Skills DirectoryBusiness & Operations
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Evaluates activist investor campaigns with thesis assessment, proposed changes, and likely outcome analysis. Use when analyzing activist situations, evaluating campaign theses, or assessing activist track records.
| Atributo | Valor | |----------|-------| | **ID** | `sre-slo-sli-sla` | | **Nivel** | ๐ด Avanzado | | **Versiรณn** | 1.0.0 | | **Keywords** | `slo`, `sli`, `sla`, `error-budget`, `service-level`, `reliability`, `availability` | | **Referencia** | [Google SRE Book - SLIs, SLAs, SLOs](https://sre.google/workbook/slo-document/) |
Intelligent skill navigation system with tiered loading and orchestrator-first routing for context-efficient agent operation. Routes tasks to the correct domain skill based on keyword detection with orchestrator priority. Each skill uses progressive disclosure with references/, scripts/, and assets/ directories. Use this skill as the entry point for any Databricks-related task to determine which specialized skills to load.
Databricks DQX framework patterns for advanced data quality validation with detailed failure insights and flexible quarantine strategies. Use when implementing Silver/Gold layer validation, needing richer diagnostics than DLT expectations, or requiring pre-merge validation with detailed failure tracking. Supports YAML configuration, Delta table storage, and serverless compute compatibility.
End-to-end orchestrator for creating Silver layer pipelines using Spark Declarative Pipelines (SDP, formerly DLT) with Delta table-based data quality rules, quarantine patterns, and monitoring views. Orchestrates mandatory dependencies on common skills (databricks-table-properties, databricks-python-imports, databricks-asset-bundles, schema-management-patterns, unity-catalog-constraints, databricks-expert-agent) and Silver-domain skills (dlt-expectations-patterns, dqx-patterns). Use when crea...
Patterns for setting up Databricks Genie Spaces with comprehensive agent instructions, data assets, SQL expressions, and benchmark questions. Use when creating Genie Spaces, configuring agent behavior, selecting data assets, defining SQL expressions (measures, filters, dimensions), or validating benchmark questions. Includes mandatory 8-section deliverable structure, General Instructions (โค20 lines), data asset organization (Metric Views โ TVFs โ Tables), SQL expressions (sql_snippets) for st...
End-to-end guide for planning, creating, deploying, and validating Table-Valued Functions (TVFs) in Databricks optimized for Genie Space natural language queries. Use when creating TVFs for Genie Spaces, planning TVF requirements from business questions, troubleshooting TVF compilation errors, or ensuring Genie compatibility. Includes requirements gathering templates, schema validation patterns, SQL requirements (STRING parameters, parameter ordering, LIMIT workarounds), v3.0 bullet-point com...
Standard patterns for creating Databricks Metric Views with semantic metadata for Genie and AI/BI. Use when creating metric views, troubleshooting metric view creation errors, validating schema references before deployment, implementing joins (including snowflake schema patterns), or optimizing metric views for Genie natural language queries.
End-to-end orchestrator for building the Databricks semantic layer including Metric Views, Table-Valued Functions (TVFs), and Genie Spaces. Guides users through metric view creation, TVF development, Genie Space setup, and API-driven deployment. Orchestrates mandatory dependencies on semantic-layer skills (metric-views-patterns, databricks-table-valued-functions, genie-space-patterns, genie-space-export-import-api) and common skills (databricks-asset-bundles, databricks-expert-agent, databric...
Create multi-phase project plans for Databricks data platform solutions with Agent Domain Framework and Agent Layer Architecture. Includes interactive Quick Start with key decisions, industry-specific domain patterns, complete phase document templates (Use Cases, Agents, Frontend), Genie Space integration patterns, deployment order requirements, and worked examples. Supports both acceleration mode (plan on a completed Gold layer) and workshop mode (`planning_mode: workshop`) that plans from t...
Schema-level anomaly detection for Databricks Unity Catalog using the Data Quality API (Public Preview). Automatically monitors table freshness and completeness using ML models. Use when setting up schema-wide data reliability monitoring, detecting stale or incomplete tables, configuring anomaly detection alerts, or querying the system results table. **Auto-triggered by Silver and Gold layer setup workflows** to ensure every new schema has baseline freshness/completeness monitoring from day one.
Production-grade patterns for Databricks AI/BI (Lakeview) dashboards. Prevents visualization errors, deployment failures, and maintenance issues through widget-query alignment, number formatting, parameter configuration, monitoring table patterns, chart scale properties, and automated deployment workflows. Includes pivot tables with hierarchy drill-down and ratio metrics, point/choropleth maps, sankey diagrams, waterfall and histogram charts, cross-filtering and drill-through patterns, filter...
Comprehensive guide for Databricks Lakehouse Monitoring (Data Profiling) with quick-start workflow (2 hours), fill-in-the-blank requirements template, concrete fact/dimension monitor examples, and complete deployment patterns. Uses the new Data Quality API (`databricks.sdk.service.dataquality`). Use when setting up Lakehouse Monitoring for Gold layer tables, creating custom business metrics, designing monitoring strategy, querying monitoring tables for dashboards, or troubleshooting monitor i...
Patterns for creating Gold layer tables dynamically from YAML schema definitions at runtime. Use when managing 10+ Gold layer tables across multiple domains, when schema evolves frequently, or when you want to avoid embedded SQL DDL strings in Python. Includes YAML schema structure, setup script implementation, Asset Bundle configuration, and workflow patterns for schema changes.
Active, source-bounded alignment of a Gold dimensional design to Databricks Industry Vibe Data Models (and other canonical industry reference models โ TM Forum SID, ARTS, ACORD, HL7, BIAN). Use when the customer operates in a recognizable vertical (retail, banking, healthcare, telecom, insurance, hospitality) and wants the Gold layer to cover industry-standard entities and use industry terminology, when checking industry coverage/gaps, or when the customer has a Vibe-generated Silver business...
Cross-validation of Gold layer design artifacts during the design phase. Use when validating that YAML schemas, ERDs, lineage CSVs, and PK/FK references are internally consistent before handing off to implementation. Catches design-time inconsistencies (e.g., column in ERD but not in YAML, FK referencing non-existent table) that would otherwise surface as runtime bugs.
Comprehensive documentation standards for Gold layer tables including naming conventions, column descriptions, and metadata requirements. Use when creating Gold layer tables, columns, or writing documentation to ensure dual-purpose descriptions that serve both business users and technical users (including LLMs like Genie). Includes YAML schema consultation patterns, surrogate key patterns, SCD Type 2 documentation, and implementation guidance for Silver table naming conventions.
Enterprise integration patterns for Gold layer dimensional models. Covers conformed dimensions, the enterprise data warehouse bus matrix, shrunken/rollup dimensions, conformed facts, and drill-across query patterns. Use when planning dimensions shared across multiple fact tables, creating a bus matrix for enterprise integration, designing rollup dimensions, or enabling cross-process analytics. Triggers on "conformed dimension", "bus matrix", "drill-across", "shrunken dimension", "rollup", "en...
End-to-end orchestrator for implementing Gold layer tables, merge scripts, FK constraints, and Asset Bundle jobs from YAML schema definitions. Guides users through Silver contract validation, YAML-driven table creation, Silver-to-Gold MERGE operations (SCD Type 1/2 dimensions, aggregated/transaction facts, accumulating snapshots, factless facts, periodic snapshots, junk dimensions), foreign key constraint application, Asset Bundle job configuration, and post-deployment validation. Orchestrate...
End-to-end orchestrator for designing complete Gold layer schemas with ERDs, YAML files, lineage tracking, and comprehensive business documentation. Guides users through dimensional modeling, ERD creation (master/domain/summary based on table count), YAML schema generation, column-level lineage documentation, business onboarding guide creation, source table mapping, and design validation. Orchestrates design-workers (05-erd-diagrams, 06-table-documentation, 01-grain-definition, 07-design-vali...
Enforces enterprise naming conventions (snake_case, table prefixes, approved abbreviations), dual-purpose COMMENT formats for tables/columns/TVFs/metric views/dashboards/Genie Spaces, and config-aware tagging standards. Scans context/ for customer tagging standards in any format (YAML, CSV, Markdown, JSON, TXT); derives meaningful smart defaults when none supplied. Uses Databricks Data Classification class.* system governed tags for PII (always inferred from column names + customer declaratio...
Provides standard TBLPROPERTIES and metadata patterns for Unity Catalog Delta tables across Bronze, Silver, and Gold medallion layers. Ensures governance compliance, performance optimization, and proper metadata tagging for all table creation operations. Covers required TBLPROPERTIES by layer (Bronze, Silver DLT, Gold), mandatory CLUSTER BY AUTO configuration, Change Data Feed (CDF) enablement, auto-optimize settings, table and column comment patterns (LLM-friendly for Bronze/Silver, dual-pur...
End-to-end Bronze layer creation for testing and demos. Creates table DDLs, generates fake data with Faker, copies from existing sources, and configures Asset Bundle jobs. Covers Unity Catalog compliance, Change Data Feed, automatic liquid clustering, and governance metadata. Use when setting up Bronze layer tables, creating test/demo data, rapid prototyping Medallion Architecture, or bootstrapping a new Databricks project. For Faker-specific patterns (corruption rates, function signatures, p...
็จๆทๅจๆฌ fork **ๆจ้ไปฃ็ **ใๅ PRใๆ้ฎใpush ๅคฑ่ดฅ / LFS / Unprocessable entityใๆถๅฏ็จใ > ๅฎๆด่ฏดๆ๏ผ`ai/docs/GIT_LFS.md`