data-engineering
5
database-migration
ゼロダウンタイム戦略、データ変換、ロールバック手順を使用して、ORM間およびプラットフォーム間でデータベース移行を実行します。データベースの移行、スキーマの変更、データ変換の実行、またはゼロダウンタイムデプロイメント戦略の実装時に使用します。
amurata
data-ai
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data-engineering
5
frappe-data-migration-generator
Generate data migration scripts for Frappe. Use when migrating data from legacy systems, transforming data structures, or importing large datasets.
Venkateshvenki404224
data-ai
data-engineering
5
databricks-python-sdk
Databricks development guidance including Python SDK, Databricks Connect, CLI, and REST API. Use when working with databricks-sdk, databricks-connect, or Databricks APIs.
databricks-solutions
data-ai
data-engineering
5
parquet-files
This describes how to create Parquet files in C#, including updating and multi threaded creation
lawless-m
data-ai
data-engineering
5
connector-config
Writes connector_config for segment/journey activations using `tdx connection schema <type>` to discover available fields. Use when configuring activations - always run schema command first to see connector-specific fields.
treasure-data
data-ai
data-engineering
5
data-reconciliation-exceptions
Reconciles data sources using stable identifiers (Pay Number, driving licence, driver card, and driver qualification card numbers), producing exception reports and “no silent failure” checks. Use when you need weekly matching with explicit reasons for non-joins and mismatches.
clawdbot
data-ai
data-engineering
5
time-filtering
Advanced td_interval patterns including offset dates (-1d/2025-10-01, -7d/-1d, 0M/now), td_interval_range for debugging, td_time_string for display formatting, and partition pruning optimization.
treasure-data
data-ai
data-engineering
4
data-cleaning-pipeline-generator
Generates data cleaning pipelines for pandas/polars with handling for missing values, duplicates, outliers, type conversions, and data validation. Use when user asks to "clean data", "generate data pipeline", "handle missing values", or "remove duplicates from dataset".
Dexploarer
data-ai
data-engineering
4
docs-manager-skill
Orchestrates complete single-document workflows with automatic validation and indexing in a write→validate→index pipeline
fractary
data-ai
data-engineering
4
faber-coordinator
Coordinate FABER-DB operations within FABER workflow phases
fractary
data-ai
data-engineering
4
indexeddb-operations
Guide for working with IndexedDB database operations in the DEVS platform. Use this when asked to add database tables, modify schemas, or work with data persistence.
codename-co
data-ai
data-engineering
4
log-director-skill
Orchestrates multi-log workflows with parallel execution for batch operations across many logs
fractary
data-ai
data-engineering
4
data-transformation
データ変換パイプラインの設計・実装・検証を整理するスキル。 スキーママッピング、ETL設計、品質確認までの実務フローを提供する。 Anchors: • Designing Data-Intensive Applications / 適用: データモデリング / 目的: 変換の整合性確保 • Designing Data-Intensive Applications / 適用: スキーマ設計 / 目的: マッピングの明確化 • Designing Data-Intensive Applications / 適用: パイプライン設計 / 目的: 伸縮性と監視性の確保 Trigger: Use when designing data transformation pipelines, defining schema mappings, implementing ETL processes, or optimizing data flows. data transformation, schema mapping, etl design, pipeline optimization, data modeling
daishiman
data-ai
data-engineering
4
database-seeding
データベースの初期データ投入を安全に設計・実装・検証するスキル。 開発/テスト/本番のデータ分離、シード戦略、再現性の確保を整理する。 Anchors: • Designing Data-Intensive Applications / 適用: データ整合性 / 目的: 参照整合性の担保 • Database Reliability Engineering / 適用: 運用設計 / 目的: 本番投入の安全性 • Data Quality Principles / 適用: データ品質 / 目的: 再現性と検証性の確保 Trigger: Use when planning database seeding, generating test fixtures, separating datasets by environment, or validating seed data quality. database seeding, test data, fixtures, seed strategy, environment separation, data validation
daishiman
data-ai
data-engineering
3
archiving-databases
Use when you need to archive historical database records to reduce primary database size. This skill automates moving old data to archive tables or cold storage (S3, Azure Blob, GCS). Trigger with phrases like "archive old database records", "implement data retention policy", "move historical data to cold storage", or "reduce database size with archival".
BbgnsurfTech
data-ai
data-engineering
3
policyengine-simulation-mechanics
Advanced simulation patterns with policyengine.py - ensure(), output_dataset.data, and map_to_entity()
PolicyEngine
data-ai
data-engineering
3
implementing-database-audit-logging
Use when you need to track database changes for compliance and security monitoring. This skill implements audit logging using triggers, application-level logging, CDC, or native logs. Trigger with phrases like "implement database audit logging", "add audit trails", "track database changes", or "monitor database activity for compliance".
BbgnsurfTech
data-ai
data-engineering
3
feature-flag-create-or-remove
When creating or removing a new feature flag from the database
jakewaldrip
data-ai
data-engineering
3
effect-streams-pipelines
Stream creation, transformation, sinks, batching, and resilience. Use when building data pipelines with concurrency and backpressure.
mepuka
data-ai
data-engineering
3
effect-collections-datastructs
Value-based data structures (Data.struct, tuple, array) and high-performance collections (Chunk, HashSet). Use for safe comparisons and pipelines.
mepuka
data-ai
data-engineering
3
memory-hygiene
Maintains memory cleanliness with deduplication, validation, and expiration
benreceveur
data-ai