>

Dynamic Application Security Testing execution and management. Configure and execute OWASP ZAP and Nuclei scans, run authenticated scanning, manage scan policies and scope, correlate findings with SAST results, and generate comprehensive vulnerability reports.

>

Database access layer guidelines for Quantum Skincare's Prisma-based data-access library. Covers Prisma schema design, DAO patterns, UUID primary keys, PostgreSQL role-based access control (RBAC), migration workflows, type-safe queries, transaction handling, soft deletes, and testing strategies. Use when working with Prisma schema, DAOs, database migrations, or data access patterns in libs/data-access.

Comprehensive data science, machine learning, and AI guide covering Python, deep learning, NLP, LLMs, prompt engineering, and MLOps. Use when building AI models, data pipelines, or machine learning systems.

데이터 분석 전문가. pandas, numpy, 시각화, 통계 분석 지원.

Expert in business intelligence, SQL, data visualization, and translating data into actionable business insights.

Analytics engineering for reliable metrics and BI readiness. Build transformation layers, dimensional models, semantic metrics, data quality tests, and documentation. Use when you need dbt or SQL transformation strategy, metrics definition, or analytics data modeling.

Advanced data analysis, pattern detection, and insight generation from structured and unstructured datasets

Create, validate, test, and manage data contracts using the Open Data Contract Specification (ODCS) and the datacontract CLI. Use when working with data contracts, ODCS specifications, data quality rules, or when the user mentions datacontract CLI or data contract workflows.

Generate high-quality synthetic datasets using statistical samplers and Claude's native LLM capabilities. Use when users ask to create synthetic data, generate datasets, create fake/mock data, generate test data, training data, or any data generation task. Supports CSV, JSON, JSONL, Parquet output. Adapted from NVIDIA NeMo DataDesigner (Apache 2.0).

Use when user needs scalable data pipeline development, ETL/ELT implementation, or data infrastructure design.

>

Import `.synnovator/*.md` files (YAML frontmatter + Markdown body) into SQLite database via SQLAlchemy models.

Build new data ingestion providers following the FF Analytics registry pattern. This skill should be used when adding new data sources (APIs, files, databases) to the data pipeline. Guides through creating provider packages, registry mappings, loader functions, storage integration, primary key tests, and sampling tools following established patterns.

Provides architectural guidance for data lake design including partitioning strategies, storage layout, schema design, and lakehouse patterns. Activates when users discuss data lake architecture, partitioning, or large-scale data organization.

Data mapping patterns for transforming API responses to internal types

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 schema, streaming data, batch processing, data quality. NOT for: API design (use api-architect), ML training (use ML skills), dashboards (use design skills).

Comprehensive guide to data quality validation, testing frameworks, anomaly detection, and data observability for production data pipelines

Data discovery and analysis specialist focused on extracting actionable insights from complex datasets, identifying patterns and anomalies, and transforming raw data into strategic intelligence. Excels at multi-source data integration, advanced analytics, and data-driven decision support.

Expert data researcher specializing in discovering, collecting, and analyzing diverse data sources. Masters data mining, statistical analysis, and pattern recognition with focus on extracting meaningful insights from complex datasets to support evidence-based decisions.

Expert in statistical analysis, predictive modeling, machine learning, and data storytelling to drive business insights.

Create database seed scripts with realistic test data for development and testing. Use when setting up development environment or creating demo data.

data-visualizer

Expert for developing Streamlit data apps for Keboola deployment. Activates when building, modifying, or debugging Keboola data apps, Streamlit dashboards, adding filters, creating pages, or fixing data app issues. Validates data structures using Keboola MCP before writing code, tests implementations with Playwright browser automation, and follows SQL-first architecture patterns.

Database architecture and design specialist. Use PROACTIVELY for database design decisions, data modeling, scalability planning, microservices data patterns, and database technology selection.

Backup database before tests, migrations, or other database operations

Implement backup and restore strategies for disaster recovery. Use when creating backup plans, testing restore procedures, or setting up automated backups.

Configures and manages database connection pools, with a focus on SQLAlchemy. Use this skill for tasks involving connection pool configuration, sizing, lifecycle management, health checks, and monitoring. It provides specialized guidance for both traditional server-based databases and serverless databases like Neon and PlanetScale.

Specialized database operations for Chuukese language data including dictionary management, phrase collections, translation pairs, and linguistic metadata. Use when working with Chuukese language databases, managing translation data, or performing database operations on linguistic datasets.

Comprehensive database management workflow that orchestrates database architecture, schema design, performance optimization, and data governance. Handles everything from database design and implementation to performance tuning, backup strategies, and data migration.

Database schema migration patterns for Aurora MySQL including reconciliation migrations, idempotent operations, and MySQL-specific gotchas.

Expert database optimizer specializing in modern performance

Resets local development database by deleting all data and restarting API container to trigger auto-seeding. SINGLE SOURCE OF TRUTH for dev database reset automation.

Test migrations, integrity, and query performance.

Database schema validation, data integrity testing, migration testing, transaction isolation, and query performance. Use when testing data persistence, ensuring referential integrity, or validating database migrations.

Any time database-related activity is required.

Language-agnostic database best practices covering migrations, schema design, ORM patterns, query optimization, and testing strategies. Activate when working with database files, migrations, schema changes, SQL, ORM code, database tests, or when user mentions migrations, schema design, SQL optimization, NoSQL, database patterns, or connection pooling.

Databricks development guidance including Python SDK, Databricks Connect, CLI, and REST API. Use when working with databricks-sdk, databricks-connect, or Databricks APIs.

Kailash DataFlow - zero-config database framework with automatic model-to-node generation. Use when asking about 'database operations', 'DataFlow', 'database models', 'CRUD operations', 'bulk operations', 'database queries', 'database migrations', 'multi-tenancy', 'multi-instance', 'database transactions', 'PostgreSQL', 'MySQL', 'SQLite', 'MongoDB', 'pgvector', 'vector search', 'document database', 'RAG', 'semantic search', 'existing database', 'database performance', 'database deployment', 'database testing', or 'TDD with databases'. DataFlow is NOT an ORM - it generates 11 workflow nodes per SQL model, 8 nodes for MongoDB, and 3 nodes for vector operations.

Use when developing BigQuery Dataform transformations, SQLX files, source declarations, or troubleshooting pipelines - enforces TDD workflow (tests first), ALWAYS use ${ref()} never hardcoded table paths, comprehensive columns:{} documentation, safety practices (--schema-suffix dev, --dry-run), proper ref() syntax, .sqlx for new declarations, no schema config in operations/tests, and architecture patterns that prevent technical debt under time pressure

Extracts key specifications from component datasheet PDFs for maker projects. Use when user shares a datasheet PDF URL, asks about component specs, needs pin assignments, I2C addresses, timing requirements, or register maps. Downloads and parses PDF to extract essentials. Complements datasheet-parser for quick lookups.

DAW-specific quirks, known issues, and workarounds for Logic Pro, Ableton Live, Pro Tools, Cubase, Reaper, FL Studio, Bitwig with format-specific requirements (AU/VST3/AAX). Use when troubleshooting DAW compatibility, fixing host-specific bugs, implementing DAW workarounds, passing auval validation, or debugging automation issues.

This skill should be used when seeding databases with realistic fake data for development, testing, or staging environments. Supports PostgreSQL, MySQL, SQLite, MongoDB with ORM-based seeding (SQLAlchemy, Django, Prisma) and Faker library for generating realistic test data. Use when the user needs to populate databases with sample data, create test fixtures, or set up development/staging environments with realistic data.

SQLite database management with Prisma ORM, type-safe queries, and Railway deployment with Litestream backup. This skill should be used when creating database schemas, writing migrations, managing SQLite on Railway volumes, or troubleshooting database issues.

DBOS durable execution patterns and CRITICAL constraints for ChainGraph executor. Use when working on workflows, steps, execution, or any DBOS-related code. Contains MUST-FOLLOW constraints about what can be called from workflows vs steps. Triggers: dbos, workflow, step, durable, execution, startWorkflow, writeStream, recv, send, runStep, atomic, checkpoint, WorkflowQueue, queue, cancelWorkflow, Promise.allSettled. (project)

Complete guide for dbt data transformation including models, tests, documentation, incremental builds, macros, packages, and production workflows

PROACTIVE skill - STOP and invoke BEFORE writing dbt SQL. Validates models against coding conventions for staging, integration, and warehouse layers. Covers naming, SQL structure, field conventions, testing, and documentation. CRITICAL - When about to write .sql files in models/, invoke this skill first, write second. Supports project-specific convention overrides and sqlfluff integration.

Create dbt models following FF Analytics Kimball patterns and 2×2 stat model. This skill should be used when creating staging models, core facts/dimensions, or analytical marts. Guides through model creation with proper grain, tests, External Parquet configuration, and per-model YAML documentation using dbt 1.10+ syntax.