gamma-performance-tuning
Optimize Gamma API performance and reduce latency. Use when experiencing slow response times, optimizing throughput, or improving user experience with Gamma integrations. Trigger with phrases like "gamma performance", "gamma slow", "gamma latency", "gamma optimization", "gamma speed".
73
68%
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/gamma-pack/skills/gamma-performance-tuning/SKILL.mdQuality
Discovery
72%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
The description has strong trigger term coverage and completeness with explicit 'Use when' and 'Trigger with' clauses. However, it is weak on specificity—it describes goals (optimize, reduce latency) rather than concrete actions (e.g., implement caching, batch API calls, configure rate limiting). The Gamma-specific framing provides some distinctiveness but the vague capability description limits its overall quality.
Suggestions
Replace vague goals like 'optimize performance and reduce latency' with specific concrete actions such as 'implement response caching, configure connection pooling, batch API requests, tune rate limits'.
Add specific Gamma API concepts or features that this skill addresses (e.g., specific endpoints, pagination strategies, webhook optimization) to improve both specificity and distinctiveness.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description says 'optimize performance and reduce latency' but these are vague goals, not concrete actions. It doesn't list specific techniques, tools, or steps (e.g., caching strategies, connection pooling, batch requests). 'Optimize' and 'reduce latency' are abstract. | 1 / 3 |
Completeness | Clearly answers both 'what' (optimize Gamma API performance and reduce latency) and 'when' (experiencing slow response times, optimizing throughput, improving UX with Gamma integrations), with explicit trigger phrases provided. | 3 / 3 |
Trigger Term Quality | Explicitly lists natural trigger phrases like 'gamma performance', 'gamma slow', 'gamma latency', 'gamma optimization', 'gamma speed'. These are terms users would naturally say when experiencing performance issues with Gamma integrations. | 3 / 3 |
Distinctiveness Conflict Risk | The 'Gamma API' specificity helps distinguish it, but 'performance optimization' and 'reduce latency' are generic enough to overlap with general API optimization or performance tuning skills. The Gamma-specific trigger terms help but the actions themselves are not distinctive. | 2 / 3 |
Total | 9 / 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 performance tuning guide with excellent executable code examples and useful reference tables. Its main weaknesses are the lack of explicit validation/measurement checkpoints in the workflow (no 'measure baseline → apply optimization → verify improvement' loop) and the monolithic structure that could benefit from splitting detailed implementations into separate files. The content is slightly verbose in places but generally well-organized.
Suggestions
Add explicit validation checkpoints: include a 'Step 0: Measure baseline' and integrate verification steps after each optimization (e.g., 'Run benchmark, compare poll count before/after adaptive polling').
Split detailed code implementations (cache.ts, batch.ts, optimized-client.ts) into separate bundle files and reference them from SKILL.md to improve progressive disclosure and reduce the main file length.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is mostly efficient with good use of tables and code, but includes some unnecessary verbosity—inline comments explaining obvious things (e.g., '// 1 hour for static data'), the overview restating what the table already shows, and the prerequisites section mentioning 'understanding of async patterns' which Claude already has. Could be tightened by ~20%. | 2 / 3 |
Actionability | Every step provides fully executable TypeScript code with concrete implementations—adaptive polling, caching, batch generation with concurrency limits, connection keep-alive. Code is copy-paste ready with specific libraries (node-cache, p-limit) and realistic patterns. The performance targets table gives specific thresholds with remediation actions. | 3 / 3 |
Workflow Clarity | Steps are clearly sequenced and logically ordered (poll optimization → caching → batching → parameter tuning → preloading → connection optimization). However, there are no explicit validation checkpoints or feedback loops—no step says 'measure before and after' or 'verify cache hit rates' or 'if latency exceeds target, do X.' The performance targets table partially compensates but isn't integrated into the workflow steps. | 2 / 3 |
Progressive Disclosure | The content is well-structured with clear sections and tables, but at ~200 lines it's quite long for a single file with no bundle support. The caching, batch generation, and connection optimization code could each be separate referenced files. References to external resources and related skills (gamma-sdk-patterns, gamma-cost-tuning) are present but the main content is monolithic. | 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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