clickzetta-analytics-agent
clickzetta/clickzetta-skills
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
clickzetta-semantic-view
clickzetta/clickzetta-skills
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
clickzetta-data-science
clickzetta/clickzetta-skills
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
clickzetta-zettapark
clickzetta/clickzetta-skills
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
singsight-install
clickzetta/clickzetta-skills
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
clickzetta-dbt-modeling
clickzetta/clickzetta-skills
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
