Content
22%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill reads more like a prompt engineering textbook or course outline than an actionable skill file for Claude. It is heavily padded with general knowledge Claude already possesses (best practices, common pitfalls, success metrics), uses a fictional Python library in its primary code example, and lacks concrete, executable workflows with validation checkpoints. The content would benefit enormously from being reduced to ~30% of its current size, focusing only on novel patterns and executable guidance.
Suggestions
Cut 'Core Capabilities', 'Best Practices', 'Common Pitfalls', 'Success Metrics', and 'Next Steps' sections entirely — these are general knowledge Claude already has. Focus only on novel, project-specific patterns and conventions.
Replace the fictional 'prompt_optimizer' library example with real, executable code or concrete prompt templates that can be directly used (e.g., actual prompt strings with placeholders, not imaginary API calls).
Add a concrete iterative workflow with validation checkpoints, e.g.: '1. Write initial prompt → 2. Test on 3 representative inputs → 3. Evaluate outputs against criteria → 4. If failures: identify failure mode and adjust → 5. Re-test until passing'.
Move the detailed inline content (integration patterns, performance optimization) to the referenced files and keep SKILL.md as a concise overview with clear pointers.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extremely verbose and padded with information Claude already knows. Sections like 'Best Practices' ('Be Specific: Vague prompts produce inconsistent results'), 'Common Pitfalls', 'Success Metrics', and 'Core Capabilities' are all general knowledge that Claude possesses. The skill reads like a textbook chapter rather than actionable instructions. | 1 / 3 |
Actionability | Contains some code examples (Quick Start, RAG integration, validation), but the code references a fictional 'prompt_optimizer' library that isn't real or executable. Most content is descriptive lists and abstract advice rather than concrete, copy-paste-ready guidance. The patterns section provides some structure but lacks executable specificity. | 2 / 3 |
Workflow Clarity | No clear multi-step workflow with validation checkpoints. The 'Progressive Disclosure' pattern shows levels but isn't a workflow. The 'Next Steps' section is vague ('experiment with few-shot learning'). There's no iterative refinement loop with explicit validation steps despite the skill being about prompt optimization, which inherently requires feedback loops. | 1 / 3 |
Progressive Disclosure | References to external files are well-signaled at the bottom (references/, assets/, scripts/), which is good. However, the main file is a monolithic wall of text with extensive inline content that could be split into referenced files. The Core Capabilities section alone lists 25+ bullet points that should be in separate reference docs. | 2 / 3 |
Total | 6 / 12 Passed |