Nutze diesen Skill für Phaser Data Manager, Registry, objektgebundene Daten, Change-Events und saubere Zustandsverwaltung.
Service-scoped data orchestration for TMNL. Invoke when implementing search, data streams, kernel systems, or Effect-based DAQ.
Translate between an external service's data model and your own, handling mismatches, nulls, and enum drift explicitly.
Greedy matching of centroid points to expert annotations based on standard Euclidean distance.
Expert-level guide to data mesh implementation. Comprehensive coverage of advanced concepts, production implementation, and optimization strategies.
Metabase REST API automation and troubleshooting: authenticate (API key preferred, session fallback), export/upsert questions (cards) and dashboards, standardize…
Plan and execute database migrations, data transformations, and system migrations safely with rollback strategies and data integrity validation.
Operational data migration guidance. Use when moving or transforming persisted data between schemas, databases, services, or storage technologies; running large backfills;…
Use this agent when reviewing PRs that touch database migrations, data backfills, or any code that transforms production data.
Plan and execute database migrations, data transformations, and system migrations safely with rollback strategies and data integrity validation.
Use when a schema change, backfill, data migration, or persistence-layer transition must be reviewed for rollout order, reversibility, backfill safety, and operational risk.
Large-scale data migrations with validation, rollback, and zero-downtime strategies. Activate on: data migration, database migration, zero-downtime migration, dual-write,…
Validate data integrity during and after migration with comprehensive verification checks
ย้ายข้อมูล vetmanage — pet_exam, owner_exam, item_exam, species, item_type_id, pet_breed. Use ONLY when user sends xlsx/csv files or asks about data migration, species mapping,…
Collect and keep only the personal data a feature actually needs, so exposure, cost, and compliance burden all shrink at once.
Extract patterns and insights from large datasets using association rules, clustering, classification, and anomaly detection.
Competence in data analytics and machine learning, enabling developers to build data-driven features and integrate AI/ML capabilities.
ML-pipeline discipline — every training run logged, lockfile-frozen environments, every seed pinned, the eval suite as a separate package the model never imports, a data hash per…
Adding new data models - schema, shared zod, entity pattern, and oRPC routes. Use when user asks to create a new entity, model, or feature that needs database tables.
Optional advanced tool for complex data modeling. For simple table creation, use relational-database-tool directly with SQL statements.
Ensure data model/DB designs cover constraints, integrity, indexes, migrations, rollback, and operational impacts.
Use when designing, reviewing, or troubleshooting Salesforce object relationships and field type choices — lookup vs master-detail, junction object modeling, indexing strategy,…
Use when a BA or admin needs to document the Salesforce data model: creating field inventories, object relationship maps, ER diagrams, or analyzing field usage across objects.
Audits SwiftData/Core Data model layer for field completeness, serialization gaps, relationship integrity, semantic ambiguity, dead fields, and migration safety.
Standards for generating Model Cards from data analysis notebooks. Aligned with Hugging Face and ISO transparency standards.
Data modeling with Entity-Relationship Diagrams (ERDs), data dictionaries, and conceptual/logical/physical models.
Use when reasoning about the foundational theory beneath data modeling: Codd's relational model (1970) and the algebra it sits on, the normal forms (1NF, 2NF, 3NF, BCNF, 4NF, 5NF)…
Data modeling with Entity-Relationship Diagrams (ERDs), data dictionaries, and conceptual/logical/physical models.
Use when asking about UTXO vs account models, ledger tokens, shielded/unshielded tokens, nullifiers, coins, balances, or choosing between token paradigms in Midnight.
myfy DataModule for database access with async SQLAlchemy. Use when working with DataModule, AsyncSession, database connections, connection pooling, migrations, or SQLAlchemy…
Neo4j graph database exploration via Cypher. Use when the user mentions Neo4j, Cypher, graph queries, nodes, relationships, or knowledge graph.
Use when you have raw or partially processed metabolomics data (mzML/mzXML format) from LC-MS or GC-MS runs and need to apply standardized feature detection, alignment, and…
Data Normalization Tool - Auto-activating skill for ML Training. Triggers on: data normalization tool, data normalization tool Part of the ML Training skill category.
Data Normalization Tool - Auto-activating skill for ML Training. Triggers on: data normalization tool, data normalization tool Part of the ML Training skill category.
Use for the connective work between skills — listing, joining, exporting, and staging the artifacts that move data from one skill to the next. Spans every lifecycle stage.
Coordinates data pipeline tasks (ETL, analytics, feature engineering). Use when implementing data ingestion, transformations, quality checks, or analytics.
Coordinates data pipeline tasks (ETL, analytics, feature engineering). Use when implementing data ingestion, transformations, quality checks, or analytics.
Data Partitioner - Auto-activating skill for Data Pipelines. Triggers on: data partitioner, data partitioner Part of the Data Pipelines skill category.
Partition datasets by pruning-friendly keys with healthy file sizes and scheduled compaction. Use when laying out lake or warehouse tables, or fixing slow scans and small-file…
Implements data persistence systems including DataStore patterns, session locking, data migration, error handling, and backup systems.
Data pipeline and ETL automation - extract, transform, load workflows for data integration and analytics
Designs ETL/ELT data pipelines with proper extraction, transformation, and loading patterns, including orchestration, error handling, and data quality validation.
Build data pipelines that rerun safely, backfill cleanly, and tolerate late data. Use when designing batch or streaming pipelines or fixing ones that need manual babysitting.
ETL pipelines that pull data from multiple sources (APIs, databases, web scraping), transform it, and produce
Expert data engineer for ETL/ELT pipelines, streaming, data warehousing. Activate on: data pipeline, ETL, ELT, data warehouse, Spark, Kafka, Airflow, dbt, data modeling, star…
Develop and manage data ingestion, processing, and transformation pipelines for pilot projects. Use when automating ETL workflows, integrating new data sources, or building…
Expert guide for Data Pipelines, ETL/ELT, and Analytics Engineering. Covers dbt, Apache Airflow, Dagster, BigQuery, ClickHouse, and DuckDB / Panduan ahli untuk Data Pipelines,…
Follow these patterns when implementing data pipelines, ETL, data ingestion, or data validation in OptAIC.
Process data files through transformation pipelines with validation, cleaning, and export. Use for CSV/Excel/JSON data processing, encoding handling, batch operations, and data…
Design an ETL/ELT data pipeline specification. Use when asked to design a data pipeline, spec an ETL or ELT process, document a data ingestion workflow, or plan a data…
Use when building internal data-governance machinery: a retention schedule (period, lawful basis, expiry action, system where deletion runs), an Art.
Data cleaning, profiling, transformation, and quality gates -- prepares raw data for visualization and analysis
Load and preprocess imaging mass cytometry (IMC) and MIBI data. Covers MCD/TIFF handling, hot pixel removal, and image normalization.
Draft and fill data privacy agreement templates — DPA, data processing agreement, GDPR, HIPAA BAA, business associate agreement, AI addendum.
Data privacy and regulatory compliance specialist for GDPR, CCPA, HIPAA, and international data protection laws.
Explain U.S. state-by-state consumer data-privacy law (CCPA/CPRA, TDPSA, VCDPA, CPA, and the other comprehensive state acts) — who a law covers, applicability thresholds,…
Review, draft, or redline a Data Processing Agreement (DPA / Auftragsverarbeitungsvertrag / AVV) under Art. 28 GDPR, or prepare a Joint Controller Arrangement under Art. 26 GDPR.
GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic d — from…
Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow.
Process and transform arrays of data with common operations like filtering, mapping, and aggregation