Builds configuration-driven CRUD tables with the Avue framework, including column definition, pagination, search, sorting, row operations (add/edit/delete), data export, and form validation. Use when the user needs to create data management interfaces with Avue CRUD tables in Vue 2 applications.
Sync delta specs from a change into main specs using `/opsx:sync`, without archiving the change. Use when the user says "sync specs", "merge specs to main", "/opsx:sync", or needs to update main specs mid-change.
Provides comprehensive guidance for Spring Data JPA including repositories, entity management, query methods, and database operations. Use when the user asks about Spring Data JPA, needs to work with JPA repositories, implement data access layers, or configure JPA in Spring.
ABAP performance best practices for S/4HANA and HANA database systems.Use when writing or reviewing ABAP code on HANA-based systems.IMPORTANT: First use the SAP system info tool to check the system type — if the system is ECC or runs on a traditional database (Oracle, DB2, MSSQL), load the abap-performance-ecc skill instead. Covers code pushdown, CDS views, AMDP, advanced SQL, and HANA-optimized patterns.
Design, refactor, and validate Grafana dashboards for OpenShift/Kubernetes platform operations. Use when users ask to improve platform health dashboards, prioritize critical tenant-impacting signals, filter noise (for example ArgoCD), add Crossplane/Keycloak health panels, validate PromQL programmatically, or apply GrafanaDashboard CR changes live then promote to GitOps.
Design, refactor, and validate Grafana dashboards for OpenShift/Kubernetes platform operations. Use when users ask to improve platform health dashboards, prioritize critical tenant-impacting signals, filter noise (for example ArgoCD), add Crossplane/Keycloak health panels, validate PromQL programmatically, or apply GrafanaDashboard CR changes live then promote to GitOps.
Assists developers in collecting and structuring a library of diverse examples ("Golden Dataset") required for data-driven evaluation, including tool trajectories.
Production-safe Drizzle migration workflow for schema changes that require data backfills or constraint tightening. Use when changing enums/check constraints/defaults, removing status values, or sequencing custom and generated migrations in Drizzle. Trigger on requests about Drizzle migration safety, deployment-safe backfills, migration ordering, and rollback planning.
Comprehensive guide for XState v5 ecosystem including state machines, actors, @xstate/store, and TanStack Query integration. Use when implementing state machines, event-driven stores, client state management, or integrating XState with React and TanStack Query for data fetching orchestration.
Build, review, or migrate Azure Functions in .NET with correct execution model, isolated worker setup, bindings, DI, and Durable Functions patterns.
Maintain or migrate EF6-based applications with realistic guidance on what to keep, what to modernize, and when EF Core is or is not the right next step. Use when working in an EF6 codebase or planning a data layer migration.
Design, tune, or review EF Core data access with proper modeling, migrations, query translation, performance, and lifetime management for modern .NET applications.
Use ManagedCode.Orleans.SignalR when a distributed .NET application needs Orleans-based coordination of SignalR real-time messaging, hub delivery, and grain-driven push flows.
Use Sep for high-performance separated-value parsing and writing in .NET, including delimiter inference, explicit parser/writer options, and low-allocation row/column workflows.
Apply MCAF human-review-planning guidance for a large AI-generated code drop by reading the target area, tracing the natural user and system flows, identifying the riskiest boundaries, and prioritizing the files a human should inspect first. Use when the codebase is too large to review line-by-line and you need a practical review sequence plus a prioritized file list.
Implement end-to-end Medallion Architecture (Bronze/Silver/Gold) lakehouse patterns in Microsoft Fabric using PySpark, Delta Lake, and Fabric Pipelines. Use when the user wants to: (1) design a Bronze/Silver/Gold data lakehouse, (2) set up multi-layer workspace with lakehouses for each tier, (3) build ingestion-to-analytics pipelines with data quality enforcement, (4) optimize Spark configurations per medallion layer, (5) orchestrate Bronze-to-Silver-to-Gold flows via notebooks. Triggers: "medallion architecture", "bronze silver gold", "lakehouse layers", "e2e data pipeline", "end-to-end lakehouse", "data lakehouse pattern", "multi-layer lakehouse", "build medallion", "setup medallion".
Run KQL queries against Fabric Eventhouse for real-time intelligence and time-series analytics using `az rest` against the Kusto REST API. Covers KQL operators (where, summarize, join, render), Eventhouse schema discovery (.show tables), time-series patterns with bin(), and ingestion monitoring. Use when the user wants to: 1. Run read-only KQL queries against an Eventhouse or KQL Database 2. Discover Eventhouse table schema and metadata 3. Analyse real-time or time-series data with KQL operators 4. Monitor ingestion health and active KQL queries 5. Export KQL results to JSON Triggers: "kql query", "kusto query", "eventhouse query", "kql database", "real-time intelligence", "time-series kql", "query eventhouse", "explore eventhouse", "show tables kql"
Develop Microsoft Fabric Spark/data engineering workflows with intelligent routing to specialized resources. Provides core workspace/lakehouse management and routes to: data engineering patterns, development workflow, or infrastructure orchestration. Use when the user wants to: (1) manage Fabric workspaces and resources, (2) develop notebooks and PySpark applications, (3) design data pipelines and orchestration, (4) provision infrastructure as code. Triggers: "develop notebook", "data engineering", "workspace setup", "pipeline design", "infrastructure provisioning", "Delta Lake patterns", "Spark development", "lakehouse configuration", "organize lakehouse tables", "create Livy session", "notebook deployment".
Execute authoring T-SQL (DDL, DML, data ingestion, transactions, schema changes) against Microsoft Fabric Data Warehouse and SQL endpoints from agentic CLI environments. Use when the user wants to: (1) create/alter/drop tables from terminal, (2) insert/update/delete/merge data via CLI, (3) run COPY INTO or OPENROWSET ingestion, (4) manage transactions or stored procedures, (5) perform schema evolution, (6) use time travel or snapshots, (7) generate ETL/ELT shell scripts, (8) create views/functions/procedures on Lakehouse SQLEP. Triggers: "create table in warehouse", "insert data via T-SQL", "load from ADLS", "COPY INTO", "run ETL with T-SQL", "alter warehouse table", "upsert with T-SQL", "merge into warehouse", "create T-SQL procedure", "warehouse time travel", "recover deleted warehouse data", "create warehouse schema", "deploy warehouse", "transaction conflict", "snapshot isolation error".
Design pipes-and-filters for sequential data transformations. Use when data flows through processing stages.
Generate workflow diagrams showing process steps, decision points, and state transitions
Orchestrates egregore's QUALITY pipeline stage. Runs convention checks and invokes review skills for each quality step. Supports self-review (pre-PR) and PR-review (other agents' PRs) modes.
Ecto patterns — schemas, changesets, queries, migrations, Multi, associations, preloads, upserts. Use when editing Repo calls, Ecto.Query, or schema fields. Skip for Ash.
Coordinates data pipeline tasks (ETL, analytics, feature engineering). Use when implementing data ingestion, transformations, quality checks, or analytics. Applies data-quality-standard.md (95% minimum).
