Content
77%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 multi-step research workflow with explicit validation and feedback loops; its main weakness is monolithic inlining of detail that could be split into reference bundle files for better progressive disclosure.
Suggestions
Split the detailed four-masters framework, the AI-bias table, and the audit-tool usage into separate reference files under references/ and link to them from SKILL.md to improve progressive disclosure.
Provide a minimal filled example for at least one empty template table (e.g. the moat-scoring or risk-checklist table) so the expected output shape is unambiguous.
Tighten or relocate the Codex adapter note so the core research workflow starts sooner and the body stays lean.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Largely efficient — compact tables, numbered steps, and checklists with minimal padding and no explanation of concepts Claude already knows; only the Codex adapter note and a few elaborated sections could be trimmed. | 4 / 5 |
Actionability | Provides concrete guidance including exact audit commands, a defined output path, tiering, star-rating scales, and table schemas, though many tables are empty fill-in templates and the four-masters analysis relies on qualitative judgment. | 4 / 5 |
Workflow Clarity | Clear 8-step numbered sequence with sub-steps, multiple checklists (risk, bias, moat), and an explicit data-audit exit gate with a validate→verdict→retry feedback loop for the report-generation batch operation. | 5 / 5 |
Progressive Disclosure | Good section structure aids navigation, but the skill is a single monolithic ~280-line SKILL.md with no bundle files; detailed content (frameworks, bias tables, audit usage) is inlined and references point to external repo tools rather than clearly-signaled one-level-deep bundle files. | 3 / 5 |
Total | 16 / 20 Passed |