Content
86%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A well-engineered skill body: an index-style overview that keeps only decision logic, traps, and error mappings inline while pushing per-feature, per-language detail to 37 real, well-named reference files. Commands and syntax are executable, and risky operations (full refresh, DLT migration) carry explicit guardrails. Minor room to tighten a few wordy passages and inline a clearer end-to-end run sequence.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense and almost entirely product-specific knowledge Claude cannot know (SDP naming rules, `CREATE OR REFRESH` vs `CREATE OR REPLACE`, `dbfs:` prefix requirement, `__START_AT__`/`__END_AT__` columns, type restrictions on cluster keys) with no filler or textbook explanation. Not 5 — a few passages could be trimmed, e.g. the "Gold layer: preserve key business dimensions" paragraph is wordy and the language-selection table repeats its Python signals across two rows. | 4 / 5 |
Actionability | Concrete, copy-paste-ready commands and syntax throughout: `databricks pipelines init --output-dir . --config-file init-config.json`, the full bundle validate/deploy/run/status command block, `databricks workspace import-dir ... --overwrite`, exact decorators (`@dp.append_flow(once=True)`), exact SQL forms (`CREATE OR REFRESH STREAMING TABLE`, `AUTO CDC INTO ... FROM STREAM`), and an error→fix table covering the common failure cases. | 5 / 5 |
Workflow Clarity | A coherent path exists: parent-skill bootstrap → decision tree → language selection → workflow choice (A/B/C) → scaffold → deploy/upload → run → poll, with explicit checkpoints (`bundle validate` before deploy, "Always poll the update", full-refresh data-loss warning gated on explicit user approval, error-extraction mapping). Not 5 — the body presents these as linked sections rather than a single end-to-end numbered sequence with feedback loops, delegating step-level detail to the reference files. The destructive-operation validation is present, so the workflow cap at 3 does not apply. | 4 / 5 |
Progressive Disclosure | Excellent structure against the actual bundle: all 37 referenced files in `references/` exist on disk (verified, including the `#cluster-key-data-types` anchor in performance.md), references are one level deep, every API-table row maps a feature to the right per-(feature, language) file, workflows A/B/C each link to their own step-by-step file, and a Reference Index closes the body. The inline tables function as a navigation index rather than bulk content, and detail is appropriately split out. | 5 / 5 |
Total | 18 / 20 Passed |