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 with executable, well-chosen examples and clean section structure. The main gap is the absence of explicit validation checkpoints sequencing the three workflows together.
Suggestions
Add an explicit checkpoint after each workflow (e.g., 'Confirm relative variation < 1e-6 before accepting a candidate invariant') to strengthen the validation loop.
Make the dependency between the three numbered workflows explicit (e.g., note that workflow 3 validates candidates from workflow 2).
Trim a few redundant docstrings/comments in the polynomial-fitting example to tighten token efficiency.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient code-first body with a one-sentence overview and brief Tips; a few docstrings and inline comments edge toward over-explanation but are largely functional rather than padded. | 4 / 5 |
Actionability | Three complete, copy-paste-ready Python workflows with imports and a concrete Kepler example cover the common discovery and verification cases fully. | 5 / 5 |
Workflow Clarity | Workflows are numbered and build on each other (generate trajectory → discover invariants → test candidates), with a YES/NO conservation check and a cross-validation tip, but explicit validation checkpoints between steps are only implied. | 4 / 5 |
Progressive Disclosure | No bundle files exist; the single-file skill is organized into clear sections (Overview, When to Use, Core Workflows, Method Summary, Tips) with no nested references, though some method detail could theoretically be split out. | 4 / 5 |
Total | 17 / 20 Passed |