snowflake-development

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, performance tuning, and security hardening.

Quality

67%

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Securityby

Passed

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tessl review fix ./skills/snowflake-development/SKILL.md

Quality

Content

68%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

This is a highly actionable and reasonably concise Snowflake reference that excels at providing executable code examples and highlighting common pitfalls (colon prefix, TO_FILE args, deprecated functions). Its main weaknesses are the monolithic structure — covering 8+ major topics in a single file without any external references — and the lack of explicit validation/feedback loops for pipeline and database operations.

Suggestions

Split into separate referenced files (e.g., CORTEX_AI.md, PIPELINES.md, PERFORMANCE.md) and keep SKILL.md as a concise overview with signposted links to each topic.

Add explicit validation steps for pipeline workflows, e.g., 'After creating a Dynamic Table, verify refresh status: SELECT * FROM TABLE(INFORMATION_SCHEMA.DYNAMIC_TABLE_REFRESH_HISTORY(...))' and 'After resuming a task, confirm execution: SELECT * FROM TABLE(INFORMATION_SCHEMA.TASK_HISTORY(...))'.

Remove the 'When to Use' section — it restates the skill description and Claude can infer applicability from the content itself.

Dimension	Reasoning	Score
Conciseness	The content is largely efficient, providing Snowflake-specific knowledge Claude wouldn't inherently have (colon prefix rule, TO_FILE argument separation, deprecated functions list). Minor verbosity in the 'When to Use' section and a few explanatory asides (e.g., 'Snowflake's columnar storage scans only referenced columns') that Claude likely knows, but overall well-trimmed for the breadth covered.	4 / 5
Actionability	Nearly every section includes executable, copy-paste-ready SQL or Python code covering common cases. The BAD/GOOD pattern comparisons for stored procedures and TO_FILE are particularly actionable, and the error patterns table maps directly from symptom to fix.	5 / 5
Workflow Clarity	The Streams+Tasks section shows a clear sequence including the critical 'Tasks start SUSPENDED — you MUST resume them' checkpoint. However, for a skill covering data pipelines and database operations (destructive/batch contexts), there are no explicit validation/feedback loops — no 'verify the Dynamic Table is refreshing correctly' or 'check stream offset after task execution' steps. Per rubric rules, missing feedback loops for database operations caps this at 3.	3 / 5
Progressive Disclosure	This is a very large monolithic file (~250+ lines) covering SQL, pipelines, Cortex AI, Cortex Agents, Snowpark, dbt, performance, and security — all inlined with no references to separate files. Given the breadth, sections like Cortex AI function reference, Cortex Agents spec format, and the error patterns table would benefit from being split into separate referenced files. No bundle files exist to support this.	2 / 5
	Total	14 / 20 Passed

Description

66%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description effectively enumerates a broad range of Snowflake-specific capabilities with good domain terminology that aids discoverability. Its main weakness is the absence of an explicit 'Use when...' clause, which limits Claude's ability to know precisely when to select this skill. The description also leans on listing topics rather than describing concrete actions with verbs.

Suggestions

Add an explicit 'Use when...' clause with trigger phrases like 'Use when the user asks about Snowflake queries, data pipelines, Cortex AI, Snowpark development, or Snowflake performance issues.'

Replace the passive 'covering' framing with active verbs describing concrete actions, e.g., 'Writes optimized Snowflake SQL, designs data pipelines using Dynamic Tables and Streams, configures Cortex AI functions, tunes query performance.'

Dimension	Reasoning	Score
Specificity	Lists several specific capabilities including Dynamic Tables, Streams, Tasks, Snowpipe, Cortex AI functions, Cortex Agents, Snowpark Python, dbt integration, performance tuning, and security hardening. However, the actions are mostly domain labels rather than concrete verbs describing what the skill does (e.g., 'covering' is vague compared to 'creates', 'optimizes', 'configures').	4 / 5
Completeness	The 'what' is reasonably clear — it covers Snowflake development across multiple domains. However, there is no explicit 'when' clause (no 'Use when...' or equivalent trigger guidance), which per the rubric caps this dimension at 3.	3 / 5
Trigger Term Quality	Includes many natural Snowflake-specific keywords users would search for: Snowflake, SQL, Dynamic Tables, Streams, Tasks, Snowpipe, Cortex AI, Snowpark, dbt, performance tuning. Missing some common user phrases like 'query optimization', 'warehouse sizing', 'data warehouse', or 'ELT'.	4 / 5
Distinctiveness Conflict Risk	The Snowflake-specific terminology (Dynamic Tables, Cortex AI, Snowpark, Snowpipe) makes this quite distinct from generic SQL or data engineering skills. Minor overlap risk exists with general SQL skills, dbt skills, or generic data pipeline skills.	4 / 5
	Total	15 / 20 Passed

Validation

90%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 10 / 11 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	10 / 11 Passed

Repository: administrakt0r/AI-Agents-Safe-Coding-Skills
Path: skills/snowflake-development/SKILL.md
Commit: 57c135b

Reviewed: 17 hours ago

Table of Contents

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