Content
50%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill provides a reasonable overview of prompt engineering with useful concrete examples (zero-shot vs few-shot, before/after optimization) and a well-organized reference table. However, it leans toward describing a general methodology rather than providing Claude with specific, executable guidance it doesn't already know — much of the content (prompt engineering best practices, the iterative workflow) is knowledge Claude already possesses. The lack of bundle files undermines the progressive disclosure structure.
Suggestions
Remove or drastically shorten the constraints lists and workflow description — Claude already knows prompt engineering best practices. Focus on project-specific conventions, tool integrations, or non-obvious patterns.
Add concrete, executable evaluation examples — e.g., a specific Python script or command for measuring prompt accuracy, rather than the abstract '80% accuracy' threshold.
Provide the referenced bundle files (references/prompt-patterns.md, etc.) or remove the reference table if they don't exist, as broken references reduce trust in the skill.
Trim the 'When to Use This Skill' section — it largely restates the skill description and consumes tokens without adding actionable information.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is reasonably well-structured but includes some unnecessary content that Claude already knows — e.g., explaining when to use zero-shot vs few-shot, the general workflow of 'understand requirements → design → test → iterate → deploy,' and the Coverage Note section which is largely filler. The constraints lists are somewhat verbose with items that are general best practices rather than skill-specific guidance. | 2 / 3 |
Actionability | The before/after prompt examples are concrete and useful, and the zero-shot vs few-shot comparison is copy-paste ready. However, the core workflow is high-level process guidance rather than executable steps — there are no specific commands, tools, or scripts to run. The validation checkpoint mentions '80% accuracy' but doesn't specify how to measure it. Much of the actionable detail is deferred to reference files. | 2 / 3 |
Workflow Clarity | The 5-step core workflow is clearly sequenced and includes a validation checkpoint at step 3, which is good. However, the checkpoint is vague ('if accuracy < 80%') without specifying measurement methodology, and there's no explicit feedback loop for steps 4-5. The workflow reads more like a general methodology description than a concrete operational procedure with verifiable gates. | 2 / 3 |
Progressive Disclosure | The reference table is well-structured with clear 'Load When' guidance, which is a good progressive disclosure pattern. However, no bundle files were provided, so the six referenced files (references/prompt-patterns.md, etc.) cannot be verified to exist. The main SKILL.md also includes substantial inline content (examples, constraints, output templates) that could arguably be split out, making the overview heavier than ideal. | 2 / 3 |
Total | 8 / 12 Passed |