klingai-batch-processing
Process multiple video generation requests efficiently with Kling AI. Use when generating batches of videos or building content pipelines. Trigger with phrases like 'klingai batch', 'kling ai bulk', 'multiple videos klingai', 'klingai parallel generation'.
59
70%
Does it follow best practices?
Impact
—
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-batch-processing/SKILL.mdQuality
Discovery
75%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 effectively establishes a clear niche (batch video generation with Kling AI) and includes both 'what' and 'when' clauses. However, the specificity of concrete actions is limited, and the trigger terms feel artificially constructed rather than reflecting natural user language. The description would benefit from more concrete action verbs and more natural keyword variations.
Suggestions
Add more specific concrete actions such as 'queue multiple video jobs, track batch progress, handle parallel generation limits, and retry failed requests'.
Include more natural trigger terms users would actually say, such as 'generate several videos', 'bulk video creation', 'many videos at once', or 'video batch processing'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (video generation with Kling AI) and mentions batch processing, but doesn't list specific concrete actions beyond 'process multiple video generation requests'. Missing details like queue management, status tracking, error handling, etc. | 2 / 3 |
Completeness | Clearly answers both 'what' (process multiple video generation requests with Kling AI) and 'when' (generating batches of videos, building content pipelines) with explicit trigger phrases provided. | 3 / 3 |
Trigger Term Quality | Includes some relevant keywords like 'klingai batch', 'kling ai bulk', 'multiple videos klingai', 'klingai parallel generation', but these feel more like engineered compound phrases than natural user language. Missing simpler natural terms like 'generate several videos', 'batch video', or 'bulk video generation'. | 2 / 3 |
Distinctiveness Conflict Risk | Highly distinctive — focuses specifically on batch/bulk video generation with Kling AI, which is a clear niche. The combination of 'Kling AI' + 'batch/bulk' makes it very unlikely to conflict with a single-video Kling AI skill or other batch processing skills. | 3 / 3 |
Total | 10 / 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 skill provides solid, executable code for batch video generation with Kling AI, covering multiple approaches (sync, async, callbacks) and including cost estimation. Its main weaknesses are the lack of explicit error handling/validation workflows (e.g., what to do on 429 rate limits, auth failures, or partial batch failures) and the somewhat monolithic structure that could benefit from better progressive disclosure across the three approaches.
Suggestions
Add explicit error handling and validation steps: check for HTTP 429 responses with retry logic, validate API credentials before starting batch, and add a summary/verification step after batch completion to report success/failure counts.
Add a brief decision guide at the top explaining when to use sync vs async vs callback approaches, so Claude can quickly select the right pattern.
Consider splitting the three batch approaches into separate referenced files, keeping SKILL.md as a concise overview with the most common pattern (sync) inline and links to async/callback alternatives.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is mostly efficient with executable code, but includes some redundancy across the sync, async, and callback approaches that could be tightened. The code is reasonably lean but the multiple full implementations add bulk without clear guidance on when to choose each approach. | 2 / 3 |
Actionability | All code blocks are fully executable with concrete functions, specific API endpoints, proper authentication handling, and copy-paste ready implementations. The cost estimation function includes specific credit values and pricing. | 3 / 3 |
Workflow Clarity | The workflow is implicit in the code structure (submit → poll → collect) but lacks explicit validation checkpoints. There's no guidance on verifying API key setup, handling rate limit errors (HTTP 429), or validating batch results before proceeding. For batch/destructive operations, the missing error recovery feedback loops cap this at 2. | 2 / 3 |
Progressive Disclosure | The content is organized into logical sections with clear headers, and includes external resource links. However, the three different batch approaches (sync, async, callback) could benefit from being split into separate files with the SKILL.md providing a concise overview and routing guidance for when to use each approach. | 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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