snowflake-smoke-test
Skill from oracle-samples/oracle-aidp-samples
Install
npx skills add oracle-samples/oracle-aidp-samples --skill snowflake-smoke-test
Runs the open skills CLI and installs the skill for your agent (Claude Code, Codex, Cursor …). Alternatively copy the skill folder into .claude/skills/ or upload it in Claude.ai under Settings → Skills.
As a Claude Code plugin
/plugin marketplace add anthropics/claude-plugins-official
/plugin install oracle-ai-data-platform-workbench-spark-connectors@claude-plugins-official
This skill is part of the plugin “oracle-ai-data-platform-workbench-spark-connectors”. In Claude Code the plugin installs it together with the plugin’s other skills, commands and hooks.
What does the snowflake-smoke-test skill do?
Description from the SKILL.md in the source repository.
Questions about snowflake-smoke-test
What does the snowflake-smoke-test skill do?
Check connectivity and permissions on both ends before any migration work. Verifies Snowflake is reachable and readable (list databases, read INFORMATION_SCHEMA) and, when AIDP coordinates are supplied, that the target catalog is readable and optionally writable. Use before the first assessment, when credentials change, or whenever a stage fails with an authorization error and you need to know which end is at fault.
How do I install snowflake-smoke-test?
In a terminal: npx skills add oracle-samples/oracle-aidp-samples --skill snowflake-smoke-test. The skills CLI asks which agents to install it for. In Claude.ai you can upload the skill folder as a ZIP under Settings → Skills.
Which agents can use snowflake-smoke-test?
Skills use the open SKILL.md format, which Claude Code, Claude.ai, the Claude API, OpenAI Codex, Cursor, Gemini CLI and other agents support.
Is snowflake-smoke-test safe to use?
Our automatic check found no risky patterns (piped shell scripts, hidden characters, secret access, prompt overrides). It is not a full audit – read the SKILL.md and any scripts before installing.
More skills from oracle-samples/oracle-aidp-samples
Create the medallion architecture on Oracle AI Data Platform - registering an EXTERNAL catalog of source type SNOWFLAKE by default, and generating Spark SQL for schemas, tables and views from an approved Snowflake plan only when the user has explicitly asked for a Standard catalog. This stage copies no data - rows are copied only by the snowmig_02_copy_schema job, when the operator runs it. Use when the user asks to create the medallion structure, soft clone, shallow clone, create the target catalog, or deploy the target schema.
Dev mode. Run the entire Snowflake-to-AIDP migration pipeline against a built-in emulated estate and an emulated AIDP — no credentials, no network, nothing real is touched. Use when the user wants to understand what the migrator does before pointing it at a real account, wants a training walkthrough, or asks "show me how this works" / "demo mode" / "dev mode". Produces every real artifact plus DEMO.md, a narrated walkthrough of the lessons.
First-run setup for the Snowflake to AIDP migrator. Finds or creates the one migration config file (snowmig-config.yaml), reads every field back with secrets masked, verifies Snowflake authentication by password, key-pair, programmatic access token or SSO, verifies the AIDP end, then smoke-tests the connection and reports the account, region, role and warehouse. Use the first time the migrator runs on a machine, or when any other stage fails with an authentication, connection or "no config found" error.
Build a high-level Snowflake to AIDP migration plan and present it for approval. States which objects can migrate and which cannot with a brief reason for each, applies user-supplied restrictions such as excluded databases or size caps, derives dependency order so views follow their base tables, and lists the Silver and Gold job stubs. Use after an estate assessment, or when the user asks what can be migrated, in what order, why something is excluded, or wants to see the migration plan.
Generate the table-creation script for a Standard AIDP catalog as an executable notebook, locally and offline. --upload is a dry run and is refused with --execute; the structure itself is created by the snowmig_01_structure workflow at S10. The script creates schemas, then tables, then views in dependency order, prints per-object progress with elapsed time so a long run stays visible, and verifies each object individually at the end. This script creates structure only and copies no data - every table arrives with zero rows; rows are copied only by the snowmig_02_copy_schema job, when the operator runs it. Use when the user has explicitly asked for a Standard catalog, or wants the migration delivered as a runnable script rather than executed straight from the CLI.
Read-only PREVIEW of a Snowflake environment from the operator's machine - inventory of tables and views with row counts, byte sizes and column types, plus the census of objects that are not tables or views, table-maintenance state and security posture. Use ONLY when the user wants to look at an account without migrating it - answering what is in there, how big the tables are, what the security posture looks like. This is NOT the discovery step of a migration: a migration discovers inside AIDP as a workflow (runbook S6), because a laptop-side read leaves no log and no evidence on the platform. If the user asked to migrate, route to snowflake-migrator-overview and follow S1 through S12.
Router, runbook and shared rules for migrating a Snowflake estate onto Oracle AI Data Platform (AIDP). Read this first whenever the user mentions migrating, moving, assessing, inventorying or cloning Snowflake databases, schemas, tables, views or warehouses onto AIDP, asks to "run the migration" or "start from zero", or asks what a Snowflake migration would involve. Carries the FIXED twelve-step order of operations and the rules that apply at every step; adds no API surface of its own.
Map Snowflake warehouses onto AIDP Spark compute clusters and produce a sizing and cost proposal. Inventories every warehouse with its size, multi-cluster scaling policy and auto-suspend, pulls observed credit consumption from ACCOUNT_USAGE where permitted, and proposes worker counts and autoscale ceilings per warehouse. Use when the user asks about compute sizing, warehouse equivalence, cluster shapes, credits, or what the migration will cost.
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