Content
65%Reviews 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 complete, executable code and a concrete worked example, but it is monolithic and lacks explicit validation feedback loops, capping workflow clarity and progressive disclosure at 2.
Suggestions
Add an explicit validate→fix→retry loop (e.g., after discovery, run test_conservation and iterate on max_degree/thresholds until relative variation passes) to raise workflow clarity.
Split the longer method implementations (polynomial null-space discovery, method comparison table) into references/ files and link to them from SKILL.md to improve progressive disclosure.
Trim debug print statements and redundant inline comments to tighten conciseness while keeping the executable core.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Prose is lean and avoids re-explaining basic concepts, but the code carries verbose debug print statements and lengthy docstrings/comments that could be tightened, matching 'mostly efficient but could be tightened'. | 2 / 3 |
Actionability | Provides fully executable Python (solve_ivp, numpy SVD, finite-difference tests) with a concrete Kepler example that is copy-paste ready, matching the anchor for fully executable code. | 3 / 3 |
Workflow Clarity | Three numbered workflows are presented as parallel techniques with some built-in checks (test_conservation YES/NO, cross-validate tip), but there is no explicit validate→fix→retry feedback loop or sequenced checkpoint tying them into one pipeline. | 2 / 3 |
Progressive Disclosure | Well-organized into sections (Overview, When to Use, Core Workflows, Method Summary, Tips) but monolithic: all ~200 lines and heavy code live inline with no references to separate files, and no bundle files exist to offload detail. | 2 / 3 |
Total | 9 / 12 Passed |