rweisssieker-xp

Use when Power BI tenant administration needs Admin API playbooks for workspace inventory, usage metrics, refresh failures, endorsements, sensitivity labels, sharing risk, gateways, and capacity.

Coordinate virtual Power BI expert roles such as BI Architect, DAX Expert, Power Query Engineer, Fabric Architect, Finance BI Lead, QA Reviewer, Security Reviewer, and CoE Lead. Use to replace manual expert routing with role-based analysis and deliverables.

Use when DAX measures require executable or semi-executable unit tests, query-based assertions, XMLA or Tabular Editor validation, edge cases, totals, RLS contexts, and reconciliation evidence.

Use when Power BI work needs PBIP, Git, deployment pipelines, semantic model versioning, Tabular Editor, DAX checks, regression tests, release gates, workspace promotion, or CI/CD governance.

Use when the user asks for Expert Mode, senior Power BI engineering, deep model/report/DAX/M review, precise control, or developer-facing Power BI delivery with explicit technical evidence.

Use when the user asks for Generalisten Mode, Prozess Manager mode, Process Owner mode, autonomous BI delivery, or wants the plugin to handle Power BI work end-to-end without requiring expert knowledge.

Classify Power BI requests and route them to the right delivery path. Use when a request may involve data sources, semantic models, DAX, UX, governance, performance, migration, Fabric architecture, portfolio cleanup, or business problem design.

Use when Power BI or Fabric work needs live metadata discovery from tenants, semantic models, SQL, lakehouse, SAP, Salesforce, Dynamics, Snowflake, APIs, or files before design decisions.

Govern AI use cases as a portfolio with business owner, data owner, model risk tier, evaluation baseline, human review and rollback path.

Map which CIO actions can be advised, drafted, prepared for approval or must remain human-only.

Protect architecture runway by detecting capacity, integration, platform, technical-debt and exception risks before approval.

Predict likely audit findings from missing evidence, weak controls, unclear ownership, exceptions and compliance signals.

Prepare action plans, task drafts, escalations, approval requests, notifications, workflow steps and decision logs from The Autonomous CIO findings. Does not execute external actions.

Run the flagship governed CIO autopilot workflow. Use for broad CIO reviews, Board in 48h scenarios, "run the CIO", mixed enterprise context, monthly operating reviews, crisis-to-control reviews and executive decision-readiness work.

Route broad executive, CIO, CISO, COO, Board, transformation, crisis, risk and governance requests to the right The Autonomous CIO skill chain. Use when the user asks a broad question, says they do not know which skill to use, or requests an end-to-end executive workflow.

Generate targeted due-diligence questions for board, vendor, crisis, audit, transformation, AI and carve-out reviews.

Use when migration intelligence fabric work needs benchmarking analyst, reusable knowledge, benchmarking, portfolio control, scenario simulation, quality audit, resilience, value realization, or continuous improvement.

Use when migration intelligence fabric work needs continuous improvement lead, reusable knowledge, benchmarking, portfolio control, scenario simulation, quality audit, resilience, value realization, or continuous improvement.

Use when migration intelligence fabric work needs delivery quality auditor, reusable knowledge, benchmarking, portfolio control, scenario simulation, quality audit, resilience, value realization, or continuous improvement.

Use when migration intelligence fabric work needs fabric data product advisor, reusable knowledge, benchmarking, portfolio control, scenario simulation, quality audit, resilience, value realization, or continuous improvement.

Use when migration intelligence fabric work needs integration resilience engineer, reusable knowledge, benchmarking, portfolio control, scenario simulation, quality audit, resilience, value realization, or continuous improvement.

Use when migration intelligence fabric work needs intelligence fabric orchestrator, reusable knowledge, benchmarking, portfolio control, scenario simulation, quality audit, resilience, value realization, or continuous improvement.

Use when migration intelligence fabric work needs knowledge transfer examiner, reusable knowledge, benchmarking, portfolio control, scenario simulation, quality audit, resilience, value realization, or continuous improvement.

Use when migration intelligence fabric work needs migration memory, reusable knowledge, benchmarking, portfolio control, scenario simulation, quality audit, resilience, value realization, or continuous improvement.

Analyze slow AX frontends, problematic users/clients, broad inventory queries, and machine-wide impact without installing client parsers.

Build or run Autonomous Ops workflows for AX Performance Advisor, including investigation queues, evidence acquisition plans, follow-up questions, change drafts, validation planning, and readiness gates.

Generate AI learning and decision-quality artifacts such as recommendation memory, similarity search, acceptance simulation, narrative variants, anomaly explanations, and action confidence tuning.

Generate autonomous AI/USP investigation artifacts including evidence scout, investigation tree, root-cause debate, recommendation quality gate, KPI storyboard, and anonymized pattern library.

Generate guarded admin execution previews with policy gates, confirmation tokens, audit records, and rollback/validation metadata.

Generate advanced operational USP outputs including SLO burn rate, maintenance sequencing, cost of delay, release gates, retention candidates, known issue matching, and executive briefings.

Prioritize AXPA findings using cost-of-delay risk points and deferred-risk reasoning.

Generate AXPA enterprise observability outputs such as time-series store, alerts, estate inventory, plan repository, notification payloads, and competitive coverage.

Designs dry-run experiments across query rewrites, indexes, workload twins, and rollout gates.

Produces a board-level briefing for closed-loop autonomous database operations.

Enforces closed-loop dry-run boundaries and blocks autonomous production apply.

Scores whether enough evidence exists to recommend, defer, or escalate an autonomous operator decision.

Compares do-nothing, dry-run-change, rollback, and defer scenarios for an operating objective.

Builds an executive-grade decision evidence packet from runtime signals and confidence checks.

Critiques proposed dry-run actions for blast radius, rollback gaps, tenant risk, and weak evidence.

Chooses the safest next dry-run diagnostic or experiment from candidate actions.

TOPdesk Action Sequences and automation design. Use for action sequence triggers, webhooks, API calls, payload mapping, idempotency, retries, error handling, safe automation boundaries, audit logging, and automation monitoring.

TOPdesk administration and configuration design. Use for categories, statuses, priorities, impact/urgency, supporting files, branches, persons, operators, operator groups, permissions, SSP forms, service catalog items, change templates, asset templates, knowledge visibility, and configuration governance.

Build TOPdesk AI/KI adoption ledgers and monitoring summaries from suggestion logs, operator feedback, acceptance/edit/rejection events, override reasons, confidence, cost estimates, prompt/model versions, and value assumptions.

Generate complete governed AI/KI feature packs for TOPdesk. Use for turning an AI use case into feature scope, data contract, prompt design, JSON output schema, evaluation dataset, feedback loop, audit fields, rollout gates, Power BI monitoring measures, RLS/PII controls, and operator-facing acceptance criteria.

Design Power BI governance cockpits for TOPdesk AI/KI features. Use for AI monitoring datasets, semantic models, DAX measures, report pages, prompt/model version tracking, suggestion acceptance, override reasons, confidence drift, cost, token usage, SLA impact, PII/policy findings, feedback loops, evaluation results, RLS, and audit reporting.

Design, implement, and evaluate AI/KI features for TOPdesk workflows. Use for ticket classification, smart routing, summaries, response suggestions, knowledge article generation, semantic search, RAG, chatbot/self-service assistants, duplicate detection, SLA-risk prediction, prompt templates, JSON schemas, operator feedback loops, AI governance, and regression evaluation.

Use when testing live TOPdesk REST/API access, authentication, endpoint availability, paging behavior, response status codes, payload shapes, demo tenants, smoke tests, regression checks, and API readiness before building Power BI, Python, PowerShell, or AI features.

TOPdesk Asset Management design, schema, analytics, and workflow support. Use for asset templates, dynamic asset fields, asset relations, owner/branch/location mapping, lifecycle, incident/change links, CMDB-like reporting, asset data quality, and asset KPIs.

Use when the user wants to inspect or operate a real Microsoft Dataverse environment through the plugin MCP server with OAuth2 client credentials. Supports metadata, query, retrieve, create, update, delete, action execution, and simulation tools.

Use when applying the monetizable enterprise USP pack for Dataverse and Power Platform: compliance evidence automation, ROI and process value scoring, autonomous remediation playbooks, tenant-specific AI skill generation, governed cross-tenant rollout, business capability mapping, trust scoring for AI actions, autonomous exception management, enterprise change impact AI, and AI-controlled operating cadence.

Use when applying the advanced AI USP expansion for Dataverse and Power Platform: organizational cognitive graph, autonomous reasoning, self-optimization, process discovery, semantic search, predictive intelligence, decision simulation, digital twin, generated business processes, autonomous governance, enterprise memory, data stewardship, personalized UX, natural-language enterprise programming, runtime security, continuous learning, executive intelligence, cross-system cognition, autonomous agents, and intent-driven enterprise computing.

Use when designing extensible Dataverse action packs, AI skills, workflow agents, industry modules, or reusable Dataverse skills. Produces marketplace architecture guidance only.

Use when planning dynamic task-specific forms, dashboards, tables, wizards, approval dialogs, or input masks generated for Dataverse and Power Apps scenarios. Produces UI plans only and generates no live UI.

Use when designing organizational memory for Dataverse AI actions, including user behavior, processes, preferences, common actions, approval patterns, and company language. Produces memory design guidance only.

Use when designing Dataverse AI behavior that adapts to user role, department, permissions, language, preferences, data context, relationships, business logic, policies, and organizational rules. Produces design guidance only and performs no live tenant inspection.

Use when designing a dialog-first ERP or CRM operating layer over Dataverse, Dynamics, or Power Platform apps. Produces architecture and interaction guidance only; it does not replace or control live applications.

Build Microsoft Fabric change assurance reports, scores, approval packets, and board-ready evidence before high-impact changes.

Prepare Microsoft Fabric compliance evidence bundles, audit anomaly reviews, policy exception evaluations, and release risk matrices.

Fingerprint Microsoft Fabric environments and explain drift between dev, test, and production.

Create executive Microsoft Fabric release control evidence with workspace criticality, freeze windows, access recertification, release briefs, and operator action queues.

Run evidence-backed Microsoft Fabric release gate reviews across approval, blast radius, sensitivity, capacity risk, drift, and assurance.

Build Microsoft Fabric incident timelines, rank root-cause candidates, and prepare executive/operator briefs.

Use Microsoft Fabric Control to inspect workspaces, preflight deployments, run Fabric jobs, diagnose failures, and enforce production guardrails.

Simulate, compare, and tighten Microsoft Fabric guardrail policies without writing policy files.

Use when working with Microsoft Power BI Desktop files or projects, including PBIX/PBIT inventory, DAX and Power Query review, semantic model documentation, Tabular Editor workflows, and local Desktop environment checks.

Use when storing and reusing Power BI planning memory across cycles, including forecast versions, assumptions, actions, overrides, actuals, error causes, segment lessons, and durable learning signals for autonomous planning.

Use when coordinating multiple AI forecasting perspectives for Power BI sales forecasts, including backlog, seasonality, budget, sales skepticism, risk, consensus, dissent, and explainable forecast arbitration.

Use when turning autonomous Power BI planning deviations into managed exceptions, including revenue gaps, risky backlog, unrealistic targets, biased models, sparse segments, owner hints, status, escalation, and closure evidence.

Use when building or running a closed-loop autonomous Power BI planning cycle that refreshes actuals, forecast, gap detection, scenarios, actions, tracking, learning, and the next plan without changing PBIX files directly.

Use when adding causal and counterfactual thinking to Power BI sales forecasts, including working days, holidays, delivery constraints, price changes, product lifecycle, stockouts, campaigns, customer behavior, and best/base/worst case simulations.

Use when constraining Power BI revenue planning by delivery, capacity, stock, supply, margin, cash, payment terms, sales resources, or operational feasibility rather than forecasting unconstrained demand only.

Use when designing or evaluating a Power BI forecast trust market, human override tracking, sales and finance confidence scoring, forecast accountability, and self-learning model-vs-human accuracy loops.

Use for CEAP AI USPs, AI impact twin, regulatory co-pilot, AI review board, cited engineering memory, risk files, autonomous fix evidence, process mining, policy-bound agents, predictive review routing, release decision intelligence, risk prediction, architecture drift, compliance gaps, test debt, migrations, incident learning, requirement contradictions, privacy data flows, DevEx friction, and contract guardianship.

Use for CEAP multi-agent workflows, agent role assignment, release management, incident response, observability, autonomous engineering, VS Code or Teams agent workflows, and human-in-the-loop operations.

Use for CEAP ticket-to-code, implementation planning, code impact analysis, repository analysis, refactoring, PR preparation, release notes, and architecture justification workflows.

Use for CEAP enterprise context aggregation, engineering memory, knowledge graphs, cross-system analysis, integrations, D365 or ERP context, documentation intelligence, and workflow observation.

Use for CEAP quality engineering, automated test generation, security checks, compliance validation, policy gates, auditability, and regulated enterprise or MedTech engineering review.

Use when creating PMO management reports, PMO report suites, filtered project portfolio reports, or DOCX/XLSX PMO outputs from real Dynamics 365 TPG project data.

Use when drafting confirmation-gated Dynamics 365 TPG project status reports for projects led by the current user.

Run the TPG-Scheduler-Codex-Plugin PMO report suite from the repository root plugin entry.

Run the TPG-Scheduler-Codex-Plugin workflow for confirmation-gated Dynamics 365 TPG project status reporting from the repository root plugin entry.

Use Azure Boards through Microsoft Entra OAuth, PAT, or bearer-token auth for Work Item operations, AI Project Cockpit reporting, history/flow mining, comment intelligence, role-based delivery reports, process policy checks, and safe write-preview workflows.