klingai-rate-limits
Handle Kling AI API rate limits with backoff and queuing strategies. Use when hitting 429 errors or planning high-volume workflows. Trigger with phrases like 'klingai rate limit', 'kling ai 429', 'klingai throttle', 'kling api limits'.
64
77%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Passed
No known issues
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npx tessl skill review --optimize ./plugins/saas-packs/klingai-pack/skills/klingai-rate-limits/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 well-crafted skill description with strong trigger terms, clear 'what' and 'when' clauses, and excellent distinctiveness for its niche. The main weakness is that the capability description could be more specific about the concrete actions it performs beyond 'backoff and queuing strategies'. Overall it is a strong description that would perform well in skill selection.
Suggestions
Expand the specificity of actions, e.g., 'Implements exponential backoff, request queuing, retry logic, and rate limit header parsing for Kling AI API rate limits.'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (Kling AI API rate limits) and some actions (backoff and queuing strategies), but doesn't list multiple specific concrete actions like retry logic, request queuing, exponential backoff configuration, or rate limit header parsing. | 2 / 3 |
Completeness | Clearly answers both 'what' (handle Kling AI API rate limits with backoff and queuing strategies) and 'when' (when hitting 429 errors or planning high-volume workflows), with explicit trigger phrases provided. | 3 / 3 |
Trigger Term Quality | Includes excellent natural trigger terms: 'klingai rate limit', 'kling ai 429', 'klingai throttle', 'kling api limits', '429 errors', and 'high-volume workflows'. These cover multiple natural variations a user would say. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive — targets a very specific niche (Kling AI API rate limiting) with unique trigger terms like 'klingai' and 'kling ai 429' that are unlikely to conflict with other skills. | 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 rate-limiting strategies for Kling AI. Its main weaknesses are the lack of a unified workflow showing how the components work together and some verbosity in presenting four separate patterns without clear guidance on when to use which. The content would benefit from a brief decision guide and a cohesive integration example.
Suggestions
Add a brief workflow section showing how the components integrate: e.g., 'For high-volume workflows: 1. Initialize RateLimitMonitor, 2. Use TaskLimiter for concurrency, 3. Wrap calls with request_with_retry, 4. Verify results'
Add a quick decision guide at the top: 'Single request? Use backoff. Batch jobs? Use RequestQueue. Async pipeline? Use TaskLimiter.' to help Claude choose the right pattern
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is mostly efficient with good code examples, but includes some unnecessary elements like the RateLimitMonitor and RequestQueue classes that add significant length. The rate limit tiers table and error reference are useful, but docstrings and print statements add minor verbosity. Overall reasonably lean but could be tightened. | 2 / 3 |
Actionability | All code examples are fully executable, copy-paste ready Python with proper imports. The exponential backoff, TaskLimiter, RateLimitMonitor, and RequestQueue are concrete, complete implementations with usage examples. The error reference table provides specific HTTP codes mapped to specific actions. | 3 / 3 |
Workflow Clarity | The skill presents individual components (backoff, limiter, monitor, queue) clearly but doesn't sequence them into a cohesive workflow showing how they fit together. There's no explicit step-by-step process like 'first check rate limits, then submit, then handle errors.' The error reference table helps but lacks a feedback loop for recovery beyond individual retry logic. | 2 / 3 |
Progressive Disclosure | The content is well-structured with clear section headers and a logical progression from simple (backoff) to complex (queue pattern). However, at ~130 lines with four substantial code blocks, some of the more advanced patterns (RequestQueue, RateLimitMonitor) could be split into separate reference files. The external links at the bottom are helpful but the skill is somewhat monolithic for its size. | 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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