Content
92%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, highly actionable skill body: copy-paste commands for every subcommand, a sequenced six-step workflow with an explicit pre-flight validation checkpoint, and clean progressive disclosure into four real reference files and two scripts. The only minor gap is a little explanatory prose that could be tightened without losing clarity.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly lean — tables, bullet lists, and code blocks carry the content, and the prose (the 'sentence' metaphor, pooling rationale, caveats) is novel 2026 domain knowledge Claude would not already know. A few explanatory passages around the code could be trimmed slightly, sitting just below the 'every token earns its place' anchor. | 4 / 5 |
Actionability | Fully executable, copy-paste-ready commands with concrete flags across all five subcommands and both bundled scripts (e.g. 'waypoint embed --model outpost-bio/Waypoint-6m --data dataset.parquet --output embeddings.parquet'), covering the common embedding/finetune/benchmark/pretrain cases. | 5 / 5 |
Workflow Clarity | A numbered six-step workflow (prepare → vocab coverage → embed → finetune → benchmark → pretrain) with an explicit validation checkpoint in step 2 ('Check vocabulary coverage before anything else', with the ~0.8 threshold and re-examine guidance) and a load-bearing caveats checklist, satisfying the explicit-validation/feedback-loop anchor. | 5 / 5 |
Progressive Disclosure | SKILL.md is a concise overview that points to four real one-level-deep reference files (cli-reference.md, compass-benchmark.md, data-preparation.md, python-api.md) and two real scripts, all verified present and clearly signaled via dedicated References/Scripts sections plus inline 'See references/... for...' pointers. | 5 / 5 |
Total | 19 / 20 Passed |