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.
npx skills add Insajin/autopus-adk --skill product-discoverygit clone --depth 1 https://github.com/Insajin/autopus-adkWrote 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.
[](https://agentmods.dev/skills/insajin/autopus-adk/product-discovery)<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.
<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>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once 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 |
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.
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-analysis스킬 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.
| 축 | 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"
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.
- 7d ago First seen · 134 lines · 16 tokens per session scan A 03f891fc3877
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.
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