apollo-performance-tuning
Optimize Apollo.io API performance. Use when improving API response times, reducing latency, or optimizing bulk operations. Trigger with phrases like "apollo performance", "optimize apollo", "apollo slow", "apollo latency", "speed up apollo".
80
77%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./plugins/saas-packs/apollo-pack/skills/apollo-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 solid skill description with excellent trigger terms and completeness, clearly specifying both what it does and when to use it. Its main weakness is that the capabilities described are somewhat general (improving response times, reducing latency, optimizing bulk operations) rather than listing specific concrete optimization techniques. The explicit trigger phrases are a strong addition that aids skill selection.
Suggestions
Add more specific concrete actions such as 'implements caching strategies, optimizes pagination, manages rate limits, batches API calls' to improve specificity.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (Apollo.io API performance) and some actions (improving response times, reducing latency, optimizing bulk operations), but doesn't list multiple concrete specific actions like caching strategies, pagination tuning, or rate limit handling. | 2 / 3 |
Completeness | Clearly answers both 'what' (optimize Apollo.io API performance) and 'when' (explicit 'Use when' clause with triggers for improving response times, reducing latency, optimizing bulk operations, plus explicit trigger phrases). | 3 / 3 |
Trigger Term Quality | Includes natural keywords users would actually say: 'apollo performance', 'optimize apollo', 'apollo slow', 'apollo latency', 'speed up apollo'. These are realistic phrases a user would type when experiencing performance issues with Apollo.io. | 3 / 3 |
Distinctiveness Conflict Risk | Highly specific to Apollo.io API performance optimization, which is a clear niche. The combination of 'Apollo.io' and 'performance/latency' makes it very 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, highly actionable skill with excellent executable code examples covering multiple Apollo API optimization techniques. Its main weaknesses are length (could be more concise by extracting detailed implementations to reference files) and the lack of explicit validation/verification steps between operations. The error handling table is a nice touch but the workflow would benefit from inline checkpoints.
Suggestions
Add validation checkpoints between steps, e.g., 'Verify connection pooling is active by checking getCacheStats() output' or 'Confirm bulk endpoint returns expected matches.length before proceeding to parallel search'.
Consider extracting the detailed code implementations into a separate reference file (e.g., APOLLO-PERF-REFERENCE.md) and keeping SKILL.md as a concise overview with the key insight, quick-start pattern, and pointers to the full implementations.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is mostly efficient with good code examples, but includes some unnecessary commentary (e.g., explaining what connection pooling does, 'Massive performance gain', the math showing 100 vs 10 calls). The overview sentence about search vs enrichment is genuinely useful insight, but some inline comments are redundant for Claude. | 2 / 3 |
Actionability | Every step includes fully executable TypeScript code with proper imports, concrete configurations (maxSockets, TTL values, batch sizes), and real Apollo API endpoints. The code is copy-paste ready with realistic patterns like p-queue concurrency control and LRU cache setup. | 3 / 3 |
Workflow Clarity | Steps are clearly sequenced and logically ordered (connection pooling → caching → bulk ops → parallel search → slimming → benchmarking), but there are no explicit validation checkpoints or feedback loops. For operations involving API rate limits and caching, there should be verification steps (e.g., validate cache is working, confirm rate limits aren't being hit before scaling concurrency). | 2 / 3 |
Progressive Disclosure | The content is well-structured with clear sections and a reference to 'apollo-cost-tuning' for next steps, plus external resource links. However, the skill is quite long (~180 lines of content) and could benefit from splitting detailed code implementations into separate reference files while keeping the SKILL.md as a concise overview with pointers. | 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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