Content
78%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 well-structured, actionable skill body that offloads detail appropriately and sequences its core procedures clearly. The main weaknesses are repeated cross-references and implicit rather than explicit validation checkpoints in the OOM/escalation workflow.
Suggestions
Consolidate the spark-training-gotchas cross-reference to one boundary statement instead of repeating it across five sections.
Add an explicit verification checkpoint in the OOM Ladder (e.g. re-check 'free -g' headroom after the flush before escalating to batch/pack reduction).
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Dense and assumes ML competence (no padding on what OOM/LoRA is), but the spark-training-gotchas cross-reference is repeated five times and some rationale prose could be trimmed, placing it at 'efficient with minor over-explanation' rather than fully lean. | 4 / 5 |
Actionability | Copy-paste-ready commands are given for the flush step, memory probe, thermal sampling, and process check, plus a concrete Python sizing snippet; the ladder's batch/pack and method-downgrade steps are guidance without exact commands, leaving minor gaps. | 4 / 5 |
Workflow Clarity | The OOM Ladder and Planning Sequence are explicit ordered sequences with conditional feedback (flush-then-check-headroom, estimate-vs-budget), but validation checkpoints are implicit rather than formal verify steps, so it sits just below the top anchor. | 4 / 5 |
Progressive Disclosure | A clear overview with a quick-reference table, well-sectioned core procedure inline, and one-level-deep references to real bundle files (references/uma-accounting.md for the worksheet, assets/thermal-sample.sh for the sampler), with detailed computation correctly offloaded. | 5 / 5 |
Total | 17 / 20 Passed |