Content
14%Scale 1-3Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill is essentially a skeleton or outline with no actionable content. It names important AI product engineering concepts (structured output, prompt versioning, validation) but provides zero executable code, no concrete examples, no workflows, and no references to deeper material. The Sharp Edges table is particularly frustrating as it lists critical issues with solution columns that contain only truncated comments rather than actual solutions.
Suggestions
Add complete, executable code examples for each pattern (e.g., a full Python snippet showing JSON schema validation of LLM output, a streaming implementation, a prompt versioning approach).
Fill in the Sharp Edges table's Solution column with actual code or concrete steps instead of truncated comments like '# Always validate output:'.
Add a clear workflow section with numbered steps for a common task (e.g., 'Building a validated LLM feature') including explicit validation checkpoints and error recovery.
Remove the repeated persona description from the body (it's already in the frontmatter) and replace the boilerplate 'When to Use' section with specific applicability criteria.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The persona description is repeated from the frontmatter and adds no value. The patterns and anti-patterns sections are terse but lack substance—they name concepts without providing enough detail to be useful, making them simultaneously too verbose (persona repetition) and too sparse (missing actual content). | 2 / 3 |
Actionability | Despite referencing code patterns like 'function calling or JSON mode with schema validation' and listing solutions in the Sharp Edges table, there is zero executable code, no concrete commands, and no specific examples. The table's 'Solution' column contains only comments (e.g., '# Always validate output:') with no actual implementation. This is vague direction, not actionable guidance. | 1 / 3 |
Workflow Clarity | There is no sequenced workflow, no multi-step process, and no validation checkpoints. The content lists concepts and anti-patterns but never describes how to actually build, validate, or deploy an AI product feature step by step. | 1 / 3 |
Progressive Disclosure | The content is a flat, monolithic document with no references to external files, no layered structure, and no navigation aids. Sections like 'Structured Output with Validation' and 'Streaming with Progress' cry out for linked detailed guides but provide none. The 'When to Use' section is a meaningless boilerplate sentence. | 1 / 3 |
Total | 5 / 12 Passed |