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
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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 provides explicit trigger guidance. Its main weakness is that the specific capabilities listed are somewhat high-level—it could benefit from more concrete action verbs describing exactly what configuration tasks it performs. The explicit trigger phrases and 'Use when' clause are strong additions that make skill selection straightforward.
Suggestions
Add more concrete, specific actions beyond 'configure'—e.g., 'generate workspace configuration files', 'set up environment-specific cluster policies', 'manage per-environment job schedules and permissions'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description 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 general and don't list truly concrete, distinct actions (e.g., 'create workspace configs', 'manage cluster policies per env', 'set up CI/CD pipelines'). | 2 / 3 |
Completeness | The description clearly answers both 'what' (configure Databricks across dev/staging/prod environments) and 'when' (explicit 'Use when...' clause with specific scenarios, plus a 'Trigger with phrases like...' section providing additional guidance). | 3 / 3 |
Trigger Term Quality | The description includes a rich set of natural trigger terms that users would actually say: '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'. Good coverage of variations. | 3 / 3 |
Distinctiveness Conflict Risk | The description is highly specific to Databricks multi-environment configuration, combining a specific platform (Databricks) with a specific concern (environment-based configuration). This is unlikely to conflict with general Databricks skills or general environment configuration skills. | 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 (could benefit from splitting detailed configs into referenced files) and missing validation checkpoints between steps — particularly after secret creation and bundle deployment. The error handling table is a nice touch but doesn't substitute for inline validation steps.
Suggestions
Integrate validation checkpoints directly into the workflow — e.g., after Step 1 run the environment verification script, after Step 3 verify secrets with `databricks secrets list-scopes`, after Step 5 verify deployment with `databricks bundle validate -t staging`.
Move the full Python config class (Step 4) and Terraform (Step 6) into separate referenced files to reduce the main skill's token footprint and improve progressive disclosure.
Add a feedback loop for bundle deployment: validate -> deploy -> verify -> rollback if needed, especially for the production deployment step.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is fairly comprehensive but includes some unnecessary verbosity — the Python config class with full dataclass definition and the Terraform section add bulk that could be trimmed or referenced externally. The environment strategy table and error handling table are efficient, but overall the skill is ~200 lines when it could be tighter. | 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 (1-6), but there are no explicit validation checkpoints between steps. The 'Quick Environment Verification' script is in the Examples section rather than integrated as a validation step after Step 1. There's no feedback loop for verifying bundle deployment success or secret scope creation. | 2 / 3 |
Progressive Disclosure | The content is a long monolithic document with all details inline. The Terraform section is marked optional but still fully inline. The Python config class, CI/CD workflow, and Terraform could be split into referenced files. The Resources section links to external docs, but there's no bundle structure to offload detailed content. | 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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