firecrawl-performance-tuning
Optimize Firecrawl scraping performance with caching, batch scraping, and format selection. Use when experiencing slow scrapes, optimizing credit usage per page, or building high-throughput scraping pipelines. Trigger with phrases like "firecrawl performance", "optimize firecrawl", "firecrawl latency", "firecrawl caching", "firecrawl slow", "firecrawl batch".
84
82%
Does it follow best practices?
Impact
—
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 an excellent skill description that hits all the marks. It provides specific capabilities (caching, batch scraping, format selection), clear use-case triggers (slow scrapes, credit optimization, high-throughput pipelines), and explicit trigger phrases. The description is concise, uses third person voice, and is highly distinctive due to the Firecrawl-specific focus combined with performance optimization context.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: caching, batch scraping, and format selection. Also mentions specific outcomes like optimizing credit usage per page and building high-throughput scraping pipelines. | 3 / 3 |
Completeness | Clearly answers both 'what' (optimize Firecrawl scraping with caching, batch scraping, format selection) and 'when' (experiencing slow scrapes, optimizing credit usage, building high-throughput pipelines) with an explicit 'Use when' clause and trigger phrases. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural trigger terms explicitly listed: 'firecrawl performance', 'optimize firecrawl', 'firecrawl latency', 'firecrawl caching', 'firecrawl slow', 'firecrawl batch'. Also includes natural phrases like 'slow scrapes' and 'credit usage' that users would say. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive with a clear niche: Firecrawl-specific performance optimization. The tool name 'Firecrawl' combined with performance-specific terms like 'caching', 'batch', 'latency' make 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 solid, actionable skill with excellent concrete code examples that clearly demonstrate performance optimization patterns. Its main weaknesses are the lack of explicit validation/feedback loops in the workflow (e.g., 'measure baseline → apply optimization → verify improvement') and moderate verbosity that could be tightened by moving some examples to bundle files. The anti-pattern/pattern comparison approach is effective for teaching.
Suggestions
Add an explicit validation loop: measure baseline performance first, apply optimizations, then re-measure and compare — integrate Step 6 as a before/after checkpoint rather than a standalone step.
Move the caching implementation and performance comparison script into separate bundle files (e.g., `cached-scrape.ts`, `perf-benchmark.ts`) and reference them from the main skill to improve progressive disclosure.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is mostly efficient with good code examples, but includes some unnecessary elements like the latency benchmarks table (which are approximate and time-sensitive), inline comments that over-explain, and the 'Typical savings: 50-80%' comment. The error handling table adds value but some entries repeat guidance already shown in the steps. | 2 / 3 |
Actionability | Every step includes fully executable TypeScript code with concrete examples showing both the slow/wrong approach and the fast/correct approach. Code is copy-paste ready with real imports, real API calls, and practical patterns like LRU caching and timed scraping. | 3 / 3 |
Workflow Clarity | Steps are clearly sequenced and logically ordered from biggest wins to measurement, but there are no explicit validation checkpoints or feedback loops. For a performance optimization workflow, there should be a 'measure before and after' validation step integrated into the workflow rather than just appended as Step 6. | 2 / 3 |
Progressive Disclosure | The content is well-structured with clear sections and a logical flow, but at ~150 lines it's fairly long for a single file with no bundle support. The caching implementation and performance comparison script could be split into referenced files. External resource links are provided but there's no bundle to offload detailed content to. | 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
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