Content
80%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The body is action-oriented and well-structured with executable examples and a clean one-level reference split, but the submission workflow lacks explicit validation and error-recovery checkpoints for a money-committing batch operation.
Suggestions
Add explicit validation/error-recovery checkpoints to the submission workflow (e.g., what to do if cost_estimate fails or the quote is rejected), so the multi-step process includes a fix-and-retry feedback loop.
Tighten the redundant token guidance — consolidate the FOUNDRY_API_TOKEN note and the repeated 'never hardcode / never commit tokens' reminders into a single concise statement.
Add a brief note on how to confirm a submitted experiment reached the expected status (e.g., polling cadence or webhook verification) to strengthen the lifecycle workflow.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient with concrete code, tables, and no filler about basic concepts, but a few passages are mildly redundant (the FOUNDRY_API_TOKEN note and repeated token-security reminders) and could be tightened. | 2.5 / 3 |
Actionability | Provides fully executable curl and Python examples (decorator and FoundryClient patterns) with specific parameters and copy-paste-ready snippets, matching the anchor for complete executable guidance. | 3 / 3 |
Workflow Clarity | The binding-screen workflow is clearly sequenced (find target, estimate, create, submit, poll, retrieve), but for a costly batch submission it lacks explicit validation/checkpoint or error-recovery steps, which caps the score per the rubric's feedback-loop guidance. | 2 / 3 |
Progressive Disclosure | SKILL.md is a clear overview with a single well-signaled, verified one-level-deep reference (references/api-endpoints.md) that holds the full endpoint detail, keeping the main body appropriately split. | 3 / 3 |
Total | 10.5 / 12 Passed |