Content
73%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.
The body excels at workflow structure: a fully sequenced profile → plan → transform → verify pipeline with explicit feedback loops, quantitative thresholds, evidence checklists, and gating cautions, plus a ready-to-run scanner command. It is held back by noticeable redundancy (the scan-denial contingency stated four times, duplicated normalization rules) and a few non-executable SQL references. Tightening the repetition and moving type-specific detail into reference files would make an already strong skill leaner.
Suggestions
State the scan-denial contingency once in a single 'If scan approval is denied' note instead of repeating it in Steps 1, 1.5, 3, and 3.5.
Extract the duplicated normalization rules ('Only trim whitespace... PRESERVE CASE. DO NOT perform case conversions') into one shared section instead of repeating them verbatim in the Array and STRUCT sections.
Replace the non-BigQuery ARRAY_FILTER/ARRAY_TRANSFORM lambda syntax with the valid UNNEST-based forms (e.g., ARRAY(SELECT e FROM UNNEST(arr) AS e WHERE e IS NOT NULL)) and add the concrete bq commands for fetching schema and sample data.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body avoids teaching known concepts and is dense with instructions, but the scan-denial contingency ("If scan execution was denied by the user, document the denial reason") is repeated nearly verbatim in Steps 1, 1.5, 3 and 3.5, and the normalization rule ("Only trim whitespace on string fields. Make sure to PRESERVE CASE") is duplicated verbatim in both the Array and STRUCT sections. The approval-presentation template ("Value Add: Enables deep data analysis...") is also padded, so this fits 'mostly efficient but could be tightened' rather than the 'minor instances' of anchor 4. | 3 / 5 |
Actionability | Gives a copy-paste scanner command ("python3 scripts/dataplex_scanner.py --tables <project.dataset.table> ... --location <location> --output-dir <output_dir>"), concrete thresholds (">1% increase compared to source", "max 1M rows, 1-hour TTL"), specific SQL functions (SAFE.PARSE_JSON, JSON_VALUE_ARRAY, ARRAY(SELECT DISTINCT ...)), and ready-made markdown evidence templates. It falls short of 5 because "Use `bq` commands to fetch schema and sample data" specifies no actual commands, and "ARRAY_FILTER(array_column, e -> e IS NOT NULL)" / "ARRAY_TRANSFORM" use lambda syntax that is not valid BigQuery, though valid UNNEST/ARRAY_AGG alternatives are given alongside. | 4 / 5 |
Workflow Clarity | The Step 1 → Step 4 sequence has explicit validation checkpoints ("Fix any runtime errors and re-run until the query succeeds"), error-recovery feedback loops ("Identify/ Fix/ Repeat: Re-run Step 3 until the anomaly is resolved"), quantitative anomaly thresholds, evidence checklists, and progression gates ("Do not proceed to implementation until both sections are completed"). This matches the top anchor — explicit validation, feedback loops, and checklists for a complex process; the half-step numbering (1.5, 3.5) is cosmetic only. | 5 / 5 |
Progressive Disclosure | The body is well-sectioned with clear headers, and its single bundle file is real and clearly signaled one level deep ("Run the scripts/dataplex_scanner.py script located in the same directory as this SKILL.md file ... Use --help to learn more"), with no nested references. It is not a 5 because all ~260 lines of guidance are inlined in SKILL.md with no content split into reference files — the type-specific cleaning rules (JSON/Array/STRUCT) and the scanner approval template are candidates for extraction. | 4 / 5 |
Total | 16 / 20 Passed |