Content
82%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 is highly actionable and well-structured with executable commands and a clear case-routing table. It is slightly held back by the absence of explicit validation feedback loops and minor verbosity in the gotchas.
Suggestions
Add an explicit validation/checkpoint step for the ROC analysis (e.g., confirm both classes are present and report PPV at the real prevalence before quoting AUC) to strengthen the workflow's feedback loop.
Tighten the 'PPV/NPV trap' callout and Gotchas into shorter bullets to trim a few tokens without losing the key warnings.
Consider splitting the long metric-definition table in Step 1 into a short inline summary with the full reference one level deeper, improving progressive disclosure.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Lean and mostly efficient with dense, high-value tables and executable commands; the 'PPV/NPV trap' callout and Gotchas section are slightly expansive but earn their tokens, leaving minor trimming possible. | 4 / 5 |
Actionability | Fully executable guidance throughout — concrete `tu run` commands with complete JSON payloads, the `ROC_analysis(...)` MCP form with inline args, and a real bundled CLI script — copy-paste ready and covering the common cases. | 5 / 5 |
Workflow Clarity | A clear decision-table routes to sequenced Step 1/2/3 branches, but there are no explicit validate-then-fix feedback checkpoints for the statistical/batch operations, leaving minor validation gaps. | 4 / 5 |
Progressive Disclosure | Well-organized single-file overview with a one-level-deep bundled reference (scripts/roc_analysis.py, verified present) clearly signaled; structure is good though the body is denser than the simple-skill exception envisions. | 4 / 5 |
Total | 17 / 20 Passed |