pivot
  4. Make discovery routine sustainable

Remember: Continuous discovery isn't a phase. It's a habit. The product trio that talks to customers weekly makes better product decisions.


Guest: Teresa Torres
Book: Continuous Discovery Habits (2021)
Website: producttalk.org
Known for: Opportunity Solution Trees, Product Trios, Weekly Touchpoints

Frequently asked questions

What does the Continuous Discovery AI skill do?

Implements Teresa Torres' continuous discovery habits for weekly customer contact, opportunity solution trees, and assumption testing. Use when building discovery processes, conducting user research, validating assumptions, or establishing product trio workflows.

Why use Continuous Discovery on TypingMind?

Because you install it once and use it with any model. Continuous Discovery is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Continuous Discovery in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/menkesu/awesome-pm-skills/tree/main/continuous-discovery. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Continuous Discovery?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Continuous Discovery?

As many as you like. As long as a model supports skills, you can use Continuous Discovery with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Continuous Discovery AI skill free?

It is published on GitHub by menkesu. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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