Content
65%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill is highly actionable with complete, executable collection code organized into clear numbered steps. It loses points for missing validation around a destructive packaging step and for keeping a large script inline instead of splitting it into a reference file.
Suggestions
Add a validation checkpoint before "rm -rf \"$BUNDLE_DIR\"", e.g. verify the tar.gz with "tar -tzf \"$BUNDLE_DIR.tar.gz\" >/dev/null" and only remove the source directory on success.
Move the bash collection script into scripts/databricks-debug-bundle.sh and reference it from SKILL.md so the body stays a lean overview rather than an inline ~200-line script.
Drop the separate Output section or fold it into Step 7's final echo to remove the duplicated file-list content.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is mostly efficient concrete code with little concept-padding, but the ~250-line length includes redundancy (the Output section restates the file list produced in Step 7) and a large deprecation block that could be tightened, matching the level-2 anchor. | 2 / 3 |
Actionability | Seven steps ship fully executable bash/python commands with concrete databricks CLI calls, jq, and tar usage that are copy-paste ready, matching the level-3 anchor. | 3 / 3 |
Workflow Clarity | Steps are clearly sequenced, but there are no validation checkpoints and the destructive "rm -rf \"$BUNDLE_DIR\"" runs without verifying the tar.gz was created, so per the batch/destructive guideline workflow clarity is capped at 2. | 2 / 3 |
Progressive Disclosure | Sections are well-organized, but the entire multi-step bash script is inlined rather than offloaded to a scripts/ file (no bundle files exist), fitting the level-2 anchor where content that should be separate is kept inline. | 2 / 3 |
Total | 9 / 12 Passed |