exa-data-handling
Implement Exa search result processing, content extraction, caching, and RAG context management. Use when handling search results, implementing caching, building citation pipelines, or managing content payloads for LLM context windows. Trigger with phrases like "exa data", "exa results processing", "exa cache", "exa RAG context", "exa content extraction".
81
78%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Advisory
Suggest reviewing before use
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npx tessl skill review --optimize ./plugins/saas-packs/exa-pack/skills/exa-data-handling/SKILL.mdQuality
Discovery
85%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 that clearly defines its scope around Exa-specific search result processing and RAG context management. It excels at completeness with explicit 'Use when' and 'Trigger with' clauses, and the Exa branding provides strong distinctiveness. The main weakness is that the trigger terms feel somewhat artificial and may not match how users naturally phrase requests, particularly if they don't use the 'exa' prefix in every query.
Suggestions
Add more natural user-facing trigger terms that don't require the 'exa' prefix, such as 'search result caching', 'citation pipeline', or 'context window management', to improve matching when users describe the task without naming the tool.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'search result processing', 'content extraction', 'caching', 'RAG context management', 'citation pipelines', and 'managing content payloads for LLM context windows'. | 3 / 3 |
Completeness | Clearly answers both 'what' (implement Exa search result processing, content extraction, caching, RAG context management) and 'when' (explicit 'Use when' clause covering search results, caching, citation pipelines, content payloads, plus explicit trigger phrases). | 3 / 3 |
Trigger Term Quality | Includes explicit trigger phrases like 'exa data', 'exa results processing', 'exa cache', 'exa RAG context', 'exa content extraction', but these are fairly specific to the Exa brand and may miss natural user phrasing like 'search results caching', 'process search results', or 'citation management'. The triggers feel somewhat manufactured rather than naturally occurring user language. | 2 / 3 |
Distinctiveness Conflict Risk | The 'Exa' qualifier creates a clear niche that distinguishes this from generic search, caching, or RAG skills. The specific trigger terms are all prefixed with 'exa', making conflicts with other skills unlikely. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
72%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 executable TypeScript examples covering the full Exa data handling pipeline. Its main weakness is the lack of validation/verification steps within the workflow (e.g., checking API response status, handling empty results) and slight verbosity in the content extraction tier examples. The progressive disclosure and error reference table are well done.
Suggestions
Add validation checkpoints within the workflow, such as checking for empty results after search, verifying cache integrity, and handling API error responses before proceeding to the next step.
Consolidate the four content extraction tier functions into a single parameterized function with a tier/options table to reduce verbosity while preserving clarity.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is mostly efficient with good code examples, but includes some unnecessary elements like the 'Prerequisites' section mentioning 'Understanding of Exa content options' and inline comments that explain obvious things. The tiered approach in Step 1 is slightly verbose with four separate functions when a single parameterized example with a comment table would suffice. | 2 / 3 |
Actionability | All code examples are fully executable TypeScript with proper imports, concrete function signatures, and real API calls. The examples are copy-paste ready and cover the complete pipeline from search to RAG context assembly. | 3 / 3 |
Workflow Clarity | Steps are clearly sequenced and logically ordered from extraction to caching to RAG processing. However, there are no validation checkpoints — no verification that API responses are valid, no error handling within the workflow steps, and no feedback loops for when caching or deduplication produces unexpected results. | 2 / 3 |
Progressive Disclosure | Content is well-structured with clear sections progressing from basic to advanced. External references are one level deep (exa-rate-limits, exa-cost-tuning, Exa docs), clearly signaled in 'Resources' and 'Next Steps' sections. The error handling table provides quick-reference without bloating the main content. | 3 / 3 |
Total | 10 / 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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