Content
42%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The body is well-structured with good progressive disclosure and a solid error-handling table, but it is instruction-only with no executable code, leaving its steps abstract rather than copy-paste ready. Workflow checkpoints for batch generation are also underdeveloped.
Suggestions
Add executable code or concrete commands for at least the core flows (client init, an image-generation request, a video-analysis request) so guidance is copy-paste ready.
Insert explicit validation checkpoints in the workflow (e.g. verify API response, check safety-filter flags, confirm asset integrity) with a fix-and-retry loop before delivery.
Move volatile specifics (exact model IDs, quota numbers) into the existing references file so the body stays lean and age-resistant.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is mostly efficient and free of beginner-concept filler, but it embeds time-sensitive specifics (model versions, quota figures) in prose steps that could be tightened, and the prose-only steps add length without executable value. | 2 / 3 |
Actionability | Steps are abstract directions ('Initialize the Vertex AI client', 'Construct the generation request with explicit parameters') with no executable code or commands anywhere, and the examples lack runnable snippets. | 1 / 3 |
Workflow Clarity | A clear 7-step numbered sequence exists with a helpful error table, but batch media-generation operations lack explicit validate-then-fix-then-retry checkpoints until a vague final 'Validate results', capping the score at 2. | 2 / 3 |
Progressive Disclosure | Sections are well organized and a real one-level-deep reference to references/core-capabilities.md is clearly signaled alongside curated external docs, giving easy navigation. | 3 / 3 |
Total | 8 / 12 Passed |