agentmods.dev

product-discovery-sprint

product-discovery-sprint is a skill for Claude Code, Codex from uthumany/uthy-legacy-os. It costs 40 tokens per session (1,304 once invoked), scanned A, original, MIT.

A structured, time-limited process for learning about an unfamiliar problem before choosing a solution. It runs over about one to two weeks and produces a validated problem statement, an opportunity-solution map, and prioritized experiments.

In plain words
What is it for?
Use it to explore a new problem area, check whether a proposed initiative is worth building, and decide which solution ideas to investigate first.
Why use it?
It reduces the risk of building something before the problem is understood. It also gives teams a shared view when people disagree about the real problem.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

not rated 5repo 5mo ago A scan 40 tokens original MIT

Good fit Use it to explore a new problem area, check whether a proposed initiative is worth building, and decide which solution ideas to investigate first.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/uthumany/uthy-legacy-os/product-discovery-sprint
View source ↗ uthumany/uthy-legacy-os uthumany.github.io
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add uthumany/uthy-legacy-os --skill product-discovery-sprint
Clone the repo
git clone --depth 1 https://github.com/uthumany/uthy-legacy-os

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for product-discovery-sprint

README.md
[![agentmods](https://agentmods.dev/badge/skills/uthumany/uthy-legacy-os/product-discovery-sprint/github.svg)](https://agentmods.dev/skills/uthumany/uthy-legacy-os/product-discovery-sprint)
Your own site
<a href="https://agentmods.dev/skills/uthumany/uthy-legacy-os/product-discovery-sprint"><img src="https://agentmods.dev/badge/skills/uthumany/uthy-legacy-os/product-discovery-sprint/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for product-discovery-sprint

Your own site · 80×15
<a href="https://agentmods.dev/skills/uthumany/uthy-legacy-os/product-discovery-sprint"><img src="https://agentmods.dev/badge/skills/uthumany/uthy-legacy-os/product-discovery-sprint.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 40 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,304 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00040 $0.01304
Opus 5.5 $0.00016 $0.00522
Sonnet 5.5 $0.00008 $0.00261
Haiku 4.5 $0.00004 $0.00130

Measured 2mo ago against content hash b9bec92e7ae0, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

product-discovery-sprint scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

workflows/product-discovery-sprint/SKILL.md · 123 lines

How it starts

The opening of the file, as written. The whole thing — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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?

Read the full file on GitHub · 123 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2mo ago First seen · 123 lines · 40 tokens per session scan A b9bec92e7ae0

Subscribe to this mod's changes

product-discovery-sprint is a skill published in the GitHub repository uthumany/uthy-legacy-os (5 stars, last pushed 5mo ago), licensed MIT. It adds 40 tokens to every session and 1,304 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

PlanningResearch
Related

product-discovery-sprint compared with skills from other repositories

product-discovery-sprint next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
product-discovery-sprintuthumany/uthy-legacy-osSkill55mo agoMITA40Claude Code, Codex
imagegenopenai/codexSkill134ktodayApache-2.0A113Codex
local-ai-agentsmicrosoft/ai-agents-for-beginnersSkill79ktodayMITA200Claude Code, Codex
playwright-climicrosoft/playwrightSkill99ktodayApache-2.0A19Claude Code
systematic-debuggingobra/superpowersSkill314k3d agoMITA21Claude Code
next-cache-components-adoptionvercel/next.jsSkill143ktodayMITA95Claude Code
chat-pet-sprite-creationmicrosoft/vscodeSkill195ktodayMITA53Claude Code, Codex