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.
The body is highly actionable with a clear 5-step workflow, validation guard, and good file structure, but it carries some inlined detail that would be better offloaded to references. Overall a strong, executable skill.
Suggestions
Move the full Grader strategy list and Known Failure Patterns table into a reference file (e.g. references/grader-and-patterns.md) and link to it, keeping only a one-line summary in SKILL.md to improve conciseness and progressive disclosure.
Add an explicit retest pass/fail checkpoint (e.g. 'only update baseline if wrong->correct flips exceed regressions') to strengthen validation feedback loops.
Tighten the Anti-memorization guard section by linking the flag definitions to check_memorization.py rather than restating them inline.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient with commands and tables doing heavy lifting, but sections like the Grader strategy list and Known Failure Patterns include detail that pads the body with material Claude could derive from the bundled scripts. | 3 / 5 |
Actionability | Provides fully executable, copy-paste-ready commands at every step (run_harness_loop.sh, run_eval.py invocations with flags) plus concrete diagnosis-to-skill routing tables covering common cases. | 5 / 5 |
Workflow Clarity | The 5-step loop is clearly sequenced with a diagnose-retest feedback loop and validation via check_memorization.py, though a few batch/retest steps could surface explicit pass/fail checkpoints more prominently. | 4 / 5 |
Progressive Disclosure | Well-structured overview that points to one-level-deep bundle files (scripts/, references/benchmark-guide.md) with clear sectioning; minor gaps where some referenceable detail (grader internals, failure patterns) is inlined rather than linked out. | 4 / 5 |
Total | 16 / 20 Passed |