klingai-sdk-patterns
Production SDK patterns for Kling AI: client wrapper, retry logic, async polling, and error handling. Use when building robust integrations. Trigger with phrases like 'klingai sdk', 'kling ai client', 'klingai patterns', 'kling ai wrapper'.
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/klingai-pack/skills/klingai-sdk-patterns/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 description that clearly identifies its niche (Kling AI SDK patterns) and provides explicit trigger guidance. Its main weakness is that the capability descriptions are somewhat abstract—listing pattern types rather than concrete actions like 'generate videos' or 'create image tasks'. The explicit trigger phrases and clear 'Use when' clause make it effective for skill selection.
Suggestions
Add more concrete actions describing what the SDK patterns enable (e.g., 'generate videos', 'create image tasks', 'manage API calls') to improve specificity beyond pattern category names.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (Kling AI SDK) and lists some actions (client wrapper, retry logic, async polling, error handling), but these are more like pattern categories than concrete actions. It doesn't specify what the SDK actually does (e.g., generate videos, create images). | 2 / 3 |
Completeness | Clearly answers both 'what' (production SDK patterns for Kling AI including client wrapper, retry logic, async polling, error handling) and 'when' (when building robust integrations, with explicit trigger phrases). The 'Use when' and 'Trigger with' clauses are present. | 3 / 3 |
Trigger Term Quality | Explicitly lists natural trigger phrases like 'klingai sdk', 'kling ai client', 'klingai patterns', 'kling ai wrapper'. These cover common variations a user might say when looking for this specific skill, including both spaced and concatenated forms. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive due to the specific product name 'Kling AI' and the focus on SDK patterns. Unlikely to conflict with other skills given the narrow niche of Kling AI integration patterns. | 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, highly actionable skill with production-ready code patterns for Kling AI integration. Its main weakness is verbosity—the Node.js client duplicates the Python patterns and could be extracted to a separate file, and the inline code is extensive for a SKILL.md overview. The workflow could benefit from explicit error recovery guidance and validation checkpoints.
Suggestions
Move the Node.js client to a separate reference file (e.g., NODEJS.md) and link to it from the main skill to reduce token usage and improve progressive disclosure.
Add a brief troubleshooting/error handling section with common failure scenarios (rate limits, invalid prompts, quota exhaustion) and recommended recovery steps.
Consolidate image_to_video and extend_video into a brief summary or table since they follow the identical pattern as text_to_video, reducing redundancy.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is mostly efficient with executable code, but the full client implementation is quite long (~150 lines of Python + JS). Some methods like extend_video and image_to_video could be summarized more briefly since the pattern is identical to text_to_video. The Node.js client largely duplicates the Python patterns. | 2 / 3 |
Actionability | Fully executable code with complete Python and Node.js client implementations, usage examples showing both sync and async patterns, a retry decorator, and concrete configuration defaults. Everything is copy-paste ready. | 3 / 3 |
Workflow Clarity | The polling workflow includes exponential backoff and error handling, but there's no explicit validation guidance for common failure modes (e.g., what to do when tasks fail, how to verify video output quality, or how to handle quota limits). The retry decorator is separate rather than integrated into a clear workflow sequence. | 2 / 3 |
Progressive Disclosure | Content is organized into logical sections (client, usage, Node.js, retry, resources) with external links at the end. However, the full Node.js client could be in a separate file since it duplicates the Python pattern, and the monolithic code blocks make the skill quite long for an overview document. | 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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