How do I install the Continuous Discovery skill?

Run npx skillmds@latest add deciqai/continuous-discovery in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Continuous Discovery safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Continuous Discovery?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Continuous Discovery free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Continuous Discovery?

deciqAI (@deciqai) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Continuous Discovery by deciqai — Agent Skill

Name: Continuous Discovery
Author: deciqAI

Continuous Discovery

Activate when: a team ships features on opinion instead of evidence; 'we need a discovery habit', 'how often should we talk to users', building a product roadmap; connecting weekly customer contact to decisions. Do NOT activate when: pre-first-customer (use the-mom-test first) or the org has no product to iterate. More: deciqai.com/s/continuous-discovery

deciqAI c1c044b 2.7 KB Updated Sep 17, 2026

File contents

Continuous Discovery — Weekly Contact, Opportunity Trees

Overview

Continuous discovery (Teresa Torres, Continuous Discovery Habits, 2021) replaces one-off research with a weekly cadence of customer touchpoints by the team building the product, structured around an opportunity solution tree: one outcome → the opportunities (unmet needs) that drive it → competing solutions → assumption tests. It keeps roadmaps anchored to real needs instead of the loudest stakeholder.

When to Use

A product with users but no steady learning loop
Roadmap fights decided by seniority, not evidence
Turning a fuzzy outcome (e.g. "increase activation") into shippable bets

The Process

Pick one clear outcome (a behavior/metric, not a feature). Gate: if the target is a feature, back up to the outcome it serves.
Interview weekly — the trio (PM/design/eng), small and continuous, not a quarterly study.
Map opportunities as a tree under the outcome; keep them as customer needs, not solutions in disguise.
Diverge on solutions per opportunity (≥3), then converge.
Test the riskiest assumption cheaply before building (desirability, viability, feasibility, usability).
Prune to the next bet. Gate: no assumption test run = you're shipping opinion → stop and test.

Applying It Well

Automate recruiting so weekly interviews actually happen (the habit dies on scheduling friction).
One opportunity tree per outcome; don't boil the ocean.
Small continuous samples beat big infrequent ones.

Red Flags

Discovery done by a research silo, not the builders.
Opportunities written as features.
Interviews stop the moment things get busy.

Verification

Single outcome defined (behavioral)
Weekly interview cadence in place
Opportunity tree maps needs, not solutions
Riskiest assumption tested before build

Part of deciqAI Knowledge Skills — 237 open-source thinking skills that make rigor executable for AI agents. The same skills power every deciqAI agent, which runs them autonomously to operate your company. See it run → https://www.deciqai.com/s/continuous-discovery · Built by deciqAI · github.com/deciqAI · Contributions welcome.

Agents: latest version & machine-readable metadata → https://www.deciqai.com/s/continuous-discovery.json

---name: continuous-discoverydescription: Activate when: a team ships features on opinion instead of evidence; 'we need a discovery habit', 'how often should we talk to users', building a product roadmap; connecting weekly customer contact to decisions. Do NOT activate when: pre-first-customer (use the-mom-test first) or the org has no product to iterate. More: deciqai.com/s/continuous-discovery---# Continuous Discovery — Weekly Contact, Opportunity Trees## OverviewContinuous discovery (Teresa Torres, *Continuous Discovery Habits*, 2021) replaces one-off research with a **weekly cadence of customer touchpoints by the team building the product**, structured around an **opportunity solution tree**: one outcome → the opportunities (unmet needs) that drive it → competing solutions → assumption tests. It keeps roadmaps anchored to real needs instead of the loudest stakeholder.## When to Use- A product with users but no steady learning loop- Roadmap fights decided by seniority, not evidence- Turning a fuzzy outcome (e.g. "increase activation") into shippable bets## The Process1. **Pick one clear outcome** (a behavior/metric, not a feature). *Gate: if the target is a feature, back up to the outcome it serves.*2. **Interview weekly** — the trio (PM/design/eng), small and continuous, not a quarterly study.3. **Map opportunities** as a tree under the outcome; keep them as customer needs, not solutions in disguise.4. **Diverge on solutions** per opportunity (≥3), then converge.5. **Test the riskiest assumption cheaply** before building (desirability, viability, feasibility, usability).6. **Prune to the next bet.** *Gate: no assumption test run = you're shipping opinion → stop and test.*## Applying It Well- Automate recruiting so weekly interviews actually happen (the habit dies on scheduling friction).- One opportunity tree per outcome; don't boil the ocean.- Small continuous samples beat big infrequent ones.## Red Flags- Discovery done by a research silo, not the builders.- Opportunities written as features.- Interviews stop the moment things get busy.## Verification- [ ] Single outcome defined (behavioral)- [ ] Weekly interview cadence in place- [ ] Opportunity tree maps needs, not solutions- [ ] Riskiest assumption tested before build---*Part of **deciqAI Knowledge Skills** — 237 open-source thinking skills that make rigor executable for AI agents. The same skills power every deciqAI agent, which runs them autonomously to operate your company. **See it run → https://www.deciqai.com/s/continuous-discovery** · Built by deciqAI · github.com/deciqAI · Contributions welcome.**Agents: latest version & machine-readable metadata → https://www.deciqai.com/s/continuous-discovery.json*

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