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 high-quality, code-first skill body: fully executable workflows covering the core physics-fitting tasks, effective pitfall/interpretation tables, and clear scoping via a 'Do NOT Use When' section. The main gaps are a missing explicit validation feedback loop in the fitting workflow, some trimmable plotting boilerplate, and no use of reference files despite a length that would benefit from them.
Suggestions
Embed an explicit validation gate in the fit workflow, e.g. 'if chi2_red is far from 1, try multiple initial guesses (p0) or lmfit bounds before reporting parameters'.
Trim the plotting boilerplate (full figure/residual-plot setup, unused t_dist import) or move it to a references/plotting.md to keep the core workflows lean.
Consider splitting the model-comparison and error-propagation utilities into a references/ file so SKILL.md stays a lean overview with well-signaled one-level-deep references.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is mostly lean: terse comments, no re-teaching of concepts Claude already knows, and code that earns its tokens. Minor trimmable material exists — the full plotting boilerplate (subplots, labels, gridspec) and an unused 'from scipy.stats import t as t_dist' import — placing it between 'mostly efficient' (3) and 'lean and efficient, every token earns its place' (5), noticeably above the midpoint. | 4 / 5 |
Actionability | All four workflows (scipy curve_fit, lmfit with bounds and confidence intervals, AIC/BIC model comparison, Jacobian-based error propagation) are complete, executable, copy-paste-ready Python with realistic sample data. The code covers the common cases end-to-end, matching the top anchor. | 5 / 5 |
Workflow Clarity | The workflows are clearly sequenced (fit, extract parameters with uncertainties, compute chi-squared/ndof, compare models) and the Goodness-of-Fit table and Common Pitfalls table supply interpretation and error-recovery guidance. However, no explicit validate-then-proceed gate or retry loop is woven into the steps (e.g. 'if chi2_red is far from 1, re-fit with different p0'), so it falls short of the anchor requiring explicit validation steps and feedback loops. | 4 / 5 |
Progressive Disclosure | The single file is well-organized into clear sections (When to Use, Do NOT Use When, four workflows, reference tables) with no nested or buried references, and cross-references to sibling skills are clearly signposted. But at ~230 lines with no bundle files, content like the full plotting/confidence-band block would fit naturally in a references/ file, so structure is good with minor organization gaps rather than an ideal split. | 4 / 5 |
Total | 17 / 20 Passed |