Product prioritization
Skill jpoindexter/product-management-skills/skills/product-prioritization
Operational product-management skills and a /pm router for Codex and Claude.
npx -y skills add jpoindexter/product-management-skills --skill product-prioritizationAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- 16 days oldThe repository was created 16 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
- 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Prioritize product bets under strategic, capacity, evidence, dependency, and risk constraints. Use when a backlog is overloaded, stakeholders compete for priority, a team asks for RICE or scoring, resources must be allocated, or explicit trade-offs and sequencing are required.
SKILL.md
2.5 KB, 428 tokens by cl100k_base, as published. Nobody here has run it
Product Prioritization
Apply strategy to a constrained portfolio of bets. Use scores as discussion aids, never as substitutes for judgment.
Inputs
- Strategy, desired outcomes, and non-goals
- Candidate bets expressed as outcome hypotheses
- Evidence, reach, impact mechanism, cost, and confidence
- Dependencies, deadlines, capacity, and risk
- Current portfolio commitments
Workflow
- Remove items that do not support the guiding policy or a required obligation.
- Normalize remaining candidates as bets: target, expected outcome, mechanism, evidence, cost, and risk.
- Separate obligations, experiments, enablers, and growth bets before comparing unlike work.
- Identify hard constraints and dependencies.
- Compare candidates on strategic fit, evidence, impact, urgency, reversibility, option value, effort, and portfolio balance.
- Use a scoring model only when definitions and scales are shared; run sensitivity analysis on uncertain inputs.
- Select a portfolio within actual capacity and reserve room for learning and reliability.
- Record deprioritized items with the reason and trigger for reconsideration.
- Define the next evidence or milestone required for each selected bet.
Output contract
Return decision criteria, normalized candidates, constraint map, comparison, selected portfolio, sequencing, explicit no/not-now list, sensitivity notes, owners, and review triggers.
Quality gate
- Strategy filters candidates before scoring.
- Capacity is allocated, not assumed infinite.
- Uncertainty and confidence are explicit.
- Mandatory work is not disguised as high-scoring discretionary work.
- The result contains meaningful exclusions.
Avoid
- Ranking features without a shared outcome
- Mixing obligations and optional bets in one score
- False precision in reach, impact, or effort
- Letting the loudest stakeholder set hidden weights
- Prioritizing the backlog while avoiding portfolio trade-offs
Source grounding
Operational synthesis informed by Rumelt's focus and coherent-action principles in Good Strategy/Bad Strategy, Perri's outcome orientation in Escaping the Build Trap, and product-risk thinking in Inspired.
What ships with it: 1 file
227 B alongside SKILL.md
agents/
- openai.yaml227 B