Content
77%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.
Highly actionable, executable skill content with a clear sequence and useful pitfalls. The main weakness is progressive disclosure: the large config block is inlined rather than externalized, and the one reference link is not a real bundle file.
Suggestions
Move the full ~90-line mmengine config into ./references/minicpm5_lora.py and reference it from SKILL.md, keeping only the key annotated lines inline to improve progressive disclosure.
Either add the referenced ./docs/finetune/xtuner.md as a real bundle file or remove/fix the broken reference link.
Add an explicit validate→fix→retry loop (e.g., 'if the chat template does not resolve to <|im_start|>/<|im_end|> or loss is flat, re-check prompt_template and start_factor before re-running') to strengthen the validation checkpoint.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Lean, code-driven body that assumes competence and avoids explaining what SFT or LoRA are; the inlined full config (~90 lines) is necessary material but could be trimmed or externalized to earn a 5. | 4 / 5 |
Actionability | Fully executable: copy-paste install commands, a complete runnable config, exact train/convert commands, and a concrete PEFT loading snippet covering the common path. | 5 / 5 |
Workflow Clarity | Clear numbered sequence (install → save config → train → validate → convert) with a validation step showing expected logs, but the destructive/batch training step lacks an explicit validate→fix→retry feedback loop, so it does not reach 5. | 4 / 5 |
Progressive Disclosure | Well-organized sections, but the full ~90-line config is inlined in SKILL.md (content that should be separate), and the single reference link points to a docs path that does not exist in the bundle. | 3 / 5 |
Total | 16 / 20 Passed |