databricks-ci-integration
Configure Databricks CI/CD integration with GitHub Actions and Asset Bundles. Use when setting up automated testing, configuring CI pipelines, or integrating Databricks deployments into your build process. Trigger with phrases like "databricks CI", "databricks GitHub Actions", "databricks automated tests", "CI databricks", "databricks pipeline".
85
83%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Advisory
Suggest reviewing before use
Quality
Discovery
89%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a well-structured skill description that clearly identifies its niche (Databricks CI/CD with GitHub Actions) and provides explicit trigger guidance. The main weakness is that the capability description could be more granular with specific concrete actions. Minor issue: uses second person 'your' in 'your build process', though this is a minor infraction.
Suggestions
Add more specific concrete actions such as 'create GitHub Actions workflow YAML, configure Asset Bundle deployment targets, set up test stages, manage environment secrets' to improve specificity.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (Databricks CI/CD with GitHub Actions and Asset Bundles) and some actions (automated testing, configuring CI pipelines, integrating deployments), but doesn't list multiple concrete granular actions like 'create workflow YAML files, configure bundle deployment targets, set up test stages'. | 2 / 3 |
Completeness | Clearly answers both 'what' (configure Databricks CI/CD integration with GitHub Actions and Asset Bundles) and 'when' (setting up automated testing, configuring CI pipelines, integrating deployments) with explicit trigger phrases listed. | 3 / 3 |
Trigger Term Quality | Includes a strong set of natural trigger terms users would actually say: 'databricks CI', 'databricks GitHub Actions', 'databricks automated tests', 'CI databricks', 'databricks pipeline'. These cover common variations and are realistic user phrases. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with the specific combination of Databricks + CI/CD + GitHub Actions + Asset Bundles. The trigger terms are domain-specific enough to avoid conflicts with generic CI/CD skills or generic Databricks skills. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
77%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a strong, highly actionable skill with complete, executable GitHub Actions workflows and PySpark test examples covering the full CI/CD lifecycle for Databricks. The workflow sequencing is excellent with proper validation checkpoints and error recovery guidance. The main weakness is that the content is somewhat lengthy for a single SKILL.md—the OIDC section and detailed unit test examples could be split into referenced files to improve progressive disclosure and reduce token cost.
Suggestions
Consider moving the OIDC authentication section (Step 4) and detailed unit test examples (Step 2) into separate referenced files to reduce the main SKILL.md size and improve progressive disclosure.
Remove the Overview paragraph since the Prerequisites and Step 1 already convey the scope; the title and section headers are sufficient orientation.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is mostly efficient with concrete YAML and Python examples, but includes some unnecessary elements like the Overview paragraph restating what the title already conveys, and the error handling table contains some obvious entries (e.g., 'PySpark import error' → 'Add to pip install step'). The env block repetition across steps adds bulk but is arguably necessary for copy-paste readiness. | 2 / 3 |
Actionability | Fully executable GitHub Actions YAML workflows, complete pytest fixtures with PySpark, concrete CLI commands, and real configuration snippets. The code is copy-paste ready with specific action versions, environment variable names, and command flags. | 3 / 3 |
Workflow Clarity | Clear 4-step sequence from PR validation → unit tests → staging deploy with integration tests → production deploy on merge, with explicit validation checkpoints (bundle validate before deploy, integration tests before merge, smoke tests after prod deploy). Includes concurrency control and approval gates as safeguards. | 3 / 3 |
Progressive Disclosure | The content is well-structured with clear sections, but it's quite long (~180 lines of substantive content) and could benefit from splitting the unit test examples and OIDC setup into separate referenced files. The Resources section provides good external links, and there's a reference to 'databricks-deploy-integration', but the inline content is heavy for a single SKILL.md. | 2 / 3 |
Total | 10 / 12 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 — 9 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
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 | 9 / 11 Passed | |
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