databricks-common-errors
Diagnose and fix Databricks common errors and exceptions. Use when encountering Databricks errors, debugging failed jobs, or troubleshooting cluster and notebook issues. Trigger with phrases like "databricks error", "fix databricks", "databricks not working", "debug databricks", "spark error".
80
77%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./plugins/saas-packs/databricks-pack/skills/databricks-common-errors/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 with strong completeness and trigger term coverage. Its main weakness is that the capability description could be more specific about what concrete actions it performs (e.g., parsing stack traces, resolving specific error codes, fixing cluster configuration issues). Overall it would perform well in a multi-skill selection scenario.
Suggestions
Add more specific concrete actions like 'parse stack traces', 'resolve dependency conflicts', 'fix cluster configuration errors', or 'troubleshoot OOM exceptions' to improve specificity.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | It names the domain (Databricks) and some actions ('diagnose and fix', 'debugging failed jobs', 'troubleshooting cluster and notebook issues'), but doesn't list specific concrete actions like parsing error logs, resolving dependency conflicts, or fixing specific error types. | 2 / 3 |
Completeness | Clearly answers both 'what' (diagnose and fix Databricks common errors and exceptions) and 'when' (encountering Databricks errors, debugging failed jobs, troubleshooting cluster and notebook issues) with explicit trigger phrases. | 3 / 3 |
Trigger Term Quality | Includes a strong set of natural trigger terms users would actually say: 'databricks error', 'fix databricks', 'databricks not working', 'debug databricks', 'spark error'. These cover common user phrasings well. | 3 / 3 |
Distinctiveness Conflict Risk | Databricks is a specific enough platform that this skill is unlikely to conflict with other skills. The trigger terms are clearly scoped to Databricks and Spark errors, creating a distinct niche. | 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 solid, actionable troubleshooting reference with real executable code for each error pattern. Its main weakness is length — it could benefit from splitting detailed error patterns into a separate reference file while keeping the SKILL.md as a concise lookup table. The workflow also lacks explicit verification steps after applying fixes, which is important for a debugging/troubleshooting skill.
Suggestions
Add explicit verification commands after each fix (e.g., 'Verify: run `databricks clusters get --cluster-id $CID | jq .state` and confirm RUNNING') to close the feedback loop.
Consider moving the detailed error patterns into a separate ERRORS_REFERENCE.md and keeping SKILL.md as a concise diagnostic flowchart with the summary table and links to each pattern.
Remove the Output section ('Error identified and categorized / Fix applied / Resolution verified') which is vague and adds no actionable value.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is mostly efficient with real code examples and minimal fluff, but it's quite long. Some sections like the Overview and Output sections add little value. The error messages shown before each fix are helpful for pattern matching though, so most content earns its place. | 2 / 3 |
Actionability | Every error pattern includes fully executable code — real SDK calls, SQL statements, and CLI commands that are copy-paste ready. The merge_with_retry function, permission grants, and diagnostic commands are all concrete and immediately usable. | 3 / 3 |
Workflow Clarity | Step 1 (identify) and Step 2 (match and fix) provide a basic workflow, but there's no explicit validation/verification step after applying fixes. The Output section mentions 'Resolution verified' but provides no guidance on how to verify. For a troubleshooting skill involving potentially destructive operations (schema changes, permission grants), the lack of verification checkpoints is a gap. | 2 / 3 |
Progressive Disclosure | The skill references external resources (databricks-rate-limits skill, databricks-debug-bundle, external docs) which is good, but the main file is quite long (~200 lines of detailed error patterns). Some error patterns could be split into separate reference files with the SKILL.md serving as a lighter index/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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