exa-architecture-variants
Choose and implement Exa architecture patterns at different scales: direct search, cached search, and RAG pipeline. Use when designing Exa integrations, choosing between simple search and full RAG, or planning architecture for different traffic volumes. Trigger with phrases like "exa architecture", "exa blueprint", "how to structure exa", "exa RAG design", "exa at scale".
84
82%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Advisory
Suggest reviewing before use
Quality
Discovery
100%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a well-crafted skill description that clearly defines its scope around Exa architecture pattern selection and implementation. It excels in all dimensions: specific capabilities are enumerated, natural trigger terms are provided, both 'what' and 'when' are explicitly addressed, and the Exa-specific focus makes it highly distinctive. The description uses proper third-person voice throughout.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'Choose and implement Exa architecture patterns at different scales: direct search, cached search, and RAG pipeline.' This names three distinct patterns and the decision-making process. | 3 / 3 |
Completeness | Clearly answers both what ('Choose and implement Exa architecture patterns at different scales: direct search, cached search, and RAG pipeline') and when ('Use when designing Exa integrations, choosing between simple search and full RAG, or planning architecture for different traffic volumes') with explicit trigger phrases. | 3 / 3 |
Trigger Term Quality | Includes natural trigger terms users would say: 'exa architecture', 'exa blueprint', 'how to structure exa', 'exa RAG design', 'exa at scale'. Also includes domain terms like 'RAG pipeline', 'cached search', 'traffic volumes' that users would naturally mention. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with a clear niche around Exa-specific architecture patterns. The combination of 'Exa' with architecture/blueprint/RAG design terms makes it very unlikely to conflict with generic search or RAG skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
64%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a solid, actionable skill with excellent executable code examples covering three distinct architecture patterns. The decision matrix is a strong feature for guiding selection. Main weaknesses are the lack of validation/verification steps in workflows (especially for the RAG pipeline which involves multiple chained API calls), and the monolithic structure that could benefit from splitting variants into separate referenced files.
Suggestions
Add explicit validation checkpoints to the RAG pipeline workflow, such as verifying API connectivity, checking that search results are non-empty before expanding context, and validating the formatted LLM context.
Split the three variant implementations into separate referenced files (e.g., direct-search.md, cached-search.md, rag-pipeline.md) and keep SKILL.md as a concise overview with the decision matrix and links.
Add a brief error recovery loop in the RAG pipeline's research() method showing how to handle cases where gatherContext or expandContext fails mid-pipeline.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is mostly efficient with good use of tables and code, but includes some unnecessary elements like the 'Natural limit: ~864K searches/day at full rate' calculation and the Resources section linking to docs Claude likely knows. The decision matrix and error handling tables are well-structured but could be slightly tighter. | 2 / 3 |
Actionability | All three variants provide fully executable TypeScript code with real Exa SDK methods, proper imports, concrete configuration objects, and copy-paste ready implementations. The code examples are complete and use actual API parameters with realistic values. | 3 / 3 |
Workflow Clarity | The decision matrix clearly guides architecture selection, and the RAG pipeline has labeled phases (1-4). However, there are no explicit validation checkpoints—no steps to verify API keys work, no error recovery loops for failed searches in the RAG pipeline, and no guidance on testing/validating the chosen architecture before deployment. | 2 / 3 |
Progressive Disclosure | The content is well-structured with clear sections and a decision matrix as an overview, but the three full code implementations inline make it quite long. The variants could be split into separate files with the SKILL.md serving as the overview. The reference to 'exa-reference-architecture' at the end is vague and there are no bundle files to support it. | 2 / 3 |
Total | 9 / 12 Passed |
Validation
81%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 9 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 9 / 11 Passed | |
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