exa-performance-tuning
Optimize Exa API performance with search type selection, caching, and parallelization. Use when experiencing slow responses, implementing caching strategies, or optimizing request throughput for Exa integrations. Trigger with phrases like "exa performance", "optimize exa", "exa latency", "exa caching", "exa slow", "exa fast".
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-performance-tuning/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 that clearly identifies its niche (Exa API performance optimization) and provides explicit trigger guidance. Its main weakness is that the capability actions could be slightly more concrete — for example, specifying what kinds of caching strategies or what parallelization techniques are covered. Overall, it's a strong description that would perform well in skill selection.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (Exa API performance) and some actions (search type selection, caching, parallelization), but these are somewhat high-level and not fully fleshed out as concrete actions (e.g., doesn't specify what 'search type selection' entails or what parallelization looks like in practice). | 2 / 3 |
Completeness | Clearly answers both 'what' (optimize Exa API performance with search type selection, caching, and parallelization) and 'when' (experiencing slow responses, implementing caching strategies, optimizing request throughput) with explicit trigger phrases listed. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural trigger terms including 'exa performance', 'optimize exa', 'exa latency', 'exa caching', 'exa slow', 'exa fast', plus contextual phrases like 'slow responses' and 'request throughput'. These are terms users would naturally use when experiencing performance issues with Exa. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive — targets a very specific niche (Exa API performance optimization) with domain-specific trigger terms that are unlikely to conflict with other skills. The combination of 'Exa' + performance-related terms creates a clear, unique identity. | 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 multiple performance optimization strategies for the Exa API. Its main weaknesses are the lack of validation/measurement steps to confirm optimizations are working and some verbosity in code comments that restate what the code already shows. The logical step-by-step progression is good but would benefit from explicit checkpoints.
Suggestions
Add a validation/measurement step (e.g., timing wrapper function or logging) so users can verify that optimizations actually meet their latency budget before and after changes.
Trim redundant code comments that restate what's obvious from the code itself (e.g., '// No text or summary — saves content retrieval time') to improve conciseness.
Consider adding a brief benchmarking checkpoint after Step 1-2 to create a feedback loop: measure baseline latency, apply optimization, re-measure, adjust if needed.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is mostly efficient with good code examples and tables, but includes some unnecessary commentary in code comments (e.g., explaining what 'no content retrieval' means, '// Cache hit: 0ms vs 500-2000ms') and the performance comparison table partially duplicates information already conveyed in the code examples. The latency table and error handling table are useful but could be slightly tighter. | 2 / 3 |
Actionability | All code examples are fully executable TypeScript with proper imports, concrete function signatures, and copy-paste ready patterns. The progression from search type selection through caching, parallelization, two-phase search, and query normalization provides specific, implementable solutions with real library calls. | 3 / 3 |
Workflow Clarity | The steps are clearly sequenced and logically ordered from simplest optimization to more complex patterns. However, there are no explicit validation checkpoints — no guidance on measuring whether optimizations actually improved latency, no benchmarking step, and no feedback loop for verifying cache hit rates or confirming the selected search type meets the latency budget in practice. | 2 / 3 |
Progressive Disclosure | The content is well-structured with clear sections and tables, and references 'exa-cost-tuning' and 'exa-reliability-patterns' for related topics. However, at ~150 lines the skill is somewhat long for a single file — the detailed code examples for caching, parallelization, and two-phase search could potentially be split into referenced files. The external resource links are helpful but the bundle has no supporting files. | 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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