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 well-crafted skill description that clearly identifies the 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, uses third person voice, and is concise without being vague.
| 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 — the combination of the specific platform (Customer.io) and the performance optimization domain makes it very unlikely to conflict with other skills. | 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 well-structured, highly actionable skill with complete, executable TypeScript examples for each optimization technique. Its main weaknesses are the lack of validation/verification steps between optimizations (how do you confirm pooling is working? how do you measure the improvement?) and the verbosity of including full implementations inline rather than splitting them into referenced files. The repeated TrackClient instantiation boilerplate across steps also adds unnecessary tokens.
Suggestions
Add explicit validation checkpoints after each step, e.g., 'Verify connection reuse by checking agent status: console.log(agent.status)' or 'Run the timedCioCall wrapper to confirm latency dropped below target'.
Extract the full code implementations (LRU cache, batch processor) into separate referenced files and keep SKILL.md focused on the technique overview, configuration values, and when to use each approach.
Consolidate the repeated TrackClient instantiation — reference the singleton from Step 1 in subsequent steps instead of re-creating it each time.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is mostly efficient with good code examples, but includes some unnecessary elements: the Prerequisites section is somewhat obvious, the Performance Targets table adds bulk without being directly actionable, and each step re-instantiates the TrackClient with the same boilerplate credentials pattern rather than referencing the singleton from Step 1. The LRU cache implementation is verbose for something Claude could generate on its own. | 2 / 3 |
Actionability | Every step provides fully executable TypeScript code with file paths, complete imports, and concrete configuration values. The code is copy-paste ready with clear comments explaining the 'why' behind each technique, and usage examples are included (e.g., the Express route in Step 4). | 3 / 3 |
Workflow Clarity | Steps are clearly numbered and sequenced, but there are no validation checkpoints between steps — no guidance on how to verify that connection pooling is actually working, that the dedup cache is hitting, or that batching improved throughput. The Performance Monitoring section exists but isn't integrated into the workflow as a verification step. For performance optimization (which can degrade systems if done wrong), explicit validation is important. | 2 / 3 |
Progressive Disclosure | The skill references external resources (customerio-observability, customerio-cost-tuning) and external docs, 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 technique summaries and links to full implementations. The inline LRU cache implementation is a prime candidate for extraction. | 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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