clickzetta

Complete guide for managing ClickZetta Analytics Agent domains via cz-cli: domain CRUD, table registration, column semantics (types/descriptions/aliases), metrics (simple & compound), Answer Builders, Knowledge Bases, and QA testing. Wizard-driven workflows with intent classification, routing tables, and troubleshooting references. Trigger when the user says: "analytics agent", "分析域", "语义层", "指标", "answer builder", "知识库", "多别名", "column semantics", "domain prompt", "metric create", "AB create", "session run", "domain table add", "semantics set", "knowledge space". Keywords: analytics agent, domain, semantics, metric, answer builder, knowledge base, alias, cz-cli, data analysis, AI agent, semantic layer

Create, query, and manage ClickZetta Lakehouse Semantic Views — schema-level logical models that encapsulate multi-table JOINs and aggregations into a business-friendly layer of logical tables, dimensions, metrics, and facts. Query with the semantic_view() function without writing JOINs or GROUP BY manually. Triggered when user says "create semantic view", "semantic view", "semantic layer", "define metrics", "define dimensions", "unified metric definitions", "business semantic model", "semantic_view()", "CREATE OR REPLACE SEMANTIC VIEW", "FACTS", "PRIVATE metric", "conditional metric", "window metric", "SHOW SEMANTIC VIEWS", "GRANT SELECT ON SEMANTIC VIEW". Keywords: semantic view, dimension, metric, fact, logical model, unified metrics, semantic layer, grain, chasm trap, FILTER metric

End-to-end data science workflow guide for ClickZetta Lakehouse, covering environment setup, data discovery, feature engineering (SQL + ZettaPark), and model inference deployment. Details: Python 3.10+/Jupyter/ZettaPark setup, project structure, data quality assessment, and inference (BITMAP profiling, UDF batch inference, vector search). Trigger when the user wants to do data science, ML, or analytical work using ClickZetta Lakehouse — connecting Jupyter to Lakehouse, doing EDA, building features, running ML inference, user profiling, audience segmentation, or batch scoring. Keywords: data science, ML, ZettaPark, Jupyter, feature engineering, EDA, profiling, inference

Use the ZettaPark Python library to work with ClickZetta Lakehouse data through a pandas-like DataFrame API that translates Python transformations into distributed SQL — no manual SQL needed. Covers Session creation, DataFrame construction and transformation (filter/select/join/groupBy), result collection (collect/to_pandas/show), writing tables (save_as_table), file operations (PUT/GET), and executing SQL. Trigger when the user says: "ZettaPark", "zettapark", "DataFrame API", "Python Lakehouse", "save_as_table", "session.table", "session.sql", "collect()", "to_pandas", "Python data engineering", "Python write to Lakehouse", "Python read from Lakehouse", "clickzetta_zettapark_python", "Python ETL", "Python ML on Lakehouse", "feature engineering Python", "Python DataFrame Lakehouse". Keywords: ZettaPark, DataFrame, pandas-like, Python, SQL translation, distributed compute

Guide users to connect their AI coding agents (Claude Code, Hermes Agent, OpenClaw, Opencode) to the Singsight AI Agent observability platform. Automatically reports every LLM call, tool execution, token usage, and session via OpenTelemetry. Triggered when the user says "connect Singsight", "install Singsight", "configure Singsight", "Agent observability", "OTel integration", "report token usage", "monitor AI Agent", "singsight install". Keywords: singsight, install, observability, opentelemetry, otel, agent telemetry, claude code, hermes, openclaw, opencode

dbt-clickzetta data modeling wizard: discovers Lakehouse data sources, infers modeling strategies, generates sources.yml and model files, and runs the full test suite. Presents choices rather than blank forms — explores data first, then proposes a justified plan for user confirmation. Trigger when the user wants to turn raw Lakehouse data into usable analytical tables — mentions dbt (dbt modeling, dbt model, sources.yml, incremental, dbt run, dbt test), wants ETL transformation, dimension/fact tables, or asks "how do I turn these tables into a queryable dataset". Keywords: dbt, dbt modeling, sources.yml, incremental, dbt run, dbt test, dimension table, fact table, ETL

dbt-clickzetta project initialization wizard: builds a dbt project connected to ClickZetta Lakehouse, covering installation checks, profiles.yml, layering standards, and dbt_project.yml generation. Hands off to clickzetta-dbt-modeling after completion. Trigger when the user wants to build a data warehouse or start a dbt project in ClickZetta — "I want to build a data warehouse with dbt", "dbt beginner guide", "dbt project init", "dbt from scratch", "how to connect dbt to ClickZetta", "dbt profiles configuration", "how do I write profiles.yml". Keywords: dbt, project setup, profiles.yml, dbt-clickzetta, initialization, ELT, data warehouse setup

ClickZetta Lakehouse data warehouse modeling wizard: explores the data landscape, then designs layered models (Traditional DW ODS/DWD/DWS/ADS, Medallion, or hybrid) with DDL and pipeline config together. Presents justified recommendations — never asks questionnaire-style questions. Core principle: use Dynamic Table for aggregation layers, not Materialized View. Trigger when the user says: "data warehouse modeling", "layering design", "ODS/DWD/DWS", "Medallion", "Bronze/Silver/Gold", "fact table", "dimension table", "star schema", "layered architecture", "modeling wizard", "build a data warehouse", "DW architecture", "design my data layers". Keywords: data warehouse, modeling, star schema, medallion, ODS, DWD, DWS, ADS, layering

Use when answering operational ClickZetta Studio or Lakehouse requests from inside a cz-cli-aware agent, including listing metadata, running SQL, managing tasks, checking runs, or operating datasource and AI Gateway resources.

Delegate ClickZetta Lakehouse OPERATIONS (run SQL, manage tables/schemas, create Studio tasks, set up sync/ingest pipelines, configure profiles) to the cz-cli agent. TRIGGER when user explicitly mentions ClickZetta, Lakehouse, cz-cli, or a known profile/workspace name AND wants to EXECUTE an operation (query data, create/alter tables, deploy tasks, build pipelines, set up a new connection). SKIP when (1) user is developing the cz-cli tool itself (cwd is the cz-cli source repo, editing CLI source/tests, debugging build/install/unlink/permission issues), (2) only discussing cz-cli design/code without wanting to run anything on Lakehouse, or (3) the current project already has its own datasource query capabilities (e.g. project has AGENTS.md or skills that provide SQL execution endpoints) — do not intercept generic 'query data' or 'run SQL' requests that belong to the host project's own toolchain.

Use when answering operational ClickZetta Studio or Lakehouse requests from inside a cz-cli-aware agent, including listing metadata, running SQL, managing tasks, checking runs, or operating datasource and AI Gateway resources.

Use when answering operational ClickZetta Studio or Lakehouse requests from inside a cz-cli-aware agent, including listing metadata, running SQL, managing tasks, checking runs, or operating datasource and AI Gateway resources.

Use when answering operational ClickZetta Studio or Lakehouse requests from inside a cz-cli-aware agent, including listing metadata, running SQL, managing tasks, checking runs, or operating datasource and AI Gateway resources.

Source prerequisite adapter for engines that cannot natively provide versioned reads or CDC. Derives Snapshot and Delta from business-level partition semantics instead. Use when the engine lacks time-travel, changelog tables, or CDC. CRITICAL PREREQUISITE: partitions already consumed by a previous REFRESH must be immutable — if historical partitions can be modified after consumption, this skill cannot be used. Must complete a structured interview with the user before activating.

Iceberg source primitives for incremental computation: Snapshot, Delta (with inserts AND deletes), Version, Schema. Maps Iceberg changelog to __weight model.

Master skill for incremental computation / materialized view maintenance. Triggers on: incremental computation, incremental ETL, materialized view, delta processing, incremental refresh. Routes to algorithm skills.

Use when releasing the Kilo JetBrains plugin -- resolve a version ("next rc" or explicit), run the prepare workflow, edit and commit a filtered human-readable changelog on the release PR, then watch publish to completion.

Use when creating, modifying, or reviewing Kotlin/Swing UI code for the Kilo JetBrains plugin. Applies to dialogs, settings pages, tool windows, forms, panels, component layout, sizing, spacing, colors, borders, icons, lists, trees, popups, notifications, and IntelliJ platform UI components.