Content
96%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, expert-level skill body: concise, highly actionable, with a well-sequenced workflow and strong validation/feedback loops. The only minor gap is that all content lives in one inlined file with no progressive split, which is appropriate given the absence of a bundle.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Lean and dense throughout: every section carries non-obvious, task-specific knowledge (quantization_group_size, the skip_quantization helper gap, interlocking dimension constraints) with no padding about what models or quantization are, assuming Claude's competence. | 5 / 5 |
Actionability | Fully actionable — concrete file paths, exact shrink targets (layers to 2, hidden/intermediate to 128, heads to 2, vocab to 128), runnable commands (`grep -n ...`, `uv run --no-sync python -m pytest ...`, `ls tests/model_saving/`), and a symptom/cause troubleshooting table. | 5 / 5 |
Workflow Clarity | A clear numbered 1–6 sequence with an explicit Verify section, a 'Constraints that will bite you' checklist, and a Troubleshooting table providing error-recovery feedback loops (e.g. lower tensors_per_shard, report a real save/load bug rather than weakening the assertion). | 5 / 5 |
Progressive Disclosure | Well-organized into clear sections with clearly signaled one-level references to source-of-truth repo files (the three Reference implementations); no bundle files exist, so all content is inline in a single dense file with minor organization gaps. | 4 / 5 |
Total | 19 / 20 Passed |