Content
42%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is overly verbose and reads like a general prompt engineering tutorial rather than a focused, actionable reference for Claude. It extensively explains concepts Claude already understands (few-shot learning, chain-of-thought, best practices) and uses a fictional Python library in its primary code example, reducing actionability. The structure has some merit with clear sections and resource references, but the content would benefit significantly from trimming generic advice and adding concrete, executable patterns.
Suggestions
Remove or drastically condense sections that explain concepts Claude already knows (Core Capabilities descriptions, Best Practices, Common Pitfalls, Success Metrics) — these are generic prompt engineering knowledge that doesn't need to be taught.
Replace the fictional 'prompt_optimizer' library example with real, executable code or concrete prompt text templates that can be directly used.
Add explicit validation/iteration workflow: e.g., 'Write prompt → Test on 3 diverse inputs → Check for consistency → Refine specific failure modes → Re-test' with concrete checkpoints.
Fix the path inconsistency: the Instructions reference 'resources/implementation-playbook.md' but the Resources section lists paths under 'references/' and 'assets/' — align these or consolidate.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Extensively verbose with many sections that explain concepts Claude already knows well (what few-shot learning is, what chain-of-thought prompting is, basic best practices like 'Be Specific', 'Test Extensively'). The 'Common Pitfalls', 'Best Practices', 'Success Metrics', and 'Next Steps' sections are largely generic advice that adds little actionable value. The skill reads more like a textbook chapter than a concise reference. | 2 / 5 |
Actionability | The code examples (PromptTemplate, RAG integration, validation) provide some concrete guidance, but the PromptTemplate/FewShotSelector code references a fictional library ('prompt_optimizer') that isn't real or executable. Most of the content is descriptive lists and abstract advice rather than specific, copy-paste-ready patterns. The 'Core Capabilities' section is entirely bullet-point descriptions with no executable examples. | 3 / 5 |
Workflow Clarity | The 'Progressive Disclosure' pattern under Key Patterns provides a reasonable sequence (Level 1-4), and the 'Instruction Hierarchy' gives a clear ordering. However, there are no validation checkpoints or feedback loops for the prompt engineering workflow itself. The 'Integration Patterns > With Validation' section shows a self-verification concept but doesn't integrate it into a clear iterative workflow with explicit error recovery steps. | 3 / 5 |
Progressive Disclosure | References to external files (references/, assets/, scripts/) are listed clearly in the Resources section, but no bundle files are provided, making these references unverifiable. The SKILL.md itself is monolithic at ~200 lines with content that could be split into separate files (e.g., the detailed Core Capabilities descriptions, Integration Patterns). The instruction to 'open resources/implementation-playbook.md' is good but the path doesn't match any listed resource. | 3 / 5 |
Total | 11 / 20 Passed |