fireflies-performance-tuning
Optimize Fireflies.ai GraphQL query performance with field selection, caching, and batching. Use when experiencing slow API responses, implementing caching, or optimizing transcript processing throughput. Trigger with phrases like "fireflies performance", "optimize fireflies", "fireflies latency", "fireflies caching", "fireflies slow", "fireflies batch".
84
82%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Advisory
Suggest reviewing before use
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 (Fireflies.ai GraphQL optimization), lists concrete actions (field selection, caching, batching), provides explicit 'Use when' guidance, and includes natural trigger phrases. It uses proper third-person voice and is concise without being vague. It serves as a strong example of a high-quality skill description.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'field selection, caching, and batching' for GraphQL query performance optimization. Also mentions 'transcript processing throughput' as a specific domain action. | 3 / 3 |
Completeness | Clearly answers both 'what' (optimize Fireflies.ai GraphQL query performance with field selection, caching, and batching) and 'when' (slow API responses, implementing caching, optimizing transcript processing throughput) with explicit trigger phrases. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural trigger terms explicitly listed: 'fireflies performance', 'optimize fireflies', 'fireflies latency', 'fireflies caching', 'fireflies slow', 'fireflies batch'. These are phrases users would naturally say when encountering these issues. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive — targets a very specific niche (Fireflies.ai GraphQL API optimization). The combination of 'Fireflies.ai' and 'GraphQL query performance' makes it extremely 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 solid, actionable skill with excellent concrete code examples and a logical progression from simple to complex optimizations. Its main weaknesses are the lack of validation/verification steps in the workflow (e.g., how to confirm optimizations are working) and the monolithic structure that could benefit from splitting detailed implementations into referenced files. Some content is mildly redundant (LRU vs Redis examples share significant structure).
Suggestions
Add validation checkpoints: e.g., 'Log response size before/after field selection to verify improvement' or 'Add a cache hit rate counter to confirm caching is effective'.
Consider splitting Redis caching (Step 3) and batch processing (Step 4) into separate referenced files, keeping SKILL.md as a concise overview with the field selection example inline.
Consolidate the Redis and LRU cache examples — show one inline and reference the other as an alternative pattern, reducing redundancy.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is mostly efficient with good code examples, but includes some unnecessary elements: the 'Prerequisites' section is somewhat obvious, the 'Output' section restates what was already covered, and the Redis example (Step 3) is very similar to the LRU example (Step 2) — one could reference the other. The benchmarks table adds value but the error handling table partially repeats earlier guidance. | 2 / 3 |
Actionability | Every step includes fully executable TypeScript code with concrete examples. The BAD/GOOD pattern in Step 1 is particularly effective. Code is copy-paste ready with real library imports (lru-cache, p-queue, ioredis), specific rate limit values, and complete function implementations. | 3 / 3 |
Workflow Clarity | Steps are clearly sequenced from simplest win (field selection) to more advanced (webhook pre-caching, pagination), which is logical. However, there are no validation checkpoints — no guidance on verifying that field selection actually reduced response size, no way to confirm cache is working correctly, and no error recovery loops for failed batch operations beyond the error table. | 2 / 3 |
Progressive Disclosure | The content is well-structured with clear headers and a logical progression, but it's quite long (~150 lines of code) and could benefit from splitting detailed implementations (Redis caching, batch processing) into separate reference files. The 'Next Steps' reference to 'fireflies-cost-tuning' is good, but no bundle files exist to support further disclosure. For a skill of this complexity, more content should be offloaded. | 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 | |
- Commit
3a2d27d
- Reviewed
Table of Contents
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