databricks-multi-env-setup
Configure Databricks across development, staging, and production environments. Use when setting up multi-environment deployments, configuring per-environment secrets, or implementing environment-specific Databricks configurations. Trigger with phrases like "databricks environments", "databricks staging", "databricks dev prod", "databricks environment setup", "databricks config by env".
80
77%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Risky
Do not use without reviewing
Optimize this skill with Tessl
npx tessl skill review --optimize ./plugins/saas-packs/databricks-pack/skills/databricks-multi-env-setup/SKILL.mdQuality
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 communicates its purpose and when to use it. It excels in completeness with explicit 'Use when' and 'Trigger with' clauses, and has strong distinctiveness. The main area for improvement is specificity — the concrete actions could be more granular to better differentiate the range of tasks this skill handles.
Suggestions
Add more specific concrete actions such as 'create workspace configurations', 'manage environment-specific cluster policies', 'configure secret scopes per environment', or 'set up environment variable mappings' to improve specificity.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | It names the domain (Databricks multi-environment configuration) and mentions some actions like 'setting up multi-environment deployments', 'configuring per-environment secrets', and 'implementing environment-specific configurations', but these are somewhat high-level and not as concrete as listing specific discrete actions (e.g., 'create workspace configs', 'manage cluster policies per env', 'set up secret scopes'). | 2 / 3 |
Completeness | Clearly answers both 'what' (configure Databricks across dev/staging/prod environments, per-environment secrets, environment-specific configurations) and 'when' (explicit 'Use when' clause plus a 'Trigger with phrases' section providing concrete trigger terms). | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural trigger terms including 'databricks environments', 'databricks staging', 'databricks dev prod', 'databricks environment setup', 'databricks config by env', plus natural phrases like 'multi-environment deployments' and 'per-environment secrets'. These are terms users would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive — the combination of 'Databricks' with 'multi-environment' configuration creates a clear niche. It's unlikely to conflict with a general Databricks skill or a general environment configuration skill due to the specific intersection of both domains. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
64%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 skill with excellent concrete examples covering CLI profiles, Asset Bundles, secrets, Python config, CI/CD, and Terraform. Its main weaknesses are length (the Python config class and Terraform block could be referenced rather than inline) and the lack of explicit validation checkpoints between steps in what is a complex, multi-step, multi-environment setup involving production credentials.
Suggestions
Add explicit validation checkpoints after key steps (e.g., 'Verify CLI profiles work before proceeding' with the verification script moved inline after Step 1, and 'Validate bundle config: databricks bundle validate -t staging' after Step 2).
Move the full Python config class (Step 4) and Terraform examples (Step 6) into separate referenced files to keep SKILL.md as a concise overview with quick-start patterns.
Trim the prerequisites section — Claude doesn't need to be told what a CI/CD pipeline or service principal is.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is fairly comprehensive but includes some content that could be tightened — the Python config class (Step 4) is verbose with boilerplate Claude could generate, and the prerequisites section states obvious requirements. However, most content is domain-specific configuration that earns its place. | 2 / 3 |
Actionability | Every step includes fully executable, copy-paste-ready code: CLI config files, YAML bundle definitions, bash scripts, Python classes, GitHub Actions workflows, and Terraform HCL. Specific commands and concrete examples are provided throughout. | 3 / 3 |
Workflow Clarity | Steps are clearly numbered and sequenced, but there are no explicit validation checkpoints between steps. The 'Quick Environment Verification' example is buried at the end rather than integrated as a checkpoint after Step 1. For a multi-environment setup involving secrets and production deployments, missing validation/feedback loops between steps is a gap. | 2 / 3 |
Progressive Disclosure | The content is well-structured with clear headers and a table of contents-like flow, but it's a long monolithic file (~200 lines of config/code). Steps 4 and 6 could be split into referenced files. The 'Next Steps' reference to 'databricks-deploy-integration' is good, but more content should be offloaded to keep the SKILL.md as an overview. | 2 / 3 |
Total | 9 / 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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