Content
73%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 meta-skill with a clear phased workflow and strong validation checkpoints. The main weakness is mild verbosity in the philosophy and conversational-style sections, and reference load conditions could be more explicitly conditional.
Suggestions
Tighten the "Core Philosophy" and "Conversation Style" sections to directive bullets, cutting rationale prose that an LLM can infer, to improve token efficiency.
Convert reference pointers into explicit conditional load language (e.g. "If the user's workflow spans multiple services, read references/patterns.md section 2") so the agent knows precisely when to incur the file read.
Consider moving the category/pattern reference tables or the full anti-pattern list fully out of the body into the referenced files to reduce body length and reinforce progressive disclosure.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient instructional content, but the "Core Philosophy" and "Conversation Style" sections add rationale prose that could be tightened; does not heavily explain concepts Claude already knows. | 3 / 5 |
Actionability | Provides concrete templates (YAML frontmatter, folder structure), decision-criteria tables, hard rules, and checklists, plus a runnable validate_skill.py script; mostly executable guidance with minor gaps. | 4 / 5 |
Workflow Clarity | Clear five-phase sequence (Discovery -> Architecture -> Craft -> Validate -> Deliver) with explicit exit-criteria checklists per phase and a dedicated Validate phase providing feedback loops and checkpoints. | 5 / 5 |
Progressive Disclosure | References real one-level-deep bundle files (patterns.md, examples.md, quality-checklist.md, validate_skill.py) signaled with step context; minor gap is that load conditions are not always framed as crisp conditionals. | 4 / 5 |
Total | 16 / 20 Passed |