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snowflake-development

Use when writing Snowflake SQL, building data pipelines with Dynamic Tables or Streams/Tasks, using Cortex AI functions, creating Cortex Agents, writing Snowpark Python, configuring dbt for Snowflake, or troubleshooting Snowflake errors.

71

Quality

86%

Does it follow best practices?

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SecuritybySnyk

The risk profile of this skill

SKILL.md
Quality
Evals
Security

Quality

Content

80%Weight 40%Scale 1-5

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

A high-quality, actionable body with executable code across the full Snowflake surface and excellent progressive disclosure to real reference files. The main weakness is workflow clarity: the primary build workflow is a batch operation presented without explicit validation checkpoints between steps.

Suggestions

Add inter-step validation checkpoints to Workflow 1 (e.g., 'Verify the cleaned Dynamic Table refreshed successfully before building the downstream DT' and 'Confirm grants applied with SHOW GRANTS TO ROLE analyst_role') so the batch pipeline build satisfies the validation cap.

Add a brief verification/feedback step to Workflow 1's grant step (run the generated RBAC, then re-audit) to give the build flow an explicit validate→fix→retry loop analogous to Workflow 3.

Consider adding a 'verify before scaling' checkpoint to Workflow 2's enrichment-DT step beyond the existing cost-sampling caution, since AI classification on full tables is a costly batch operation.

DimensionReasoningScore

Conciseness

The body is dense and assumes Claude's competence (no explanations of what Snowflake/SQL/dbt are), but a few minor trim targets remain — the 'Originally contributed by' attribution line and brief rationale clauses like 'Snowflake's columnar storage scans only referenced columns'.

4 / 5

Actionability

Copy-paste-ready, executable code and commands span every domain (MERGE, Dynamic Table/Stream/Task DDL, Snowpark session, dbt config, Cortex calls) plus a runnable helper script and concrete debug queries, fully covering the common cases per the score-5 anchor.

5 / 5

Workflow Clarity

The three Practical Workflows are well sequenced and Workflow 3 is a genuine validate/debug loop, but Workflow 1 (a batch reporting-pipeline build) lists steps with no inter-step validation checkpoints, and the rubric caps batch workflows lacking validation at 3.

3 / 5

Progressive Disclosure

SKILL.md is a clear overview that points to three well-signaled, one-level-deep references (all verified to exist as substantive 155–281 line documents) via blockquote pointers and a Reference Documentation navigation table, matching the score-5 anchor.

5 / 5

Total

17

/

20

Passed

Description

92%Weight 40%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.

A strong, well-scoped description that explicitly answers both what the skill does and when to use it, with concrete Snowflake-specific trigger terms. The only minor gap is the absence of some synonyms and file extensions that would round out trigger-term coverage.

DimensionReasoningScore

Specificity

The description lists seven concrete actions — 'writing Snowflake SQL', 'building data pipelines with Dynamic Tables or Streams/Tasks', 'using Cortex AI functions', 'creating Cortex Agents', 'writing Snowpark Python', 'configuring dbt for Snowflake', 'troubleshooting Snowflake errors' — giving comprehensive coverage of the skill's scope, matching the score-5 anchor.

5 / 5

Completeness

An explicit 'Use when…' clause provides the 'when', and the enumerated action list provides a concrete 'what', clearly and explicitly answering both halves, which satisfies the score-5 anchor.

5 / 5

Trigger Term Quality

It surfaces natural terms users would actually say (Snowflake SQL, Dynamic Tables, Streams/Tasks, Cortex AI, Snowpark, dbt for Snowflake), but omits common synonyms and file extensions (no 'stored procedures', 'MERGE/upsert', '.sql/.py'), so it sits just below the fully-comprehensive score-5 anchor.

4 / 5

Distinctiveness Conflict Risk

Nearly every clause is scoped to Snowflake-branded mechanisms (Dynamic Tables, Streams/Tasks, Cortex AI, Snowpark), carving a clear niche with minimal conflict risk against adjacent dbt or general data-engineering skills.

5 / 5

Total

19

/

20

Passed

Validation

100%

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

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
alirezarezvani/claude-skills
Reviewed

Table of Contents

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