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".
62
75%
Does it follow best practices?
Impact
—
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 identifies its domain (Exa search ecosystem), lists concrete capabilities, and provides both 'Use when' guidance and explicit trigger phrases. Its main weakness is that the trigger terms are somewhat rigid and brand-prefixed, which may not match how users naturally phrase requests when they don't know the skill name. Overall it's a strong description that would perform well in skill selection.
Suggestions
Add more natural user-facing trigger terms beyond the 'exa' prefix, such as 'search result caching', 'citation pipeline', 'context window management', or 'search content extraction' to capture users who may not use the Exa brand name explicitly.
| 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) with additional 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', 'citation management', or 'context window optimization'. The triggers are somewhat formulaic rather than reflecting how users naturally speak. | 2 / 3 |
Distinctiveness Conflict Risk | The 'Exa' branding creates a clear niche that distinguishes this from generic search or caching skills. The combination of Exa-specific triggers with RAG context management and citation pipelines makes it unlikely to conflict with other 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 skill with excellent executable code examples covering a comprehensive Exa data handling workflow. Its main weaknesses are the lack of explicit validation/error recovery steps in the workflow (particularly important for caching and API operations) and moderate verbosity — the tiered content extraction section is thorough but could be more compact. The progressive disclosure is adequate but would benefit from bundle files for the more detailed code examples.
Suggestions
Add explicit validation checkpoints: verify API responses before caching (e.g., check for error fields), validate cache hits are structurally sound, and add a try/catch with retry logic around API calls.
Tighten the content extraction tiers — consider a compact table showing tier name, method, options, and tradeoffs instead of four separate full function definitions.
Move detailed code examples (e.g., the full tiered extraction functions) into a bundle reference file like CONTENT_TIERS.md, keeping only the most common pattern inline.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is mostly efficient with executable code examples, but includes some unnecessary elements like the 'Prerequisites' section mentioning understanding of Exa content options (Claude knows this), and the 'Overview' paragraph restates what the title and structure already convey. The tiered content extraction section could be more compact. | 2 / 3 |
Actionability | All code examples are fully executable TypeScript with proper imports, concrete function signatures, and realistic usage patterns. The error handling table maps specific issues to specific solutions. The final RAG pipeline example ties all pieces together into a copy-paste-ready function. | 3 / 3 |
Workflow Clarity | Steps are clearly sequenced and logically ordered from content extraction through caching, token management, deduplication, and summary extraction. However, there are no explicit validation checkpoints — no verification that cache entries are valid, no check that token budget fitting actually produced usable results, and no error recovery loops for API failures. | 2 / 3 |
Progressive Disclosure | The content is well-structured with clear sections and a helpful error handling table. References to 'exa-rate-limits' and 'exa-cost-tuning' in Next Steps provide some progressive disclosure, and external doc links are included. However, the skill is quite long (~180 lines of content) and some sections like the full tiered extraction examples could be split into a reference file, especially since no bundle files exist to support this. | 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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