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 dense, highly actionable guide that appropriately splits overview from detail via four well-signaled one-level-deep reference files. Its weaknesses are modest: some duplicated parallelism-sizing guidance, a known-concept explanation, one pseudocode placeholder command, and no pre-launch validation checkpoint.
Suggestions
Remove the duplicated parallelism sizing content — the Workflow 1 model-size table and the Workflow 3 'Rules of thumb'/405B example overlap with each other and with references/parallelism-guide.md; keep one inline and point to the reference for the rest.
Replace the literal 'torchrun ... --micro-batch-size $MBS' placeholder in the micro-batch sweep with a complete runnable command (including the model/parallelism flags to use).
Add an explicit smoke-test checkpoint before full launches, e.g. run a short '--train-iters 100' dry run and verify loss decreases and MFU is in range before starting the 100k-iteration run.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dominated by executable commands, tables, and checklists rather than prose, but includes a few trims: "Expert parallelism distributes different experts across GPUs, reducing memory while maintaining capacity" explains a concept Claude already knows, and parallelism sizing is repeated across the Workflow 1 table, the Workflow 3 "Rules of thumb"/405B example, and references/parallelism-guide.md. | 4 / 5 |
Actionability | Nearly copy-paste-ready guidance throughout — the full 70B torchrun script with every flag, docker/sbatch launch commands, and commented fix commands ("--recompute-granularity full # Gradient checkpointing") — with minor gaps: the micro-batch sweep uses a literal "torchrun ... --micro-batch-size $MBS" placeholder, and examples/run_simple_mcore_train_loop.py is not part of the skill bundle. | 4 / 5 |
Workflow Clarity | Each workflow has a numbered, copyable checklist ("Step 1: Choose parallelism configuration ... Step 4: Monitor performance metrics") with concrete validation targets ("MFU: Target >40% on H100", "Loss: Should decrease steadily") and an extensive error-recovery section, but there is no smoke-test checkpoint before committing to a 100k-iteration launch. | 4 / 5 |
Progressive Disclosure | The overview-level guidance (quick start, three core workflows, troubleshooting, hardware needs) lives in SKILL.md while all four real references/ files (parallelism-guide.md, benchmarks.md, production-examples.md, training-recipes.md) are one level deep and each signaled in "Advanced topics" with a one-line scope description. | 5 / 5 |
Total | 17 / 20 Passed |