Snowflake Development

Overview

This public intake copy packages

plugins/antigravity-awesome-skills-claude/skills/snowflake-development

https://github.com/sickn33/antigravity-awesome-skills

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses

metadata.json

ORIGIN.md

Snowflake Development You are a Snowflake development expert. Apply these rules when writing SQL, building data pipelines, using Cortex AI, or working with Snowpark Python on Snowflake.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: SQL Best Practices, Data Pipelines, Cortex AI, Snowpark Python, dbt on Snowflake, Performance.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• When the user asks for help with Snowflake SQL, data pipelines, Cortex AI, or Snowpark Python.
• When you need Snowflake-specific guidance for dbt, performance tuning, or security hardening.
• Use when the request clearly matches the imported source intent: Comprehensive Snowflake development assistant covering SQL best practices, data pipeline design (Dynamic Tables, Streams, Tasks, Snowpipe), Cortex AI functions, Cortex Agents, Snowpark Python, dbt integration,....
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
• Use when provenance needs to stay visible in the answer, PR, or review packet.
• Use when copied upstream references, examples, or scripts materially improve the answer.

Operating Table

metadata.json

ORIGIN.md

SKILL.md

SKILL.md

## Related Skills

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

Imported: SQL Best Practices

Naming and Style

• Use

  snake_case

  for all identifiers. Avoid double-quoted identifiers — they create case-sensitive names requiring constant quoting.
• Use CTEs (

  WITH

  clauses) over nested subqueries.
• Use

  CREATE OR REPLACE

  for idempotent DDL.
• Use explicit column lists — never

  SELECT *

  in production (Snowflake's columnar storage scans only referenced columns).

Stored Procedures — Colon Prefix Rule

In SQL stored procedures (BEGIN...END blocks), variables and parameters must use the colon

:

BAD:

CREATE PROCEDURE my_proc(p_id INT) RETURNS STRING LANGUAGE SQL AS
BEGIN
    LET result STRING;
    SELECT name INTO result FROM users WHERE id = p_id;
    RETURN result;
END;

GOOD:

CREATE PROCEDURE my_proc(p_id INT) RETURNS STRING LANGUAGE SQL AS
BEGIN
    LET result STRING;
    SELECT name INTO :result FROM users WHERE id = :p_id;
    RETURN result;
END;

Semi-Structured Data

• VARIANT, OBJECT, ARRAY for JSON/Avro/Parquet/ORC.
• Access nested fields:

  src:customer.name::STRING

  . Always cast:

  src:price::NUMBER(10,2)

  .
• VARIANT null vs SQL NULL: JSON

  null

  is stored as

  "null"

  . Use

  STRIP_NULL_VALUE = TRUE

  on load.
• Flatten arrays:

  SELECT f.value:name::STRING FROM my_table, LATERAL FLATTEN(input => src:items) f;

MERGE for Upserts

MERGE INTO target t USING source s ON t.id = s.id
WHEN MATCHED THEN UPDATE SET t.name = s.name, t.updated_at = CURRENT_TIMESTAMP()
WHEN NOT MATCHED THEN INSERT (id, name, updated_at) VALUES (s.id, s.name, CURRENT_TIMESTAMP());

Examples

Example 1: Ask for the upstream workflow directly

Use @snowflake-development to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @snowflake-development against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @snowflake-development for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @snowflake-development using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in

plugins/antigravity-awesome-skills-claude/skills/snowflake-development

metadata.json

ORIGIN.md

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated

SKILL.md

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @server-management

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @service-mesh-expert

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @service-mesh-observability

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @sexual-health-analyzer

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

references

references/n/a

examples

examples/n/a

scripts

scripts/n/a

agents

agents/n/a

assets

assets/n/a

Imported Reference Notes

Imported: Data Pipelines

Choosing Your Approach

Dynamic Tables

CREATE OR REPLACE DYNAMIC TABLE cleaned_events
    TARGET_LAG = '5 minutes'
    WAREHOUSE = transform_wh
    AS
    SELECT event_id, event_type, user_id, event_timestamp
    FROM raw_events
    WHERE event_type IS NOT NULL;

Key rules:

• Set

  TARGET_LAG

  progressively: tighter at top, looser at bottom.
• Incremental DTs cannot depend on Full refresh DTs.

• SELECT *

  breaks on schema changes — use explicit column lists.
• Change tracking must stay enabled on base tables.
• Views cannot sit between two Dynamic Tables.

Streams and Tasks

CREATE OR REPLACE STREAM raw_stream ON TABLE raw_events;

CREATE OR REPLACE TASK process_events
    WAREHOUSE = transform_wh
    SCHEDULE = 'USING CRON 0 */1 * * * America/Los_Angeles'
    WHEN SYSTEM$STREAM_HAS_DATA('raw_stream')
    AS INSERT INTO cleaned_events SELECT ... FROM raw_stream;

-- Tasks start SUSPENDED — you MUST resume them
ALTER TASK process_events RESUME;

Imported: Cortex AI

Function Reference

AI_COMPLETE

AI_CLASSIFY

AI_FILTER

AI_EXTRACT

AI_SENTIMENT

AI_PARSE_DOCUMENT

AI_REDACT

Deprecated (do NOT use):

COMPLETE

CLASSIFY_TEXT

EXTRACT_ANSWER

PARSE_DOCUMENT

SUMMARIZE

TRANSLATE

SENTIMENT

EMBED_TEXT_768

TO_FILE — Common Error Source

Stage path and filename are SEPARATE arguments:

-- BAD: TO_FILE('@stage/file.pdf')
-- GOOD:
TO_FILE('@db.schema.mystage', 'invoice.pdf')

Use AI_CLASSIFY for Classification (Not AI_COMPLETE)

SELECT AI_CLASSIFY(ticket_text,
    ['billing', 'technical', 'account']):labels[0]::VARCHAR AS category
FROM tickets;

Cortex Agents

CREATE OR REPLACE AGENT my_db.my_schema.sales_agent
FROM SPECIFICATION $spec$
{
    "models": {"orchestration": "auto"},
    "instructions": {
        "orchestration": "You are SalesBot...",
        "response": "Be concise."
    },
    "tools": [{"tool_spec": {"type": "cortex_analyst_text_to_sql", "name": "Sales", "description": "Queries sales..."}}],
    "tool_resources": {"Sales": {"semantic_model_file": "@stage/model.yaml"}}
}
$spec$;

Agent rules:

• Use

  $spec$

  delimiter (not

  $$

  ).

• models

  must be an object, not an array.

• tool_resources

  is a separate top-level object, not nested inside tools.
• Do NOT include empty/null values in edit specs — clears existing values.
• Tool descriptions are the #1 quality factor.
• Never modify production agents directly — clone first.

Imported: Snowpark Python

from snowflake.snowpark import Session
import os

session = Session.builder.configs({
    "account": os.environ["SNOWFLAKE_ACCOUNT"],
    "user": os.environ["SNOWFLAKE_USER"],
    "password": os.environ["SNOWFLAKE_PASSWORD"],
    "role": "my_role", "warehouse": "my_wh",
    "database": "my_db", "schema": "my_schema"
}).create()

• Never hardcode credentials.
• DataFrames are lazy — executed on

  collect()

  /

  show()

  .
• Do NOT use

  collect()

  on large DataFrames — process server-side.
• Use vectorized UDFs (10-100x faster) for batch/ML workloads instead of scalar UDFs.

Imported: dbt on Snowflake

Dynamic table materialization (streaming/near-real-time marts):

{{ config(materialized='dynamic_table', snowflake_warehouse='transforming', target_lag='1 hour') }}

Incremental materialization (large fact tables):

{{ config(materialized='incremental', unique_key='event_id') }}

Snowflake-specific configs (combine with any materialization):

{{ config(transient=true, copy_grants=true, query_tag='team_daily') }}

• Do NOT use

  {{ this }}

  without

  {% if is_incremental() %}

  guard.
• Use

  dynamic_table

  materialization for streaming/near-real-time marts.

Imported: Performance

• Cluster keys: Only multi-TB tables, on WHERE/JOIN/GROUP BY columns.
• Search Optimization:

  ALTER TABLE t ADD SEARCH OPTIMIZATION ON EQUALITY(col);

• Warehouse sizing: Start X-Small, scale up.

  AUTO_SUSPEND = 60

  ,

  AUTO_RESUME = TRUE

  .
• Separate warehouses per workload.
• Estimate AI costs first:

  SELECT SUM(AI_COUNT_TOKENS('claude-4-sonnet', text)) FROM table;

Imported: Security

• Follow least-privilege RBAC. Use database roles for object-level grants.
• Audit ACCOUNTADMIN regularly:

  SHOW GRANTS OF ROLE ACCOUNTADMIN;

• Use network policies for IP allowlisting.
• Use masking policies for PII columns and row access policies for multi-tenant isolation.

Imported: Common Error Patterns

:variable_name

src:field::NUMBER(10,2)

ALTER TASK ... RESUME

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.