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: concrete schemas, executable decision logic, exact output templates, and exemplary progressive disclosure via one verified reference file. The only improvements are trimming the Overview's slight redundancy with the Step 1 table and adding explicit handling of missing/malformed engine artifacts in the main flow.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Largely efficient — dense tables, executable code, and it assumes Claude already knows the engines. The Overview's description of each engine's output artifact ("dbt writes run_results.json", "Great Expectations returns a JSON result...", "Soda emits a per-check pass/fail summary") partially duplicates the Step 1 table and could be trimmed. | 4 / 5 |
Actionability | Provides a concrete unified record JSON with a field-by-field source table, a copy-paste-ready Python gate_decision function, exact markdown and JSON artifact templates, and links a real full end-to-end implementation (references/worked-example.md). The decision logic is executable despite the 'Pseudocode:' label, and the common cases are covered. | 5 / 5 |
Workflow Clarity | Steps 1-4 are clearly sequenced (identify sources, define unified record, apply decision rule, emit artifact) and the decision rule is itself an explicit validation gate, with the strict-mode variant documented. The gap: no in-body handling of missing or malformed engine artifacts (what happens if an engine didn't run), which is deferred to the worked example. | 4 / 5 |
Progressive Disclosure | Clean overview/when-to-use/steps/worked-example/references structure with a single one-level-deep reference (references/worked-example.md) that exists and is well-signaled. The full collector implementation is appropriately split out of the main flow, making navigation easy. | 5 / 5 |
Total | 18 / 20 Passed |