Dados e Análise

Diagnose and resolve performance issues in Microsoft Fabric Real-Time Intelligence including Eventhouse, KQL databases, Eventstream, and ingestion pipelines. Use when asked to troubleshoot slow KQL queries, high Eventhouse CPU or memory, ingestion latency or failures, Eventstream throughput problems, capacity throttling (HTTP 430), cache policy tuning, materialized view lag, or Always-On configura

Diagnose and resolve Microsoft Fabric Data Agent issues including tenant settings, data source configuration, query generation failures, cross-region capacity errors, XMLA endpoint setup, Power BI semantic model integration, lakehouse/warehouse/KQL connectivity, example query validation, publishing/sharing problems, and Azure AI Foundry integration. Use when asked to troubleshoot data agent, fix F

Activate when the user wants to audit a paper's empirical or technical claims against a linked code repository — checking whether experiments, datasets, models, metrics, and hyperparameters described in the paper actually exist and match the code. Produces a structured audit report classifying each claim as CONFIRMED, PARTIAL, MISSING, or MISMATCH, with file/line evidence. Useful for reproducibili

Interactive visualization library. Use when you need hover info, zoom, pan, or web-embeddable charts. Best for dashboards, exploratory analysis, and presentations. For static publication figures use matplotlib or scientific-visualization.

Guides AI agents through Senzing entity resolution workflows using the Senzing MCP server. Covers data mapping to Senzing format, SDK code generation (Python, Java, C#, Rust, TypeScript/Node.js), documentation search, error troubleshooting, sample data access, reporting and visualization, SDK setup guides, and V3-to-V4 migration. Use when working with entity resolution, record matching, record lin

Save, load, update, delete, merge, and navigate entity objects using TMS Aurelius ORM (TObjectManager). Use when the user asks how to persist or retrieve data with Aurelius, manage object lifetime, work with associations or collections at runtime, handle transactions, use cached updates, or control concurrency. Does NOT cover query DSL or criteria/projections (those are in a separate skill). Trigg

Map Delphi classes to a relational database using TMS Aurelius ORM attributes. Use when the user asks to create entity classes, add Aurelius mapping to existing classes, fix or review mapping attributes, explain how a class is mapped, or work with associations, inheritance, automapping, nullable fields, blobs, or composite identifiers. Triggers on requests like "create Aurelius entities for...", "

Diagnose and resolve Apache Spark performance issues in Microsoft Fabric notebooks and Spark Job Definitions. Use when PySpark jobs are slow, notebooks take too long, Spark stages are skewed, shuffles are excessive, out-of-memory errors occur, Delta Lake writes are slow, or Fabric capacity is throttled. Covers data skew, shuffle optimization, broadcast joins, partition tuning, VOrder, Optimized Wr

Diagnose and resolve Microsoft Fabric Data Factory pipeline performance issues. Use when pipelines are slow, copy activities timeout, dataflows stall, activities are stuck, throughput is low, capacity is throttled, or jobs queue indefinitely. Covers copy activity tuning (parallelCopies, DIU, ITO, partitioning), pipeline monitoring via Monitoring Hub and workspace monitoring, Spark job queueing, ca

Diagnose and resolve Microsoft Fabric Data Agent performance issues including slow query generation, capacity throttling (HTTP 430), Spark session startup delays, KQL/SQL/DAX query timeouts, data source misconfiguration, example query validation failures, resource profile tuning, VOrder optimization, autotune settings, and Lakehouse table maintenance. Use when asked to troubleshoot Fabric Data Age

Troubleshoot and optimize pandas performance in Microsoft Fabric Spark notebooks. Use when diagnosing slow pandas operations, toPandas() out-of-memory errors, pandas API on Spark (pyspark.pandas) bottlenecks, DataFrame conversion failures, collect() memory issues, driver memory exhaustion, notebook cell timeouts, or when optimizing pandas workloads for Fabric capacity. Covers pandas vs Spark DataF

Consolidate Agentic SWMM run artifacts into auditable provenance, comparison records, and local Obsidian audit notes. Use after any SWMM build/run/QA attempt, successful or failed, when OpenClaw or a CLI workflow needs a traceable record of inputs, commands, artifacts, metrics, QA checks, run-to-run differences, and first-user-friendly Obsidian visualization.