Content
75%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 solid, well-sequenced investigation workflow with concrete Astro/Airflow CLI commands and clear output format guidance. Its weaknesses are modest: no explicit fallback loops for dead-end searches, and example report content that pads the token budget slightly.
Suggestions
Add brief fallback guidance for dead ends (e.g., 'If no DAG name matches the table, grep DAG source code for the table name or check the Astro Lineage tab'), which would add an explicit feedback loop and raise workflow clarity.
Trim the illustrative lineage trees and fabricated report rows to schema-only examples (headers plus placeholder rows) to tighten the token budget.
Consider moving the full Output report format section into a references/ file if the skill grows, keeping SKILL.md as a lean overview.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is efficient — it assumes Airflow knowledge, gives commands without library tutorials, and uses compact lists ('S3Operator -> S3 bucket source'). Minor trimming is possible: the two large illustrative ASCII lineage trees and the fabricated example report rows (freshness '2h ago', owner 'data-team') are example filler that could be shortened, so it fits 'efficient; minor instances of over-explanation that could be trimmed' rather than the fully lean score 5. | 4 / 5 |
Actionability | Concrete, copy-paste-ready commands appear throughout: 'af dags list', 'af dags source <dag_id>', 'af tasks list <dag_id>', 'af dags stats', plus specific search patterns ('load_customers -> customers table') and column-transformation examples. It falls short of score 5 only because key steps rely on hints ('Look for INSERT, MERGE...') rather than complete worked examples of interpreting command output, and the recursion in Step 4 is described but not given as a concrete procedure. | 4 / 5 |
Workflow Clarity | A clear five-step sequence (identify target type -> find producing DAG -> trace sources -> build chain -> check source health) with a health-check step serving as validation. It does not reach score 5 because there are no explicit error-recovery loops (e.g., what to do when no DAG name matches the table or a source cannot be traced), leaving feedback checkpoints implicit. | 4 / 5 |
Progressive Disclosure | No bundle files exist, and the ~135-line body is organized into well-labeled sections with cross-links to sibling skills clearly signaled under 'Related Skills'. Structure is good, matching 'good structure; most content is appropriately placed; minor organization gaps' — the Output report format and column-lineage sections are borderline candidates for separate reference files if the skill grows, keeping it below score 5. | 4 / 5 |
Total | 16 / 20 Passed |