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 executable code and dense reference tables, and it points to real, well-signaled reference files. Its main weakness is conciseness: marketing-style prose and scattered time-sensitive details pad the guide beyond what Claude needs to know.
Suggestions
Cut promotional framing ('best model in the world', star intelligence ratings, 'previously requiring human expertise') and keep the benchmark/spec tables as the source of truth — Claude needs the numbers, not the sales pitch.
Consolidate time-sensitive details (release dates, knowledge cutoffs, model IDs) into one clearly marked section or a reference file so the body stays stable as versions change.
Add a brief inline model-selection decision rule (e.g., 'complex reasoning/agents -> Opus 4.5, fast routine tasks -> Sonnet/Haiku') so the most common question is answerable without opening the reference file.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient data-dense tables and code, but padded marketing prose ('best model in the world for complex coding', 'previously requiring human expertise', star-based intelligence ratings) assumes no competence, and time-sensitive dates/versions ('released November 24, 2025', knowledge cutoffs) are scattered rather than isolated. Could be tightened by cutting the promotional framing. | 3 / 5 |
Actionability | Fully executable, copy-paste-ready Python for basic usage and all three effort levels, with the real model ID ('claude-opus-4-5-20251101'), output_config, and beta header, plus concrete pricing and model-ID tables covering common cases. | 5 / 5 |
Workflow Clarity | As a reference skill there is no multi-step process, and lookup paths are clear ('When to Use This Skill', effort-level table with use cases, 'Best Practice: Default to high effort, then measure and decrease strategically'). Not 5 because the actual model-selection decision flow is deferred to a reference file rather than stated inline, leaving a minor gap for the most common question. | 4 / 5 |
Progressive Disclosure | Three real, one-level-deep reference files (references/effort-parameter-guide.md, model-selection-guide.md, model-capabilities.md) are clearly signaled with purpose statements at the end. Not 5 because the full specification and benchmark tables inlined in the body duplicate what the references cover, and the 'Related Skills' links point to unverifiable ../ sibling skill paths. | 4 / 5 |
Total | 16 / 20 Passed |