Content
87%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-structured skill that efficiently covers the Tavily API surface with executable examples and excellent progressive disclosure to reference files. The main weakness is the lack of error handling, validation steps, and recovery patterns—particularly important for the research() polling workflow and batch extraction scenarios. The decision table for method selection is a standout feature that adds significant value concisely.
Suggestions
Add basic error handling to the research() polling example (e.g., what to do when status is 'failed', timeout handling)
Include a brief note on rate limits or common failure modes and how to handle them, especially for extract() with multiple URLs
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is lean and efficient. It avoids explaining what a search API is or how Python packages work. Every section provides direct, actionable content without padding. The method selection table is an excellent concise decision aid. | 3 / 3 |
Actionability | Every method includes executable, copy-paste ready Python code examples with real parameters. Key parameters are listed concisely for each method, and the research() example even includes the polling pattern needed for async operations. | 3 / 3 |
Workflow Clarity | The decision table for choosing methods is clear, and individual method usage is well-demonstrated. However, there are no validation checkpoints or error handling patterns shown (e.g., what happens if research() fails, how to handle rate limits, or what to do when extract() returns incomplete results). The research polling loop checks for 'failed' status but doesn't show what to do about it. | 2 / 3 |
Progressive Disclosure | Excellent progressive disclosure structure: the SKILL.md serves as a clear overview with quick-start examples, then consistently points to well-organized reference files (sdk.md, search.md, extract.md, crawl.md, research.md, integrations.md) with descriptive summaries of what each contains. All references are one level deep and clearly signaled. | 3 / 3 |
Total | 11 / 12 Passed |