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CD azure-databricks

Expert knowledge for Azure Databricks development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when working with Unity Catalog, Delta Lake/Lakehouse, Lakeflow pipelines, ML/LLM serving, or external connectors, and other Azure Databricks related development tasks. Not for Azure Synapse Analytics (use azure-synapse-analytics), Azure HDInsight (use azure-hdinsight), Azure Machine Learning (use azure-machine-learning), Azure Data Factory (use azure-data-factory).

MicrosoftDocs/Agent-Skills Agent Skills author: MicrosoftDocs CC-BY-4.0 8 files body ≈ 13 682 tokens Open the sourcegithub.com↗ analyzed 6 d ago

Expert knowledge for Azure Databricks development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas…

As a process D 43/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, execution cost

IntegrationAzureSalesforceTerraformInfrastructuretype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
C
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
71
Run on models
none yet
Process rating
D
43/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 8. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 13682 tokens (recommended < 5000); move details to references/
  • note edit-residue the text marks something as outdated (lines 194, 195, 221, 262, 322, 344): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 43/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 19 mutating operations with no state check
  • 40Execution cost. Instruction body is 13682 tokens: crowds the task out of the window
  • 50Steps. 2 steps
  • 50When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (web, python, node) that frontmatter does not declare
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +3No numbered steps or checklist
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +4Description says when NOT to use the skill
  • +3Description length 604: enough signal without eating the budget
  • +4Structure: 5 headings

Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.