Content
82%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 strong, highly actionable skill body: real executable commands for every tool, a clear 5-step workflow with conditionals and a human sign-off gate, and a well-organized one-level-deep reference bundle. The main improvements are trimming duplicated tool descriptions, adding an iteration loop for REDESIGN/NO-GO verdicts, and moving the forcing-question library into a reference file.
Suggestions
Merge the Purpose section's tool bullets with the Scripts table — they describe the same three scripts twice; keep one (the table) and trim the opening paragraph that restates the frontmatter description.
Add a feedback step to the Workflow for adverse verdicts, e.g., step 4.5: 'If REDESIGN or NO-GO: address the listed blockers (endpoints, enrollment, budget), update study.json, and re-score before routing for sign-off.'
Move the forcing-question library to a reference file (e.g., references/grill_questions.md) and keep a one-line pointer in SKILL.md, since it is only walked by /cs:grill-research-ops; consider the same for the opt-in autoresearch section.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense and operational — no concept-teaching padding (it never explains what power or an endpoint is), and tables/commands carry most of the load. However, the opening paragraph restates the frontmatter description, and the Purpose section's three tool bullets duplicate the Scripts table's descriptions of the same three scripts — trimmable redundancy that fits the score-4 anchor ('minor instances of over-explanation that could be trimmed') rather than the fully lean score-5 anchor. | 4 / 5 |
Actionability | Commands are copy-paste ready and backed by real bundle files: 'python3 scripts/sample_size_estimator.py --design proportions --p1 0.30 --p2 0.45 --dropout 0.15', 'endpoint_selector.py --input endpoints.json --profile {drug|device|biologic|diagnostic|digital-therapeutic}', onboarding commands, and the /ar:setup example. '--sample' invocations cover common cases and every referenced script and template exists in the bundle — fully matching the score-5 anchor. | 5 / 5 |
Workflow Clarity | The 5-step Workflow is clearly sequenced with real checkpoints: 'If >1 primary, plan multiplicity control', 'Trace the effect/difference/HR to a published or anchor-based source', 'inflate for dropout', and the final sign-off gate ('a biostatistician, medical monitor, and regulatory owner sign'). It falls short of the score-5 anchor because there is no feedback loop for adverse verdicts — nothing says what to do when the scorer returns REDESIGN or NO-GO (e.g., address blockers and re-score). | 4 / 5 |
Progressive Disclosure | Structure is good against the actual bundle: three topical reference files are listed with one-line content summaries ('references/study_design_canon.md — ICH E8(R1)... '), all one level deep, and scripts plus the asset template are correctly cited. But content that belongs in a reference is inlined: the ~25-line forcing-question library (walked only by '/cs:grill-research-ops') and the opt-in autoresearch section could be split out, matching the score-4 anchor ('most content is appropriately placed; minor organization gaps') rather than the score-5 anchor ('content appropriately split'). | 4 / 5 |
Total | 17 / 20 Passed |