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.
A strong, executable skill body with well-structured workflows and concrete code throughout. The main gap is the absence of explicit validation/feedback-loop checkpoints for batch simulation runs, and inline reference material that could be split into separate files.
Suggestions
Add explicit validation checkpoints to batch workflows (e.g. 'verify <|M|> is in [0,1] and C >= 0 before recording; if not, extend equilibration'), with a fix-and-retry loop.
Move the critical-exponents table and extended algorithm notes into a references/ file, signaled from SKILL.md, to improve progressive disclosure.
Tighten a few inline comments that restate what the code already shows.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly lean and code-forward with minimal padding; only minor over-explanation remains (e.g. restating 'Wolff cluster flip (much faster near T_c)' and a few inline comments that assume little), so it sits above the midpoint but not fully lean. | 4 / 5 |
Actionability | Fully executable, copy-paste-ready Python covering the common cases (Metropolis/Wolff sweeps, temperature sweep, finite-size scaling, autocorrelation), with concrete observables and plotting code. | 5 / 5 |
Workflow Clarity | Four clearly sequenced workflows with implicit validation (equilibration vs measurement separation, autocorrelation/error estimation, T_c cross-check against Onsager), but no explicit validation checkpoints or fix-retry feedback loops for the batch operations. | 4 / 5 |
Progressive Disclosure | Well-organized sections (Overview, When to Use, Core Workflows, exponents, troubleshooting) with no nested references and a single self-contained body; minor gap only because bulk reference material (exponent tables, API details) is inline rather than split out. | 4 / 5 |
Total | 17 / 20 Passed |