data-engineering
349
411-frameworks-quarkus-jdbc
Use when you need programmatic JDBC in Quarkus — Agroal DataSource, parameterized SQL, transactions, batching, and Dev Services. Part of the skills-for-java project
jabrena
data-ai
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data-engineering
349
511-frameworks-micronaut-jdbc
Use when you need programmatic JDBC in Micronaut — pooled DataSource, parameterized SQL, io.micronaut.transaction.annotation.Transactional, batching, and domain exception translation. Part of the skills-for-java project
jabrena
data-ai
data-engineering
347
data-analysis
High-performance data analysis using Polars - load, transform, aggregate, visualize and export tabular data. Use for CSV/JSON/Parquet processing, statistical analysis, time series, and creating charts.
ArtificialAnalysis
data-ai
data-engineering
342
migrating-json-schemas
Migrates JSON Schemas between draft versions for use with z-schema. Use when the user wants to upgrade schemas from draft-04 to draft-2020-12, convert between draft formats, update deprecated keywords, replace id with $id, convert definitions to $defs, migrate items to prefixItems, replace dependencies with dependentRequired or dependentSchemas, adopt unevaluatedProperties or unevaluatedItems, or adapt schemas to newer JSON Schema features.
zaggino
data-ai
data-engineering
341
hl-build-pipeline-app
Build a complete GStreamer pipeline app for real-time video processing on Hailo-8/8L/10H.
hailo-ai
data-ai
data-engineering
333
transforming-data
Transform raw data into analytical assets using ETL/ELT patterns, SQL (dbt), Python (pandas/polars/PySpark), and orchestration (Airflow). Use when building data pipelines, implementing incremental models, migrating from pandas to polars, or orchestrating multi-step transformations with testing and quality checks.
ancoleman
data-ai
data-engineering
333
ingesting-data
Data ingestion patterns for loading data from cloud storage, APIs, files, and streaming sources into databases. Use when importing CSV/JSON/Parquet files, pulling from S3/GCS buckets, consuming API feeds, or building ETL pipelines.
ancoleman
data-ai
data-engineering
333
streaming-data
Build event streaming and real-time data pipelines with Kafka, Pulsar, Redpanda, Flink, and Spark. Covers producer/consumer patterns, stream processing, event sourcing, and CDC across TypeScript, Python, Go, and Java. When building real-time systems, microservices communication, or data integration pipelines.
ancoleman
data-ai
data-engineering
332
walkeros-create-transformer
Use when creating a new walkerOS transformer. Example-driven workflow for validation, enrichment, or redaction transformers.
elbwalker
data-ai
data-engineering
332
walkeros-understanding-transformers
Use when working with walkerOS transformers, understanding event validation/enrichment/redaction, or learning about transformer chaining. Covers interface, return values, and pipeline integration.
elbwalker
data-ai
data-engineering
332
nw-command-design-patterns
Best practices for command definition files - size targets, declarative template, anti-patterns, and canonical examples based on research evidence
nWave-ai
data-ai
data-engineering
332
nw-data-architecture-patterns
Data architecture patterns (warehouse, lake, lakehouse, mesh), ETL/ELT pipelines, streaming architectures, scaling strategies, and schema design patterns
nWave-ai
data-ai
data-engineering
332
nw-database-technology-selection
Database comparison catalogs, RDBMS vs NoSQL selection criteria, CAP/ACID/BASE theory, OLTP vs OLAP, and technology-specific characteristics
nWave-ai
data-ai
data-engineering
332
nw-deliver
Orchestrates the full DELIVER wave end-to-end (roadmap > execute-all > finalize). Use when all prior waves are complete and the feature is ready for implementation.
nWave-ai
data-ai
data-engineering
332
nw-der-review-criteria
Evaluation criteria and scoring for data engineering artifact reviews
nWave-ai
data-ai
data-engineering
332
nw-divio-framework
DIVIO/Diataxis four-quadrant documentation framework - type definitions, classification decision tree, and signal catalog
nWave-ai
data-ai
data-engineering
332
nw-research-methodology
Research output templates, distillation workflow, and quality standards for evidence-driven research
nWave-ai
data-ai
data-engineering
325
differential-expression
Bulk transcriptomics differential expression with count-aware modeling, design validation, contrast handling, thresholded exports, and publication-ready DE figures.
Runchuan-BU
data-ai
data-engineering
319
meeting-transcript
Process meeting recordings and notes into structured decisions, action items, and team dynamics with intelligent noise filtering
huytieu
data-ai
data-engineering
317
message-bus
File-based message queue for inter-agent coordination. Used by workers AND board directors to communicate. Provides: progress updates, task completion signals, file locking, board deliberation. Core infrastructure for parallel execution.
Ibrahim-3d
data-ai
data-engineering
317
parallel-dispatch
Parallel execution engine for dispatching worker agents. Used by conductor-orchestrator to spawn multiple workers simultaneously from DAG parallel groups. Handles dispatch, monitoring, aggregation, and failure recovery.
Ibrahim-3d
data-ai
data-engineering
314
scenario-scaffolding
Assists with creating complete ITBench scenarios by applying fault mechanisms to specific services, populating scenario files, and generating groundtruth DSL with fault propagations and alert predictions.
itbench-hub
data-ai
data-engineering
314
sf-data
Salesforce data operations with 130-point scoring. TRIGGER when: user creates test data, performs bulk import/export, uses sf data CLI commands, or needs data factory patterns for Apex tests. DO NOT TRIGGER when: SOQL query writing only (use sf-soql), Apex test execution (use sf-testing), or metadata deployment (use sf-deploy).
Jaganpro
data-ai