aipom plugin

• /aipom:README

  No description in the file.

• /aipom:aipom-assess-organization

  No description in the file.

• /aipom:aipom-establish-technical-readiness-bar

  No description in the file.

• /aipom:aipom-evaluate-initiative

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• /aipom:aipom-redesign-workflow

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• /aipom:aipom-set-product-direction

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• aipom-accountability-charter

  Assign human decision rights, accountability, review, contribution, escalation, and evidence duties for a material AI product or recurring operating decision.

• aipom-adoption-impact-scorecard

  Measure whether AI operating practices change behavior, decisions, workflows, reuse, outcomes, burden, and risk rather than merely increasing activity.

• aipom-autonomy-boundary-designer

  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.

• aipom-behavior-contract-builder

  Define expected, acceptable, and prohibited AI behavior with representative cases, thresholds, escalation, and consequences. Use before evaluation, launch, or autonomy decisions.

• aipom-bet-charter

  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.

• aipom-capability-maturity-advisor

  Diagnose role capability, applied learning, workflow adoption, stewardship, and outcome evidence; recommend the next capability-building motion rather than more generic training.

• aipom-context-lifecycle-designer

  Design how AI context is created, retrieved, refreshed, versioned, reconciled, retained, expired, excluded, and retired for a recurring purpose.

• aipom-context-package-builder

  Assemble a bounded, reusable context package with purpose, authoritative sources, constraints, decisions, examples, exclusions, and refresh rules. Use for recurring AI-assisted work.

• aipom-context-readiness-advisor

  Diagnose missing, stale, conflicting, inaccessible, excessive, sensitive, or untrusted context and recommend the next source, package, data, or lifecycle intervention.

• aipom-data-readiness-audit

  Assess whether data is fit for a specific AI product decision across provenance, quality, access, representativeness, consent, privacy, freshness, and operations.

• aipom-decision-cycle-redesign

  Redesign a recurring product decision cycle around evidence, context, human judgment, AI assistance, authority, feedback, and measurable learning.

• aipom-economic-case-builder

  Build an evidence-aware economic case for an AI investment across value, full lifecycle cost, uncertainty, alternatives, risk, and decision thresholds.

• aipom-eval-scorecard-builder

  Define calibrated AI evaluation metrics, rubrics, judges, thresholds, sampling, uncertainty, ownership, and decision rules tied to behavior and consequences.

• aipom-evaluation-strategy-advisor

  Recommend the product, model, workflow, human, and production evaluations needed for an AI decision, based on behavior, consequences, evidence gaps, and lifecycle stage.

• aipom-golden-dataset-builder

  Build a governed, representative AI evaluation set with provenance, expected behavior, edge cases, affected groups, adjudication, versioning, and limits.

• aipom-initiative-readiness-review

  Review one AI initiative across value, economics, dependencies, workflow, context, evaluation, governance, controls, capability, and recovery before a material decision.

• aipom-investment-stage-gates

  Define evidence-based entry, continuation, pivot, scale, pause, and stop decisions for AI investments without turning gates into document approval theater.

• aipom-learning-system-designer

  Design a role-based applied learning system with real work, practice, coaching, peer feedback, progression, reinforcement, stewardship, and outcome evidence.

• aipom-operating-model-assessment

  Assess AI product operating-model maturity across seven categories using evidence, disagreement, and critical-gap logic. Use to identify consequential gaps and next interventions.

• aipom-operating-model-design-sprint

  Design and test a bounded AI product operating-model change across decisions, workflows, context, evidence, governance, capability, ownership, and adoption.

• aipom-operating-model-retrospective

  Review whether the AI product operating model improves decisions and outcomes, identify systemic friction and performative activity, and choose the next changes.

• aipom-operating-model-roadmap

  Convert evidence-based operating-model findings into an owned 30-, 90-, 180-, and 365-day sequence of interventions, learning milestones, dependencies, and decisions.

• aipom-opportunity-framing

  Frame an AI product opportunity around an evidenced condition, affected actors, consequences, alternatives, and the uncertainty that should be tested next.

• aipom-outcome-value-map

  Map how AI behavior may change user behavior, product outcomes, economic value, and risk while exposing causal assumptions and countermeasures.

• aipom-platform-dependency-audit

  Assess model, vendor, data, infrastructure, integration, talent, switching, and portability dependencies that can change an AI investment decision.

• aipom-portfolio-posture-advisor

  Diagnose AI portfolio imbalance, premature scaling, weak evidence, vendor exposure, and zombie pilots; recommend where to explore, validate, scale, pause, or stop.

• aipom-portfolio-quarterly-review

  Run a recurring AI portfolio review that reallocates capital and capacity using strategy, outcomes, economics, readiness, production evidence, dependencies, and learning.

• aipom-production-evidence-review

  Run a recurring review of AI behavior, workflow, human, outcome, control, incident, and affected-party evidence to continue, change, constrain, roll back, or retire.

• aipom-productive-motion-map

  Map a recurring product-team motion through decisions, actors, inputs, handoffs, delays, rework, and failure before assigning AI and human responsibilities.

• aipom-risk-control-incident-playbook

  Design preventive controls, detection, triage, containment, rollback, communication, investigation, remediation, learning, and reporting for AI incidents.

• aipom-strategy-narrative-builder

  Turn an evidence-based AI product thesis, portfolio choices, outcomes, boundaries, and learning into a clear narrative for aligned organizational action.

• aipom-strategy-thesis-advisor

  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.

• aipom-trust-assurance-pack-builder

  Assemble current, audience-appropriate evidence about an AI product's purpose, behavior, limits, evaluations, controls, ownership, incidents, and change history.

• aipom-use-case-triage

  Compare AI opportunities across outcome value, evidence, feasibility, responsibility, readiness, and reversibility to recommend explore, validate, defer, or reject.

• aipom-workflow-opportunity-advisor

  Identify which product-team decision or productive workflow should be redesigned with AI first, based on outcome value, friction, evidence, consequence, and readiness.

• aipom-workflow-playbook-builder

  Turn a tested human-AI workflow into an inspectable playbook with context, roles, decisions, examples, controls, measures, fallback, and improvement ownership.

• authoritative-source-map

  Define which sources are authoritative for a purpose, who owns them, who may use them, how conflicts resolve, and when trust expires.

• human-aipom-work-contract

  Define AI responsibilities, human judgment, review, decision authority, escalation, and learning in a recurring workflow. Use when human-AI collaboration is vague or unreliable.

• responsible-aipom-governance-advisor

  Diagnose the most consequential missing AI governance condition across ownership, authority, controls, escalation, oversight, and trust evidence; recommend the next motion.

• role-based-aipom-competency-map

  Define observable AI product competencies by role and proficiency level, tied to real decisions, evidence, practice, and progression rather than generic tool fluency.

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