Content
85%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 ops-capacity skill: clean workflow with validation gates and a checklist, good progressive disclosure to real reference/script/asset bundles, and no padding of known concepts. Minor conciseness and inline-example gaps keep it just short of perfect.
Suggestions
Trim the full bibliographic citations in the Forcing-question library to author + short title (e.g., 'Goldratt, The Goal') to reduce tokens without losing grounding.
Show a minimal inline JSON input skeleton for one script so the input shape is visible without opening the asset template.
Consider moving the seven forcing questions to a references file and keeping only the discipline preamble plus a one-line-per-question summary in SKILL.md.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Dense and largely free of concepts Claude already knows (no 'what is Erlang-C' padding), but the full bibliographic citations in the Forcing-question library ('Eli Goldratt, The Goal, 1984'; 'Hopp & Spearman, Factory Physics, 3rd ed., 2008') and some 'Why it matters' prose add tokens that could be trimmed without losing clarity. | 4 / 5 |
Actionability | Concrete, executable guidance throughout — named scripts with specific flags (--profile, --input, --output, --sample, --help) and a copy-paste quick example — but the actual JSON input/output shape is deferred to the asset template rather than shown inline, leaving a minor gap. | 4 / 5 |
Workflow Clarity | Five-step workflow is clearly sequenced with explicit validation gates (Step 1: 'If you only have averages, stop and pull the distribution'; Step 3: '>85% sustained is a throughput-collapse risk', 'fix that before hiring') and a forcing-question checklist with feedback loops ('If you can't answer one, that is your next investigation'). | 5 / 5 |
Progressive Disclosure | SKILL.md is a clear overview with well-signaled one-level-deep references to real bundle files — references/queueing_theory_canon.md, ops_workforce_planning_canon.md, capacity_anti_patterns.md, scripts/*.py, assets/capacity_brief_template.md — each described with its contents and source count; no nested reference chains. | 5 / 5 |
Total | 18 / 20 Passed |