Content
77%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A highly actionable, well-sequenced update playbook with clear verification steps. Its main weaknesses are inline time-sensitive version detail and dense torch content that would be better split into a reference file.
Suggestions
Move the volatile torch/CUDA wheel URLs, git revs, and flash-attn/flash-qla pin details into a referenced file (e.g. references/torch-update.md) and keep SKILL.md as an overview.
Add an 'old patterns' or 'deprecated' section for time-sensitive version specifics so conciseness is not penalized for them.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly lean and actionable with no basic-concept padding, but it is dense with time-sensitive version pins, direct wheel URLs, and git revs that the rubric penalizes when not isolated in a deprecated/old-patterns section. | 2 / 3 |
Actionability | Every scenario gives concrete executable commands ('uv lock', 'uv sync --extra gpu --dev', 'pytest tests/', 'make patchgen') and exact pyproject.toml/Dockerfile path locations. | 3 / 3 |
Workflow Clarity | Each scenario is a clearly numbered sequence with explicit verification checkpoints ('git diff uv.lock', 'pytest tests/') plus a Common Pitfalls section for error recovery. | 3 / 3 |
Progressive Disclosure | There is one well-signaled one-level reference ('.agents/knowledge/uv.md'), but the dense torch/CUDA and flash-attn wheel/rev details are inline rather than split into a separate reference file. | 2 / 3 |
Total | 10 / 12 Passed |