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databricks-python-sdk

Databricks development guidance including Python SDK, Databricks Connect, CLI, and REST API. Use when working with databricks-sdk, databricks-connect, or Databricks APIs.

53

Quality

67%

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tessl review fix ./plugins/databricks/claude/skills/databricks-python-sdk/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

57%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.

The body is strong on concrete, copy-paste-ready SDK examples and covers the major Databricks API surfaces well, but it is inflated by duplicated link tables and repeated URL-pattern explanations, inlines an API reference that belongs in the existing references file, and includes a dangling 'examples/' link. Destructive operations lack validation guidance.

Suggestions

Delete the redundant 'Quick Reference Links' table and merge 'When Uncertain' into 'SDK Documentation Architecture' — both repeat the same doc-URL pattern already shown in every section header.

Move the per-service inline API examples out of SKILL.md into the referenced 'examples/' files (or remove the dangling 'examples/' reference, since that directory does not exist in the bundle), keeping only the highest-value snippets (setup, async wrapping, waiting patterns) inline.

Resolve the version contradiction (frontmatter 'Requires databricks CLI (>= v1.0.0)' vs body '0.278.0 or higher') into a single statement, and add a validation/confirmation step before destructive operations like clusters.delete and permanent_delete.

DimensionReasoningScore

Conciseness

Mostly efficient — the body is dominated by dense, useful code examples — but contains unnecessary duplication: the doc-URL pattern is explained twice ('SDK Documentation Architecture' and 'When Uncertain'), the ~17-row 'Quick Reference Links' table repeats the per-section Doc URLs verbatim, and the CLI version requirement is stated twice with contradictory values (frontmatter '>= v1.0.0' vs body '0.278.0 or higher'). This fits anchor 3 (mostly efficient, some unnecessary content that could be tightened) rather than anchor 2, since beyond the redundant sections the tokens are largely reference material Claude cannot know.

3 / 5

Actionability

Nearly all guidance is concrete, executable code with real method calls and parameters (e.g. w.clusters.create_and_wait(...), asyncio.to_thread patterns, exact CLI commands), fitting anchor 4/5. It falls short of anchor 5 due to minor gaps: some snippets omit needed imports (StatementState, timedelta used before their import sections), the Pagination example imports ListJobsRequest but never uses it, and the 'examples/' directory it points to does not exist in the bundle.

4 / 5

Workflow Clarity

There is a clear setup sequence, explicit wrong/right async patterns, and a 'When Uncertain' verify-before-answering procedure, but the skill documents destructive and long-running operations (w.clusters.delete, stop pipeline, permanent_delete) with no validation or confirmation checkpoints. Per the rubric's cap, missing validation for destructive/batch operations caps workflow_clarity at 3; the otherwise decent sequencing prevents scoring lower.

3 / 5

Progressive Disclosure

There is a clearly signaled one-level-deep reference (references/doc-index.md, which exists and is well-organized), but the body inlines ~300 lines of per-service API reference that overlaps the separate file's role, and it points readers to an 'examples/' directory that is absent from the bundle — a broken navigation path. This matches anchor 3 (references present but content that should be separate is inline, plus organization gaps) better than anchor 4.

3 / 5

Total

13

/

20

Passed

Description

65%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.

The description has a clear, explicit trigger clause with good package-name keywords and a well-scoped Databricks niche, but the 'what' is generic ('development guidance') with no concrete actions. Tightening it with specific capabilities would lift specificity and completeness.

Suggestions

Replace 'Databricks development guidance' with concrete actions, e.g. 'Manage Databricks clusters, jobs, SQL warehouses, and Unity Catalog resources via the Python SDK' — this raises specificity from naming components to naming actions.

Add missing natural trigger terms to the 'when' clause such as 'Databricks CLI', 'Databricks Connect', or 'Unity Catalog' so users phrasing requests around those surfaces also match.

Disambiguate from sibling skills by scoping the 'when' clause (e.g. exclude the vector-search/serving surfaces covered by dedicated sibling skills).

DimensionReasoningScore

Specificity

The description names the domain and enumerates tool surfaces ("including Python SDK, Databricks Connect, CLI, and REST API") but contains no concrete actions — "Databricks development guidance" is generic. It matches anchor 2 (names the domain, actions minimal/generic) better than anchor 3, which requires 1-2 concrete actions like 'create clusters' or 'run jobs'; the enumerated items are components, not actions.

2 / 5

Completeness

Both 'what' ("Databricks development guidance including Python SDK, Databricks Connect, CLI, and REST API") and 'when' (explicit "Use when working with..." clause) are present, matching anchor 4. Not anchor 5 because the 'what' leans on the vague word 'guidance' rather than concrete capability phrases; not anchor 3 because the 'when' clause is explicit with concrete triggers, not merely implied.

4 / 5

Trigger Term Quality

"Use when working with databricks-sdk, databricks-connect, or Databricks APIs" gives good package-name keywords users would naturally say, matching anchor 4. It falls short of anchor 5's comprehensive synonym coverage — missing natural variations like 'Databricks CLI', 'Unity Catalog', or 'run Spark against Databricks'.

4 / 5

Distinctiveness Conflict Risk

Databricks-specific package names make this mostly distinct from unrelated skills, but the broad trigger "Databricks APIs" overlaps with the closely related sibling skills this bundle declares (databricks-unity-catalog, databricks-model-serving, databricks-vector-search) — a minor overlap risk fitting anchor 4.

4 / 5

Total

14

/

20

Passed

Validation

81%

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

Validation — 13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (634 lines); consider splitting into references/ and linking

Warning

frontmatter_unknown_keys

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

Warning

relative_links

Relative link issues: 1 missing, 2 suspicious

Warning

Total

13

/

16

Passed

Repository

databricks/databricks-agent-skills

Path

plugins/databricks/claude/skills/databricks-python-sdk/SKILL.md

Reviewed

Table of Contents

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