Content
65%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.
The content is highly actionable with executable examples across config formats, CLI, and programmatic APIs, and includes a useful validation feedback loop. However, it is a large monolithic reference that would benefit from progressive disclosure into separate bundled reference files.
Suggestions
Move the exhaustive 'Rule Reference' tables and 'Complete Configuration Schema' into separate reference files (e.g. references/rules.md, references/schema.md) and link to them from SKILL.md.
Tighten verbose commented schema blocks to only the non-obvious fields to improve token efficiency.
Add a brief end-to-end 'Getting started' workflow at the top that sequences install → configure → lint → interpret exit codes.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is mostly efficient reference material without 'what is a commit message' padding, but at ~510 lines it includes exhaustive rule tables and a fully-commented complete schema that could be tightened or moved to reference files. | 3 / 5 |
Actionability | It provides copy-paste ready installation commands, CLI invocations, executable JS/TS/JSON/YAML config examples, and Python snippets for programmatic validation and rule extraction covering the common cases. | 5 / 5 |
Workflow Clarity | The LLM 'Validation loop pattern' is a clear sequenced feedback loop (generate → lint → feed errors back → retry), but the skill is otherwise reference-oriented with no end-to-end workflow tying the sections together. | 3 / 5 |
Progressive Disclosure | Sections are well-organized with clear headers, but no bundle files exist and large reference blocks (full rule tables, complete schema) are inlined in SKILL.md rather than split into one-level-deep reference files. | 3 / 5 |
Total | 14 / 20 Passed |