Product ai fit
Skill jpoindexter/product-management-skills/skills/product-ai-fit
Operational product-management skills and a /pm router for Codex and Claude.
npx -y skills add jpoindexter/product-management-skills --skill product-ai-fitAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- 14 days oldThe repository was created 14 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
Decide whether a product problem should use AI and define the appropriate human-AI workflow. Use when a team starts with a model or agent idea, must compare AI with deterministic software or process change, needs an AI use-case brief, or must assess data, uncertainty, value, feasibility, cost, and fallback fit.
SKILL.md
2.7 KB, as published. Nobody here has run it
Product AI Fit
Start with a user decision or workflow that benefits from interpretation, generation, prediction, ranking, or adaptive behavior. Recommend AI only when it creates enough value to justify uncertainty and operating cost.
Inputs
- User, job, workflow, and current alternative
- Required output or decision
- Tolerance for error, latency, variability, and explanation
- Available data, feedback, and permissions
- Expected frequency, value, and cost
- Human expertise and escalation capacity
Workflow
- Define the user outcome and the part of the workflow that is currently constrained.
- Compare four approaches: no change, process or policy change, deterministic software, and AI-assisted or AI-automated behavior.
- Identify the AI capability required: classify, predict, retrieve, rank, generate, perceive, or plan.
- Determine whether examples, labels, context, feedback, and evaluation data exist or can be obtained responsibly.
- Analyze the cost and consequence of wrong, delayed, inconsistent, or manipulated outputs.
- Choose the human role: author, reviewer, approver, exception handler, or recipient.
- Define confidence handling, fallback, explanation, correction, and user control.
- Estimate value per successful task, cost per task, latency budget, and operational burden.
- Recommend AI, deterministic software, hybrid workflow, or no build, with the smallest validating prototype.
Output contract
Return workflow and problem, alternatives, AI capability, value mechanism, data requirements, error-cost analysis, human role, fallback, economics, recommendation, prototype, and decision rule.
Quality gate
- The problem exists without mentioning AI.
- AI provides a specific advantage over deterministic alternatives.
- Uncertainty is visible in the user experience and operating model.
- Evaluation and feedback data are plausible.
- Cost, latency, privacy, and failure recovery are included.
Avoid
- Choosing a model before defining the task
- Using AI where rules are adequate and safer
- Assuming natural interaction means reliable behavior
- Automating high-impact decisions without meaningful oversight
- Ignoring the cost of review and exceptions
Source grounding
Operational synthesis informed by AI use-case, workflow, uncertainty, and lifecycle concepts in Building AI-Powered Products and production trade-offs in Designing Machine Learning Systems.