Diagnose missing, stale, conflicting, inaccessible, excessive, sensitive, or untrusted context and recommend the next source, package, data, or lifecycle intervention.
لغة النص الأصلي: الإنجليزية
القائمة
جمع SkillsMP عدد ٤١ من skills من deanpeters/ai-product-operating-model-skills. افتح أي skill لمراجعة مصدره وتفاصيله.
عرض ٤٠ من أصل ٤١ skills مجمعة.
Diagnose missing, stale, conflicting, inaccessible, excessive, sensitive, or untrusted context and recommend the next source, package, data, or lifecycle intervention.
لغة النص الأصلي: الإنجليزية
Assess whether data is fit for a specific AI product decision across provenance, quality, access, representativeness, consent, privacy, freshness, and operations.
لغة النص الأصلي: الإنجليزية
Recommend the product, model, workflow, human, and production evaluations needed for an AI decision, based on behavior, consequences, evidence gaps, and lifecycle stage.
لغة النص الأصلي: الإنجليزية
Diagnose AI portfolio imbalance, premature scaling, weak evidence, vendor exposure, and zombie pilots; recommend where to explore, validate, scale, pause, or stop.
لغة النص الأصلي: الإنجليزية
Design preventive controls, detection, triage, containment, rollback, communication, investigation, remediation, learning, and reporting for AI incidents.
لغة النص الأصلي: الإنجليزية
Turn scattered AI ambition into an evidence-aware product strategy thesis with choices, boundaries, outcomes, assumptions, and next bets. Use when direction or non-goals are unclear.
لغة النص الأصلي: الإنجليزية
Assign human decision rights, accountability, review, contribution, escalation, and evidence duties for a material AI product or recurring operating decision.
لغة النص الأصلي: الإنجليزية
Measure whether AI operating practices change behavior, decisions, workflows, reuse, outcomes, burden, and risk rather than merely increasing activity.
لغة النص الأصلي: الإنجليزية
Define evidence-based boundaries for what an AI system may do independently, with human approval, or never. Use before launch, scaling, or increasing AI authority.
لغة النص الأصلي: الإنجليزية
Define expected, acceptable, and prohibited AI behavior with representative cases, thresholds, escalation, and consequences. Use before evaluation, launch, or autonomy decisions.
لغة النص الأصلي: الإنجليزية
Turn an AI idea into an owned investment hypothesis with outcomes, economics, constraints, evidence, and a next learning test. Use before funding or expanding an initiative.
لغة النص الأصلي: الإنجليزية
Diagnose role capability, applied learning, workflow adoption, stewardship, and outcome evidence; recommend the next capability-building motion rather than more generic training.
لغة النص الأصلي: الإنجليزية
Design how AI context is created, retrieved, refreshed, versioned, reconciled, retained, expired, excluded, and retired for a recurring purpose.
لغة النص الأصلي: الإنجليزية
Assemble a bounded, reusable context package with purpose, authoritative sources, constraints, decisions, examples, exclusions, and refresh rules. Use for recurring AI-assisted work.
لغة النص الأصلي: الإنجليزية
Redesign a recurring product decision cycle around evidence, context, human judgment, AI assistance, authority, feedback, and measurable learning.
لغة النص الأصلي: الإنجليزية
Build an evidence-aware economic case for an AI investment across value, full lifecycle cost, uncertainty, alternatives, risk, and decision thresholds.
لغة النص الأصلي: الإنجليزية
Define calibrated AI evaluation metrics, rubrics, judges, thresholds, sampling, uncertainty, ownership, and decision rules tied to behavior and consequences.
لغة النص الأصلي: الإنجليزية
Build a governed, representative AI evaluation set with provenance, expected behavior, edge cases, affected groups, adjudication, versioning, and limits.
لغة النص الأصلي: الإنجليزية
Review one AI initiative across value, economics, dependencies, workflow, context, evaluation, governance, controls, capability, and recovery before a material decision.
لغة النص الأصلي: الإنجليزية
Define evidence-based entry, continuation, pivot, scale, pause, and stop decisions for AI investments without turning gates into document approval theater.
لغة النص الأصلي: الإنجليزية
Design a role-based applied learning system with real work, practice, coaching, peer feedback, progression, reinforcement, stewardship, and outcome evidence.
لغة النص الأصلي: الإنجليزية
Assess AI product operating-model maturity across seven categories using evidence, disagreement, and critical-gap logic. Use to identify consequential gaps and next interventions.
لغة النص الأصلي: الإنجليزية
Design and test a bounded AI product operating-model change across decisions, workflows, context, evidence, governance, capability, ownership, and adoption.
لغة النص الأصلي: الإنجليزية
Review whether the AI product operating model improves decisions and outcomes, identify systemic friction and performative activity, and choose the next changes.
لغة النص الأصلي: الإنجليزية
Convert evidence-based operating-model findings into an owned 30-, 90-, 180-, and 365-day sequence of interventions, learning milestones, dependencies, and decisions.
لغة النص الأصلي: الإنجليزية
Frame an AI product opportunity around an evidenced condition, affected actors, consequences, alternatives, and the uncertainty that should be tested next.
لغة النص الأصلي: الإنجليزية
Map how AI behavior may change user behavior, product outcomes, economic value, and risk while exposing causal assumptions and countermeasures.
لغة النص الأصلي: الإنجليزية
Run a recurring AI portfolio review that reallocates capital and capacity using strategy, outcomes, economics, readiness, production evidence, dependencies, and learning.
لغة النص الأصلي: الإنجليزية
Run a recurring review of AI behavior, workflow, human, outcome, control, incident, and affected-party evidence to continue, change, constrain, roll back, or retire.
لغة النص الأصلي: الإنجليزية
Map a recurring product-team motion through decisions, actors, inputs, handoffs, delays, rework, and failure before assigning AI and human responsibilities.
لغة النص الأصلي: الإنجليزية
Assemble current, audience-appropriate evidence about an AI product's purpose, behavior, limits, evaluations, controls, ownership, incidents, and change history.
لغة النص الأصلي: الإنجليزية
Compare AI opportunities across outcome value, evidence, feasibility, responsibility, readiness, and reversibility to recommend explore, validate, defer, or reject.
لغة النص الأصلي: الإنجليزية
Identify which product-team decision or productive workflow should be redesigned with AI first, based on outcome value, friction, evidence, consequence, and readiness.
لغة النص الأصلي: الإنجليزية
Turn a tested human-AI workflow into an inspectable playbook with context, roles, decisions, examples, controls, measures, fallback, and improvement ownership.
لغة النص الأصلي: الإنجليزية
Define which sources are authoritative for a purpose, who owns them, who may use them, how conflicts resolve, and when trust expires.
لغة النص الأصلي: الإنجليزية
Define AI responsibilities, human judgment, review, decision authority, escalation, and learning in a recurring workflow. Use when human-AI collaboration is vague or unreliable.
لغة النص الأصلي: الإنجليزية
Diagnose the most consequential missing AI governance condition across ownership, authority, controls, escalation, oversight, and trust evidence; recommend the next motion.
لغة النص الأصلي: الإنجليزية
Define observable AI product competencies by role and proficiency level, tied to real decisions, evidence, practice, and progression rather than generic tool fluency.
لغة النص الأصلي: الإنجليزية
Convert a proven, improved workflow into a governed, reusable skill with context, decisions, examples, guardrails, evaluations, ownership, and maintenance rules.
لغة النص الأصلي: الإنجليزية
Turn an evidence-based AI product thesis, portfolio choices, outcomes, boundaries, and learning into a clear narrative for aligned organizational action.
لغة النص الأصلي: الإنجليزية