databases
Work with MongoDB (document database, BSON documents, aggregation pipelines, Atlas cloud) and PostgreSQL (relational database, SQL queries, psql CLI, pgAdmin). Use when designing database schemas, writing queri...
发现 devops data engineering 相关 Skills,比较来源仓库和用途,查看适合你工作流的技能说明。
Work with MongoDB (document database, BSON documents, aggregation pipelines, Atlas cloud) and PostgreSQL (relational database, SQL queries, psql CLI, pgAdmin). Use when designing database schemas, writing queri...
Copilot agent for database schema design, ER diagrams, normalization, DDL generation, and performance optimization Trigger terms: database design, schema design, ER diagram, normalization, DDL, database modeli...
Copilot agent that assists with performance analysis, bottleneck detection, optimization strategies, and benchmarking Trigger terms: performance optimization, performance tuning, profiling, benchmark, bottlene...
Copilot agent that assists with database operations, performance tuning, backup/recovery, monitoring, and high availability configuration Trigger terms: database administration, DBA, database tuning, performan...
Analyzes impact of proposed changes on existing systems (brownfield projects) with delta spec validation. Trigger terms: change impact, impact analysis, brownfield, delta spec, change proposal, change manageme...
ElectroDB patterns for DynamoDB single-table design with full type safety and optimized access patterns
Single table design patterns for DynamoDB with SST - modeling complex data relationships in a single table for optimal performance and cost
Generate execution plans from user queries through intake, research, specification, and validation. Integrated with DecisionLearningIntegration for deterministic, self-learning planning. Use when user provides...
Generate execution plans from user queries through intake, research, specification, and validation. Use when user provides any query (vague to specific) and needs an executable plan for /execute skill.
Use when working with SQLMesh projects - creating models, reviewing plans, validating changes, debugging failures, or deploying to production environments.
Use when editing .sql files, reviewing SQL in Python/JavaScript/TypeScript code, writing database queries, or when user mentions PostgreSQL, SQL validation, query optimization, or database schema.
Isolate and test parser behavior on specific text snippets to debug pattern matching, validate regex patterns against edge cases, understand which extraction rules triggered, and test parser changes before full...
End-to-end orchestrator for parsing raw workout logs through the complete 4-stage pipeline (ingest, parse, validate, commit) with quality gates and git tracking. Use this skill when: (1) Processing new workout...
Run the full ParserZamaActive validation suite to ensure database schema is synchronized, parser logic matches golden set expectations, and block type classification is accurate. CRITICAL - Use this skill BEFOR...
Persists ds.episode_skill_ingest_run_v1 in the lab’s genetic file store.
Central data strategy coordinator for AI-driven crypto trading. Use when building data pipelines, validating data quality, coordinating multi-source data aggregation, or implementing ML-ready data infrastructur...
Generate safe PostgreSQL queries from natural language. Supports three databases (small, medium, large) with automatic schema detection. Only generates SELECT queries - no writes, no dangerous operations.
Audits Decision JSONL logs for schema compliance, required metadata, and invariants across recorded decisions.
Create standardized metadata for data assets. Use when documenting new datasets, building data catalogs, improving data discoverability, or creating data dictionaries for teams.
Transform data findings into compelling insights. Use when converting analysis results into actionable insights, connecting findings to business impact, or preparing insights for stakeholder communication.
Database schema understanding and relationship mapping. Use when exploring unfamiliar databases, documenting table relationships, identifying join paths, or generating ERD documentation for existing schemas.
Comprehensive data quality assessment against defined business rules and constraints. Use when validating data against expected schemas, checking referential integrity across tables, or auditing data pipeline o...
SQL query review and validation for correctness, performance, and best practices. Use when reviewing queries for logical errors, optimizing query performance, checking for SQL anti-patterns, or validating busin...
Create comprehensive semantic layer documentation for analytics assets. Use when documenting data models, defining business metrics, creating data dictionaries, or building context for AI-assisted analysis.
默认按已收录数据中的 GitHub 仓库 Stars 排序。Stars 属于来源仓库,同一仓库的多个技能可能共享相同数值;它反映仓库关注度,不代表单个技能的安装量或质量评分。