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 description with excellent trigger terms and completeness, clearly specifying both what the skill does and when to use it. Its main weakness is that the capability description is somewhat high-level—it could benefit from listing more concrete optimization techniques to better differentiate from a generic API performance skill. Overall, it performs well for skill selection purposes.
Suggestions
Add specific concrete actions like 'Implements caching strategies, optimizes pagination, manages rate limits, and batches API calls for Apollo.io' to improve specificity.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (Apollo.io API performance) and mentions some actions like 'improving API response times, reducing latency, optimizing bulk operations,' but doesn't list concrete specific techniques or actions (e.g., caching strategies, pagination tuning, rate limit handling). | 2 / 3 |
Completeness | Clearly answers both 'what' (optimize Apollo.io API performance) and 'when' (improving response times, reducing latency, optimizing bulk operations) with explicit trigger phrases listed. | 3 / 3 |
Trigger Term Quality | Includes a good range of natural trigger phrases users would actually say: 'apollo performance', 'optimize apollo', 'apollo slow', 'apollo latency', 'speed up apollo'. These cover common variations of how users would express performance concerns. | 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 the lack of validation/verification steps in the workflow (e.g., confirming cache hits, verifying bulk operation success) and the monolithic structure that packs substantial implementation detail into a single file. Minor verbosity in explanatory prose could be trimmed.
Suggestions
Add validation checkpoints: after enabling caching, verify with getCacheStats(); after bulk enrichment, validate result count matches input count; add error handling for partial batch failures.
Consider splitting the detailed implementations (cache.ts, bulk-ops.ts, benchmark.ts) into referenced bundle files, keeping SKILL.md as a concise overview with key patterns and links to full implementations.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is mostly efficient with good code examples, but includes some unnecessary explanations Claude would know (e.g., 'Reuse TCP connections to avoid TLS handshake overhead', explaining what LRU cache does, explaining that 2KB is larger than 200 bytes). The inline comments are helpful but the prose around them could be tighter. | 2 / 3 |
Actionability | Every step includes fully executable TypeScript code with proper imports, concrete configuration values, and real Apollo API endpoints. The code is copy-paste ready with specific library usage (axios, lru-cache, p-queue) and realistic parameters. | 3 / 3 |
Workflow Clarity | Steps are clearly sequenced and logically ordered (connection pooling → caching → bulk ops → parallel search → slimming → benchmarking). However, there are no validation checkpoints or feedback loops — no guidance on verifying that caching is working, that bulk operations succeeded, or how to handle partial failures in batch enrichment. | 2 / 3 |
Progressive Disclosure | The content is well-structured with clear sections, but it's quite long (~180 lines of code-heavy content) that could benefit from splitting implementation details into separate files. The reference to 'apollo-cost-tuning' in Next Steps is good, but the main body is monolithic with all implementation inline. | 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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