groq-performance-tuning
Optimize Groq API performance with model selection, caching, streaming, and parallel requests. Use when experiencing slow responses, implementing caching strategies, or optimizing request throughput for Groq integrations. Trigger with phrases like "groq performance", "optimize groq", "groq latency", "groq caching", "groq slow", "groq speed".
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 defines its scope (Groq API performance optimization), lists specific techniques, provides explicit 'Use when' guidance, and includes natural trigger phrases. It uses proper third-person voice and is concise without being vague. The only minor note is that it could potentially mention specific Groq model names for even richer trigger coverage, but this is a strong description overall.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: model selection, caching, streaming, and parallel requests. These are distinct, actionable optimization techniques rather than vague language. | 3 / 3 |
Completeness | Clearly answers both 'what' (optimize Groq API performance with model selection, caching, streaming, parallel requests) and 'when' (experiencing slow responses, implementing caching strategies, optimizing request throughput) with explicit trigger phrases. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural trigger terms including 'groq performance', 'optimize groq', 'groq latency', 'groq caching', 'groq slow', 'groq speed'. These are terms users would naturally use when experiencing performance issues with Groq. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive due to the specific focus on Groq API optimization. The 'groq' keyword throughout makes it very unlikely to conflict with general performance optimization or other API 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 solid, actionable skill with excellent executable code examples and a useful decision matrix. Its main weaknesses are the lack of validation/verification steps integrated into the workflow and some verbosity in explaining concepts Claude already knows (like why fewer tokens are better). The content would benefit from being split across files given its length.
Suggestions
Add validation checkpoints to the workflow, e.g., after Step 4 (cache), verify cache hit rate; after Step 5 (parallel), verify no 429 errors are occurring; integrate the error handling table into the relevant steps rather than as a separate section.
Remove the BAD/GOOD comparison in Step 2 — Claude understands token efficiency. Replace with just the concise guidance: 'Minimize system prompts and set max_tokens to expected output size.'
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is mostly efficient with good code examples, but includes some unnecessary verbosity: the benchmark table with approximate values that may become stale, the BAD/GOOD comparison in Step 2 explains token efficiency concepts Claude already understands, and some inline comments are redundant. The overall length (~180 lines) is reasonable for the scope but could be tightened. | 2 / 3 |
Actionability | All code examples are fully executable TypeScript with proper imports, concrete model names, and copy-paste ready implementations. The decision matrix provides specific, actionable guidance for choosing configurations. Each step includes real, runnable code rather than pseudocode. | 3 / 3 |
Workflow Clarity | Steps are clearly numbered and sequenced, and the decision matrix helps with selection. However, there are no validation checkpoints or feedback loops — for example, no verification that caching is working correctly, no check that rate limiting is properly configured, and no guidance on what to do if benchmark results are unexpected. The error handling table is helpful but reactive rather than integrated into the workflow. | 2 / 3 |
Progressive Disclosure | The content is well-structured with clear sections and a logical flow from model selection to benchmarking. However, the skill is quite long and monolithic — the detailed code for caching, streaming, and parallel requests could be split into separate reference files. The 'Next Steps' reference to 'groq-cost-tuning' is good, but there are no bundle files to support progressive disclosure. | 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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