customerio-performance-tuning
Optimize Customer.io API performance for high throughput. Use when improving response times, implementing connection pooling, batching, caching, or regional routing. Trigger: "customer.io performance", "optimize customer.io", "customer.io latency", "customer.io connection pooling".
84
82%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
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 strong skill description that clearly identifies its domain (Customer.io API performance), lists specific optimization techniques, and provides explicit trigger terms. It follows the recommended pattern with 'Use when' and 'Trigger' clauses, making it easy for Claude to select appropriately. Uses correct third-person voice throughout.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: improving response times, implementing connection pooling, batching, caching, and regional routing. These are clear, actionable capabilities. | 3 / 3 |
Completeness | Clearly answers both 'what' (optimize Customer.io API performance via connection pooling, batching, caching, regional routing) and 'when' (explicit 'Use when' clause and 'Trigger' terms). | 3 / 3 |
Trigger Term Quality | Includes explicit natural trigger terms like 'customer.io performance', 'optimize customer.io', 'customer.io latency', 'customer.io connection pooling' — these are phrases users would naturally say when seeking this skill. | 3 / 3 |
Distinctiveness Conflict Risk | Highly specific to Customer.io API performance optimization — a clear niche that is unlikely to conflict with general API skills or other Customer.io skills focused on different concerns. | 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 multiple performance optimization techniques. Its main weaknesses are length (the full LRU cache implementation and verbose code blocks could be externalized) and missing validation/verification steps between optimization stages. The skill would benefit from being restructured as a concise overview pointing to detailed implementation files.
Suggestions
Add validation checkpoints after each step, e.g., 'Verify connection reuse: log agent.freeSockets count' or 'Test batch flush: add 5 items and confirm they're sent within flushIntervalMs'
Move detailed implementations (LRU cache, batch processor class) to separate referenced files, keeping SKILL.md focused on the pattern and a minimal usage example for each technique
Remove the LRU cache implementation entirely — Claude can implement one when needed — and instead just show the dedupIdentify function with a comment like '// Use an LRU cache (max 10k entries, 5min TTL)'
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is mostly efficient with good code examples, but includes some unnecessary elements: the LRU cache implementation is something Claude could produce on its own, the performance targets table adds bulk without being directly actionable, and some comments are explanatory rather than essential. The regional routing section is fairly boilerplate. | 2 / 3 |
Actionability | Every step includes fully executable TypeScript code with proper imports, configuration, and usage examples. The code is copy-paste ready with clear file paths, concrete parameter values, and real SDK method calls. | 3 / 3 |
Workflow Clarity | Steps are clearly numbered and sequenced, but there are no validation checkpoints between steps. For performance optimization involving connection pooling, batching, and caching, there should be explicit verification steps (e.g., 'verify connection reuse with agent stats', 'confirm batch flush completes before shutdown'). The batch processor lacks retry logic for failed items. | 2 / 3 |
Progressive Disclosure | The skill references external resources (customerio-observability, customerio-cost-tuning) and includes links, which is good. However, the content is quite long (~200 lines of code) and could benefit from splitting the detailed implementations into separate files, keeping SKILL.md as an overview with quick-start patterns and links to full implementations. | 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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