documenso-performance-tuning
Optimize Documenso integration performance with caching, batching, and efficient patterns. Use when improving response times, reducing API calls, or optimizing bulk document operations. Trigger with phrases like "documenso performance", "optimize documenso", "documenso caching", "documenso batch operations".
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/documenso-pack/skills/documenso-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 skill description that clearly identifies its niche (Documenso performance optimization), provides explicit trigger guidance, and answers both what and when. Its main weakness is that the specific capabilities could be more concrete—terms like 'efficient patterns' are vague, and listing more specific actions (e.g., 'implement request caching', 'batch API calls', 'deduplicate requests') would strengthen it.
Suggestions
Replace vague 'efficient patterns' with specific concrete actions like 'implement request deduplication', 'add pagination for large result sets', or 'configure cache TTLs'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (Documenso integration performance) and mentions some techniques (caching, batching, efficient patterns), but doesn't list multiple concrete actions—'optimize' and 'efficient patterns' are somewhat vague and don't describe specific steps like 'implement request deduplication' or 'add response caching layers'. | 2 / 3 |
Completeness | Clearly answers both 'what' (optimize Documenso integration with caching, batching, efficient patterns) and 'when' (improving response times, reducing API calls, optimizing bulk operations) with explicit trigger phrases provided. | 3 / 3 |
Trigger Term Quality | Includes explicit trigger phrases like 'documenso performance', 'optimize documenso', 'documenso caching', 'documenso batch operations', plus natural terms like 'response times', 'reducing API calls', and 'bulk document operations' that users would naturally say. | 3 / 3 |
Distinctiveness Conflict Risk | Highly specific to Documenso performance optimization, which is a narrow niche unlikely to conflict with other skills. The combination of 'Documenso' + 'performance/caching/batching' creates very distinct triggers. | 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, actionable skill with excellent executable code examples covering multiple performance optimization strategies. Its main weaknesses are the length (could split detailed implementations into referenced files) and the lack of explicit validation/verification steps in the batch operations workflow. The error handling and performance targets tables are valuable additions.
Suggestions
Add explicit validation checkpoints to the batch operations workflow, e.g., 'Verify results array for errors before proceeding' and 'Log/review failed items and decide whether to retry'
Consider moving the full implementation code for caching, batching, and queue processing into separate referenced files, keeping only concise patterns and key concepts in the main SKILL.md
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is mostly efficient with good code examples, but includes some unnecessary verbosity—the inline comments numbering API calls are helpful, but the overall length is substantial. The 'WITHOUT templates' example is useful for contrast but adds significant tokens. Some explanatory text like 'The biggest performance win:' could be trimmed. | 2 / 3 |
Actionability | All code examples are fully executable TypeScript with concrete imports, real API calls, proper error handling, and complete function signatures. The examples are copy-paste ready with specific library usage (p-queue, Bull, node-cache) and realistic patterns. | 3 / 3 |
Workflow Clarity | Steps are clearly numbered and sequenced, but there are no validation checkpoints or feedback loops for what are essentially batch/destructive operations. Step 3 (batch operations) handles errors via Promise.allSettled but lacks explicit guidance on verifying results or retrying failures. The performance targets table helps but doesn't constitute validation steps within the workflow. | 2 / 3 |
Progressive Disclosure | The content is well-structured with clear sections and tables, but it's quite long (~200 lines of code) and could benefit from splitting detailed implementations into separate files. The references to external docs and 'documenso-cost-tuning' are good, but the caching, batching, and queue implementations could each be separate referenced files with just patterns shown 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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