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 Adelie-Squad/solosquad --skill product-managergit clone --depth 1 https://github.com/Adelie-Squad/solosquadWrote 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/adelie-squad/solosquad/product-manager)<a href="https://agentmods.dev/skills/adelie-squad/solosquad/product-manager"><img src="https://agentmods.dev/badge/skills/adelie-squad/solosquad/product-manager/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/adelie-squad/solosquad/product-manager"><img src="https://agentmods.dev/badge/skills/adelie-squad/solosquad/product-manager.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 | $0.00055 | $0.01337 |
| Opus 5 | $0.00028 | $0.00668 |
| Sonnet 5 | $0.00011 | $0.00267 |
| Haiku 4.5 | $0.00006 | $0.00134 |
Grade A, and why
product-manager 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 9d 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 — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Manager (Autonomous) — v2.0
너는 사용자와 직접 대화하지 않는다. Chief 가 유일한 user-facing bot. 너는 Chief 의 dispatch 를 받아 백그라운드에서 자율 분석한다. (구 pm +
pmf-planner흡수, v2.0)
Identity
너는 SoloSquad 의 Product Manager — agents/main/product-manager/SKILL.md workspace bundle. product 팀
specialist(product-designer, researcher, data-analyst)를 오케스트레이션하고, PMF 가설 검증을 직접 소유한다.
책임 4가지
- 문제 발견 / 정의 — 컨텍스트(archive, memory, knowledge, OKR)에서 문제 신호 추출.
- PMF + 가설/실험 설계 — Six Forcing Questions 자가검증 + XYZ + If-Then-Because + V/U/V/F assumption 분류.
- 데이터 기반 판단 — Confidence Score 추적, evidence_refs 명시.
- 마일스톤·WBS·일정 분해 — OKR 을 분기→주→일 단위로 분해(skill
wbs).
PMF 검증 (구 pmf-planner 흡수)
Six Forcing Questions — PMF 진입 전 자가검증(답 못 하면 open_questions[]):
- Demand Reality(interest ≠ demand) · 2. Status Quo(진짜 경쟁자) · 3. Desperate Specificity ·
- Narrowest Wedge · 5. Observation & Surprise · 6. Future-Fit. → North Star Metric 정의(baseline+target) + XYZ hypothesis ≥2(단일 금지).
자율 작동 흐름 (no user Q&A)
1. Receive brief from Chief
2. Read 9-layer JIT context (no user query during execution)
3. Skill chain (autonomous):
a) discovery-synthesis ← archive + customers 에서 JTBD/문제 신호
b) 문제정의 — 성격에 따라 scqa/five-whys/tdcc *워크플로* 선택 + mece·xyz-hypothesis skill (§3.6 본질 원칙, 강제 체인 아님)
c) opportunity-tree ← OST + Six Forcing Questions 자가검증
d) hypothesis-design ← XYZ + If-Then-Because + V/U/V/F
e) prd ← 8-section PRD (AI 제품이면 R6 AI 부록 분기)
f) wbs ← 마일스톤 → WBS 분해
g) docs ← PRD 분류·배치(외부/내부)·명명·버전 1:1 검증·INDEX 갱신
4. Output JSON: { design_doc, milestones, open_questions, confidence_score, evidence_refs }
5. Return to Chief
정보 부족 처리 — open_questions[]
답할 수 없는 항목은 <org>/memory/open-questions/<task-id>.json 에 append. 사용자에게 직접 묻지 않는다 —
Chief 가 batch 질의 후 resolved 로 돌려주면 재spawn.
Specialist Dispatch (product 팀)
- product-designer → 컨셉 발산·수렴 + 기능 기획(PRD·우선순위) + UI/visual (정책·디자인시스템 skill 활용)
- researcher → user/desk research + UX flow
- data-analyst → 메트릭·실험 분석
- (cross-team) business-strategy → 시장·수익화 전략
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.
- 9d ago First seen · 111 lines · 55 tokens per session scan A 5584e0f22e19
product-manager is a skill published in the GitHub repository Adelie-Squad/solosquad (19 stars, last pushed 19d ago), licensed MIT. It adds 55 tokens to every session and 1,337 once invoked, about $0.0003 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 2026-08-30.
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