# Product Discovery Sprint > End-to-end product discovery sprint over 1-2 weeks. Use when entering a new problem space, preparing for a major initiative, or before committing to a solution. - **Type:** Skill - **Install:** `agentstack add skill-uthumany-uthy-legacy-os-product-discovery-sprint` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [uthumany](https://agentstack.voostack.com/s/uthumany) - **Installs:** 0 - **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [uthumany](https://github.com/uthumany) - **Source:** https://github.com/uthumany/uthy-legacy-os/tree/main/workflows/product-discovery-sprint - **Website:** https://uthumany.github.io/uthy-legacy-os/ ## Install ```sh agentstack add skill-uthumany-uthy-legacy-os-product-discovery-sprint ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Product Discovery Sprint (1-2 Weeks) ## Overview A discovery sprint is a structured, time-boxed exploration of a problem space. Unlike a design sprint (which focuses on prototyping and testing a solution), this discovery sprint focuses on understanding the problem and identifying the best opportunity to pursue. Use this workflow when you're entering a new area and need to go from "we don't know much" to "we have a validated problem and a prioritized set of solutions to explore." ## When to Use - You're entering a new problem space and need structured exploration - Before committing to a build cycle, you want to validate the problem first - You have an outcome in mind but don't know what opportunities to pursue - Stakeholders disagree on what the "real problem" is - Don't use for: already-validated problems (just build it), compliance requirements, bug fixing - **Duration**: 5-10 days (can be compressed to 3-5 days) - **Team**: PM (leads), Designer (participates), Researcher (optional), 1-2 Engineers (partial participation) - **Output**: Opportunity-solution map, validated problem statement, prioritized experiment ideas ## Instructions Follow the steps below in order. Each phase has specific outputs that serve as handoff criteria to the next phase. Do not skip phases — discovery quality depends on the full cycle. ### Day 1: Frame & Plan **Goal**: Align on what we're exploring and how. 1. **Define the scope** — What outcome are we trying to achieve? What decisions will this sprint inform? 2. **Surface assumptions** — What do we currently believe? Rate confidence (🔴🟡🟢) 3. **Plan research** — Who will we talk to? What will we ask? (Load skill: customer-interviews) 4. **Review existing data** — Analytics, support tickets, previous research — what do we already know? **Output**: Research plan with 5-8 interview targets, interview guide, assumption map ### Day 2-3: Research & Discover **Goal**: Talk to real users and gather evidence. 1. **Conduct 5-8 customer interviews** — Use the customer-interviews skill. Focus on past behavior, not future intent 2. **Capture findings** — After each interview: top 3 takeaways, surprising insight, quote 3. **Update assumptions** — Which assumptions were validated? Invalidated? New questions raised? **Output**: Interview notes, raw findings ### Day 4: Synthesize **Goal**: Turn interview data into actionable understanding. 1. **Affinity mapping** — Cluster observations into themes 2. **Build opportunity map** — Use the opportunity-solution-tree skill 3. **Write problem statement** — Use the problem-statement skill 4. **Identify key opportunities** — 3-5 opportunities with strongest evidence **Output**: Opportunity map, problem statement, key opportunities ranked by evidence ### Day 5: Ideate & Prioritize **Goal**: Generate solution ideas for the top opportunities. 1. **Brainstorm solutions per opportunity** — Use solution-brainstorming skill. 3-5 solutions per opportunity 2. **Surface assumptions** — For each solution, what needs to be true for it to work? 3. **Prioritize** — Which solutions are most promising? Which assumptions are riskiest? (Use hypothesis-prioritization) 4. **Design experiments** — For the top 2-3 solutions, design the cheapest valid experiment (Use experiment-design skill) **Output**: Prioritized solution list with experiment designs ### Day 5 (PM): Debrief & Decide **Goal**: Present findings and decide next steps. 1. **Present the opportunity map** — What did we learn? What surprised us? 2. **Recommend next steps** — Which opportunity to pursue? Which solution to test first? 3. **Get stakeholder alignment** — Do they agree with the problem frame? The opportunity ranking? 4. **Plan next sprint** — Discovery sprint → Solution exploration → Build cycle **Output**: Decision on which opportunity/solution to pursue next ## Common Pitfalls 1. **Skipping user interviews** — Without real customer conversations, you're guessing. Talk to 5-8 people minimum 2. **Solutioning too early** — The sprint is for understanding the problem, not designing the solution 3. **Too many stakeholders** — Keep the core team small (3-5 people). Expand only for key decisions 4. **Analysis paralysis** — A discovery sprint produces a direction, not a guarantee. It's OK to be uncertain 5. **Not involving engineers** — Engineering input on feasibility and effort prevents wasted design work ## Verification Checklist - [ ] 5-8 customer interviews completed - [ ] Affinity mapping done with themes identified - [ ] Opportunity map created (3-5 opportunities) - [ ] Problem statement written with evidence - [ ] Top 2-3 solutions identified with assumptions - [ ] Experiments designed for risikiest assumptions - [ ] Stakeholders aligned on next steps Pass to next phase when: - [ ] Problem statement validated with customer evidence - [ ] Opportunity map created with 3-5 opportunities - [ ] Top opportunity identified with confidence rating - [ ] Stakeholders aligned on problem frame - [ ] Next steps planned (experiment, prototype, or skip to build) ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [uthumany](https://github.com/uthumany) - **Source:** [uthumany/uthy-legacy-os](https://github.com/uthumany/uthy-legacy-os) - **License:** MIT - **Homepage:** https://uthumany.github.io/uthy-legacy-os/ Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-uthumany-uthy-legacy-os-product-discovery-sprint - Seller: https://agentstack.voostack.com/s/uthumany - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.