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sian-new-system-checklist

Provides a pre-code checklist for adding new data source systems or tables to the SIAN platform.

Updated May 21, 2026
Search Tags:#checklist#dbt#data ingestion#airflow#BigQuery#iceberg#data platform
One-click install
npx skills add https://github.com/gestaosian/gtsi-claude-skills --skill sian-new-system-checklist-gestaosian
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sian-new-system-checklist
Source: https://github.com/gestaosian/gtsi-claude-skills/tree/main/plugins/gtsi-ops-plugin/skills/sian-new-system-checklist
Command: npx skills add https://github.com/gestaosian/gtsi-claude-skills --skill sian-new-system-checklist-gestaosian

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Adding a new ingestion system or table to the SIAN data platform involves architectural decisions and artifacts that must be settled before writing DAGs, and missing any of them causes rework or broken deployments. ## Core Features & Use Cases - Architecture Decisions: Guides destination project selection (gcp-sian-dados vs gcp-sian-proj-<empresa>), table format choice (BigQuery native vs Iceberg), factory verification, and Airflow pool planning per ADRs 0012, 0013, 0015, and 0017. - Complete Artifact Checklist: Covers schema JSON files, DAG naming and location conventions, sys.path depth, dbt source declarations, and governance fields like data_owner and data_domain. - Use Case: When onboarding a new CRM system into SIAN, walk through the checklist to decide the destination project, create the schema in data/include/schemas, confirm the DAG factory exists, and set up the dbt folder structure before writing any code. ## Quick Start Use the sian-new-system-checklist skill to walk me through everything required before adding a new source system to the SIAN platform.

Frequently Asked Questions about sian-new-system-checklist

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I add a new data source system to the SIAN platform?▼

Follow the pre-code checklist: decide the destination project, choose the table format, verify or create the DAG factory, configure an Airflow pool if the source has rate limits, then create the schema JSON, DAG file, and dbt folders.

When should I use Iceberg vs BigQuery native tables?▼

Use BigQuery native format for tables under roughly 10 million rows with full daily loads. Use Iceberg managed format for tables above 10 million rows or with continuous growth and incremental delta loads, per ADR 0013.

How do I choose between gcp-sian-dados and a dedicated GCP project?▼

If the system is used by more than one company, the data goes to gcp-sian-dados. If the system is exclusive to a single company, use a dedicated project named gcp-sian-proj-<empresa>, per ADR 0012.

What is the DAG naming convention in SIAN?▼

DAG files follow the pattern {sistema}__{tabela}__{cliente}__{frequência}.py and live under dags/<sistema>/<domínio>/. The dag_id must be identical to the filename without the .py extension.

When do I need to create an Airflow pool for a new system?▼

Create a pool named <sistema>_pool when the source API or database has rate limits or connection limits. Set the slot count based on the source system's limit and create it in Airflow Admin before deploying.

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