Content
35%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 tutorial or reference guide about prompt engineering concepts that Claude already deeply understands, rather than actionable instructions for a specific task. The content is verbose with explanations of well-known concepts (few-shot learning, chain-of-thought) and generic best practices. While examples are provided, they serve more as illustrations of concepts than as executable templates Claude would need to follow.
Suggestions
Remove explanations of concepts Claude already knows (what few-shot learning is, what chain-of-thought is) and focus only on project-specific patterns, preferred formats, or non-obvious techniques.
Add a concrete workflow with validation steps: e.g., 'When user asks to improve a prompt: 1. Identify the failure mode 2. Apply the matching pattern 3. Test with the user's example input 4. Compare outputs before recommending.'
Cut the 'Best Practices' and 'Common Pitfalls' sections entirely—these are generic knowledge Claude already has—and replace with specific, actionable decision trees or lookup tables.
Consider splitting detailed examples into a separate EXAMPLES.md file and keeping SKILL.md as a concise overview with clear references.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extensively explains concepts Claude already knows well—prompt engineering patterns, few-shot learning, chain-of-thought, etc. are core LLM knowledge. Phrases like 'Improves accuracy on analytical tasks by 30-50%' and explanations of what few-shot learning is are unnecessary. The 'Best Practices' and 'Common Pitfalls' sections are generic advice Claude inherently understands. | 1 / 3 |
Actionability | Examples are provided and some are concrete (the Python template, the few-shot extraction example), but much of the content is descriptive rather than instructive. The skill tells Claude about prompt engineering concepts rather than giving it specific, executable procedures to follow when a user asks for help. The optimization example shows version progression but isn't copy-paste actionable. | 2 / 3 |
Workflow Clarity | The Progressive Disclosure section provides a clear sequence of escalating complexity, and the Instruction Hierarchy gives a useful ordering. However, there are no validation checkpoints—no guidance on how to verify a prompt is working, no feedback loops for iterating when results are poor, and no concrete steps for the prompt optimization process beyond 'test and iterate.' | 2 / 3 |
Progressive Disclosure | Content is organized into sections with headers, but everything is inline in one large file with no references to external resources. For a skill this long (~150+ lines), some content like the detailed examples or the template systems section could be split into separate files. The structure is reasonable but the monolithic approach hurts navigability. | 2 / 3 |
Total | 7 / 12 Passed |