Universal Product Manager
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About this skill
Problem
Product work often gets stuck in two places. Vague requests turn directly into PRDs, user stories, or roadmaps, which can become template filling without decisions. Cross-domain work across discovery, positioning, delivery, metrics, and AI products needs a shared standard, otherwise assumptions stay unlabeled, outcomes stay unmeasurable, and roles stay generic.
How It Works
The skill routes the request to the matching knowledge area: discovery and research, strategy and positioning, artifacts and delivery, finance and metrics, career and leadership, or AI product craft. It then loads the relevant knowledge/ sections and templates/ files, fills them with concrete context, and applies quality gates: label [assumption], define measurable outcomes, specify roles and tradeoffs, and flag antipatterns such as metrics theater, feature factory, and solution smuggling.
Boundaries
Use it when you need PM-style probing, first drafts, decomposition, and delivery structure. It depends on the provided knowledge modules and templates. If the request is outside PM scope, redirect instead of forcing a framework. Complex requests can use guided, info-dump, or best-guess modes; simple requests can be answered directly.
Use Cases
- Before kickoff, turn scattered customer interview notes into a problem statement, an opportunity-solution tree, and testable experiment hypotheses while flagging assumptions and success metrics.
- Prepare a PRD review by drafting user stories, acceptance criteria, epic hypotheses, and a list of assumptions that still need validation.
- Run a SaaS metrics review using MRR, NRR, CAC, and LTV to decide feature ROI and channel investment priority for the next quarter.
- Design an AI feature validation plan using PoL probing, context engineering, and agent orchestration to define test goals and failure risks.
Best For
- Engineers who need to turn vague requirements into PRDs, user stories, and acceptance criteria before review, with tradeoffs and assumptions labeled.
- Product owners who must explain MRR, NRR, unit economics, and channel ROI before leadership reviews and decide investment priority for the next quarter.
- Algorithm or platform engineers who want to move AI features from demos to validated workflows with clear test goals and failure modes.
- Product managers preparing to move into director or VP roles and need help framing altitude, scope, and readiness for transition interviews.
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