product-discovery

product-discovery is a skill for Claude Code, Codex from Insajin/autopus-adk. It costs 16 tokens per session (879 once invoked), scanned A, original, MIT.

A product discovery workflow for understanding user problems and testing possible solutions. It uses an Opportunity-Solution Tree, a diagram that connects desired outcomes, user problems, and solution ideas.

In plain words
What is it for?
Use it to define outcomes, collect user opportunities, compare solution ideas, map assumptions, plan experiments, and prepare user interviews.
Why use it?
It helps teams avoid building on untested assumptions and focus research on problems that matter to users and the business.

Skill for Claude CodeCodex

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

Good fit Use it to define outcomes, collect user opportunities, compare solution ideas, map assumptions, plan experiments, and prepare user interviews.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/insajin/autopus-adk/product-discovery
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 Insajin/autopus-adk --skill product-discovery
Clone the repo
git clone --depth 1 https://github.com/Insajin/autopus-adk

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

README.md
[![agentmods](https://agentmods.dev/badge/skills/insajin/autopus-adk/product-discovery/github.svg)](https://agentmods.dev/skills/insajin/autopus-adk/product-discovery)
Your own site
<a href="https://agentmods.dev/skills/insajin/autopus-adk/product-discovery"><img src="https://agentmods.dev/badge/skills/insajin/autopus-adk/product-discovery/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

Your own site · 80×15
<a href="https://agentmods.dev/skills/insajin/autopus-adk/product-discovery"><img src="https://agentmods.dev/badge/skills/insajin/autopus-adk/product-discovery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 16 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 879 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00016 $0.00879
Opus 5 $0.00008 $0.00439
Sonnet 5 $0.00003 $0.00176
Haiku 4.5 $0.00002 $0.00088

Measured 7d ago against content hash 03f891fc3877, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

product-discovery 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 7d 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.

.omp/skills/product-discovery/SKILL.md · 134 lines

How it starts

The opening of the file, as written. The whole thing — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Product Discovery Skill

Opportunity-Solution Tree를 중심으로 사용자 문제를 탐색하고 솔루션 가설을 검증하는 디스커버리 워크플로우입니다.

핵심 흐름

1. Outcome 정의

  • Business Outcome: 어떤 비즈니스 지표를 움직이려는가?
  • Product Outcome: 사용자 행동이 어떻게 바뀌어야 하는가?
  • Constraint: 시간, 기술, 리소스 제약은 무엇인가?

Outcome은 반드시 측정 가능하고 기간이 명시되어야 합니다.

2. Opportunity 탐색

사용자의 unmet need와 friction을 구조화합니다.

추천 소스:

  • 사용자 피드백 / CS 티켓
  • 인터뷰 인사이트
  • 경쟁사 분석 (competitive-analysis 스킬 연계)
  • 퍼널 데이터와 사용 패턴
| ID | Opportunity | Source | Impact | Confidence |
|----|-------------|--------|--------|------------|
| O1 | 사용자가 X를 찾는 데 오래 걸린다 | 인터뷰 3건 | High | Medium |
| O2 | Y 기능의 실패율이 높다 | 지원 티켓 | Medium | High |

3. Solution Ideation

각 Opportunity마다 최소 3개의 솔루션 후보를 만듭니다.

  • PM 관점: 제품 전략과 우선순위
  • Designer 관점: UX 단순화
  • Engineer 관점: 구현 가능성과 비용

4. Assumption Mapping

각 솔루션의 핵심 가정을 4축으로 정리합니다.

검증 질문
Value 사용자가 정말 원하고 있는가?
Usability 이해하고 쓸 수 있는가?
Feasibility 현재 스택과 팀으로 구현 가능한가?
Viability 비즈니스적으로 지속 가능한가?

Impact × Uncertainty가 높은 가정을 먼저 검증합니다.

5. Experiment Design

가정별로 최소 비용 실험을 설계합니다.

실험 유형 적합한 가정 특징
Fake Door Value 관심도 빠른 검증
Concierge Value / Usability 사람 기반 수동 검증
Wizard of Oz Usability / Feasibility 내부 수동 처리 숨김
Prototype Usability UX 검증
MVP 전체 비용 크지만 검증력 높음
### Experiment: {name}
- Assumption: {검증 대상}
- Type: {실험 유형}
- Metric: {측정 지표}
- Success Criteria: {수치 기준}
- Duration: {기간}

6. Interview Script 생성

필요 시 인터뷰 스크립트를 다음 구조로 만듭니다.

  • Warm-up: 맥락과 최근 경험 파악
  • Context: 현재 해결 방식과 pain point 확인
  • Deep Dive: 핵심 가정 검증 질문
  • Wrap-up: 후속 인터뷰 / 추가 사례 확보

원칙:

  • 유도 질문 금지
  • 미래 의향보다 과거 행동 중심
  • 5 Whys로 근본 동기 파악

7. Discovery Summary 저장

결과는 BS 파일 또는 PRD/SPEC 연구 문서로 저장합니다.

# BS-{ID}: Discovery — {title}

## Outcome
{목표}

## Opportunities
{우선순위 포함 목록}

## Key Assumptions
| # | Assumption | Axis | Impact | Uncertainty | Action |

## Experiments
{실험 카드}

출력 형식

## Discovery Summary

### Outcome
{목표}

### Opportunities
{핵심 기회 3-5개}

### Key Assumptions
{우선 검증할 가정}

### Experiments
{실험 설계}

### Next Step
/auto plan --from-idea BS-{ID} "feature description"

Read the full file on GitHub · 134 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. 7d ago First seen · 134 lines · 16 tokens per session scan A 03f891fc3877

Subscribe to this mod's changes

product-discovery is a skill published in the GitHub repository Insajin/autopus-adk (111 stars, last pushed yesterday), licensed MIT. It adds 16 tokens to every session and 879 once invoked, about $0.0001 per session on Opus 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 2026-09-03.