exa-reference-architecture
Implement Exa reference architecture for search pipelines, RAG, and content discovery. Use when designing new Exa integrations, reviewing project structure, or establishing architecture standards for neural search applications. Trigger with phrases like "exa architecture", "exa project structure", "exa RAG pipeline", "exa reference design", "exa search pipeline".
80
77%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./plugins/saas-packs/exa-pack/skills/exa-reference-architecture/SKILL.mdQuality
Discovery
89%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-structured skill description with strong completeness and distinctiveness. It clearly identifies when to use the skill and provides explicit trigger phrases. The main weakness is that the capability descriptions are somewhat high-level—listing categories like 'search pipelines' and 'RAG' rather than specific concrete actions the skill performs.
Suggestions
Add more specific concrete actions beyond category names, e.g., 'define folder structure, configure search endpoints, set up RAG retrieval chains, implement content filtering logic'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (Exa reference architecture) and some actions (search pipelines, RAG, content discovery), but the actions are more like categories than concrete specific tasks. It doesn't list granular actions like 'configure endpoints', 'set up vector stores', or 'define schema'. | 2 / 3 |
Completeness | Clearly answers both 'what' (implement Exa reference architecture for search pipelines, RAG, and content discovery) and 'when' (designing new Exa integrations, reviewing project structure, establishing architecture standards) with explicit trigger phrases. | 3 / 3 |
Trigger Term Quality | Explicitly lists natural trigger phrases like 'exa architecture', 'exa project structure', 'exa RAG pipeline', 'exa reference design', 'exa search pipeline'. These are specific and likely match what users would say. Also includes terms like 'neural search applications' and 'content discovery'. | 3 / 3 |
Distinctiveness Conflict Risk | The description is highly specific to Exa as a product/platform, with distinct trigger terms all prefixed with 'exa'. This makes it very unlikely to conflict with generic search or RAG skills. | 3 / 3 |
Total | 11 / 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 reference architecture with excellent executable TypeScript examples covering the full Exa API surface. Its main weaknesses are the lack of validation/verification steps in the multi-phase workflows and the monolithic structure that could benefit from splitting detailed implementations into separate files. The error handling table is helpful but should be integrated into the workflow as explicit checkpoints rather than listed separately.
Suggestions
Add explicit validation checkpoints in the research pipeline (e.g., check for empty results after Phase 1 before proceeding, verify API response status codes) to create proper feedback loops for error recovery.
Split detailed implementation patterns (domain profiles, competitor discovery, research pipeline) into separate referenced files, keeping SKILL.md as a concise overview with the architecture diagram and service layer setup.
Add a retry/error handling wrapper around API calls that implements the solutions mentioned in the error handling table, rather than listing them separately from the code.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is mostly efficient with executable code examples, but includes some unnecessary bulk—the architecture diagram is quite large, and some patterns (like competitor discovery) feel like extras that inflate the content without being core to the reference architecture. The domain profiles with hardcoded domain lists add token weight that could be more concise. | 2 / 3 |
Actionability | Every step provides fully executable TypeScript code with proper imports, typed interfaces, and realistic API usage patterns. The code is copy-paste ready and covers the full range of Exa API capabilities including search, findSimilar, RAG formatting, and answer endpoints. | 3 / 3 |
Workflow Clarity | Steps are clearly sequenced (service layer → research pipeline → RAG → profiles → discovery), but there are no validation checkpoints or error recovery feedback loops. The research pipeline has no handling for empty results in Phase 1 beyond a ternary, and there's no guidance on verifying API responses or retrying failed requests despite the error handling table mentioning rate limits. | 2 / 3 |
Progressive Disclosure | The content has reasonable section structure and references external docs and a variants file, but the main file is quite long with all implementation details inline. The domain profiles and competitor discovery could be split into separate reference files, with the SKILL.md serving as a leaner overview pointing to them. | 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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