CtrlK
BlogDocsLog inGet started
Tessl Logo

databricks-sdk-patterns

Apply production-ready Databricks SDK patterns for Python and REST API. Use when implementing Databricks integrations, refactoring SDK usage, or establishing team coding standards for Databricks. Trigger with phrases like "databricks SDK patterns", "databricks best practices", "databricks code patterns", "idiomatic databricks".

67

Quality

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Passed

No known issues

SKILL.md
Quality
Evals
Security

Quality

Content

65%

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

The content is highly actionable with executable, SDK-accurate code and a clear step sequence, but it is verbose for a single skill, lacks validation feedback loops for risky operations, and fails to surface its existing reference file. Tightening the body and linking the implementation guide would raise the weaker dimensions.

Suggestions

Link references/implementation-guide.md from the body (e.g. under a '## Advanced' or '## Full implementation' section) so the detailed material is clearly signaled rather than inline-only.

Add explicit validation/checkpoint steps for cluster lifecycle and job creation workflows (e.g. verify cluster state, confirm job creation, retry-on-failure) to introduce feedback loops for these destructive/batch operations.

Trim redundancy — for example, consolidate the inline error-handling code with the Error Handling table, and move the peripheral Examples (health check, multi-workspace inventory) into the reference file — to improve token efficiency.

DimensionReasoningScore

Conciseness

The body is mostly lean code with brief lead-ins and assumes Claude's competence, but it runs long (~280 lines) with five full code blocks plus overlapping error-handling material in both code and a table, so it could be tightened.

2 / 3

Actionability

Each step provides fully executable, copy-paste-ready Python using real SDK classes and API shapes, with concrete usage examples — matching the 'fully executable code' anchor.

3 / 3

Workflow Clarity

Steps are clearly sequenced (Step 1–5), but operations involving cluster lifecycle and job creation lack explicit validation checkpoints or fix-and-retry feedback loops, capping workflow clarity at 2 per the destructive/batch-operations guideline.

2 / 3

Progressive Disclosure

A bundle reference (references/implementation-guide.md) exists but is never linked or signaled from the body, which is otherwise a monolithic inline wall of code; the reference is present but not clearly surfaced for navigation.

2 / 3

Total

9

/

12

Passed

Description

100%

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 is strong across all dimensions: it states concrete capabilities, provides natural trigger phrases, answers both what and when, and occupies a distinctive niche. No significant improvements needed.

DimensionReasoningScore

Specificity

Names multiple concrete actions such as singleton client initialization, typed error handling, cluster lifecycle management, type-safe job construction, and pagination, matching the 'lists multiple specific concrete actions' anchor.

3 / 3

Completeness

Explicitly answers both what ('Apply production-ready Databricks SDK patterns for Python and REST API') and when ('Use when implementing Databricks integrations, refactoring SDK usage, or establishing team coding standards') with explicit triggers.

3 / 3

Trigger Term Quality

Includes natural phrases a user would say — 'databricks SDK patterns', 'databricks best practices', 'databricks code patterns', 'idiomatic databricks' — giving good coverage of common variations.

3 / 3

Distinctiveness Conflict Risk

Targets a clear niche (Databricks SDK) with distinct, domain-specific triggers, making it unlikely to fire for unrelated skills.

3 / 3

Total

12

/

12

Passed

Validation

87%

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

Validation14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

allowed_tools_field

'allowed-tools' contains unusual tool name(s)

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

14

/

16

Passed

Repository
jeremylongshore/claude-code-plugins-plus-skills
Reviewed

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.