Content
92%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
Highly actionable and concise content with clear, sequenced workflows and an explicit validation step. The main gap is progressive disclosure: everything lives inline in one file with no separate reference materials.
Suggestions
Move the detailed analysis-shortcut recipes or the bottleneck-solution table into a separate reference file (e.g. references/optimization.md) and link to it from SKILL.md to apply progressive disclosure.
Add an explicit retry/revert feedback loop in Mode 2 Step 5 for the case where training correctness is not preserved after optimization.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Dense, lean reference material using tables, config blocks, and code with no padding or explanation of basic concepts Claude already knows; every section earns its place. | 3 / 3 |
Actionability | Provides fully executable Python (load_chrome_trace, analyze_kernel_time), a copy-paste YAML profile config, torchrun commands, and a concrete bottleneck-to-solution table. | 3 / 3 |
Workflow Clarity | Mode 2 is sequenced Step 1-5 with an explicit Step 5 validation checkpoint ('Verify training correctness is preserved (loss matches baseline)'); operations are non-destructive so the strict feedback-loop cap does not apply. | 3 / 3 |
Progressive Disclosure | Well-organized into clear sections and tables (avoiding a 1), but all detailed content is inline in a single ~145-line file with no content split across bundle files or one-level-deep references; the referenced scripts/profile paths are external repo files, not skill bundle references. | 2 / 3 |
Total | 11 / 12 Passed |