klingai-ci-integration
Integrate Kling AI video generation into CI/CD pipelines. Use when automating video content in GitHub Actions or GitLab CI. Trigger with phrases like 'klingai ci', 'kling ai github actions', 'klingai automation', 'automated video generation'.
86
84%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Quality
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.
The description is well-structured with clear 'what' and 'when' clauses and strong trigger terms that define a distinct niche. Its main weakness is the lack of specific concrete actions beyond 'integrate'—listing particular capabilities like configuring pipeline steps, managing API credentials, or handling video output formats would strengthen it.
Suggestions
Add 2-3 more specific concrete actions beyond 'integrate', such as 'configure pipeline workflows, manage API authentication, handle video output artifacts' to improve specificity.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (Kling AI video generation, CI/CD pipelines) and a general action (integrate), but does not list multiple specific concrete actions like configuring workflows, setting up API keys, handling video outputs, etc. | 2 / 3 |
Completeness | Clearly answers both 'what' (integrate Kling AI video generation into CI/CD pipelines) and 'when' (automating video content in GitHub Actions or GitLab CI, with explicit trigger phrases). | 3 / 3 |
Trigger Term Quality | Includes natural keywords users would say: 'klingai ci', 'kling ai github actions', 'klingai automation', 'automated video generation', plus mentions GitHub Actions and GitLab CI. Good coverage of natural terms for this niche. | 3 / 3 |
Distinctiveness Conflict Risk | Very specific niche combining Kling AI with CI/CD pipelines; unlikely to conflict with general video generation skills or general CI/CD skills due to the narrow intersection. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
79%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a strong, highly actionable skill with complete, executable code for integrating Kling AI into CI/CD pipelines. Its main strengths are conciseness and actionability—every section provides real, usable code. Weaknesses include missing validation steps for batch operations and the batch example referencing an undefined function, plus the content could benefit from splitting the full Python script into a referenced file.
Suggestions
Add a validation step after video download (e.g., check file size > 0, verify MP4 header) to ensure the artifact is valid before uploading.
Complete the batch processing example by either defining `submit_async` or referencing the main script's function, and add error handling for batch failures.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is lean and efficient. It doesn't explain what CI/CD is, what GitHub Actions are, or how JWT works. Every section provides directly usable code with minimal prose. The overview is one sentence with three concrete use cases. | 3 / 3 |
Actionability | Fully executable code throughout: a complete GitHub Actions workflow YAML, a complete Python script with argument parsing, polling, error handling, and file download, plus GitLab CI config. Everything is copy-paste ready and production-quality. | 3 / 3 |
Workflow Clarity | The CI script has good implicit workflow (submit → poll → download/fail/timeout) with status logging and error exits, but there's no explicit validation checkpoint for the generated video (e.g., checking file size, verifying the download succeeded, or validating video integrity). For batch operations from YAML config, there's no error handling or feedback loop shown. | 2 / 3 |
Progressive Disclosure | The content is well-structured with clear section headers and a logical progression from single generation to batch to different CI platforms. However, the file is fairly long (~150 lines of code) and the batch YAML config section uses an undefined `submit_async` function without referencing where it comes from. No bundle files exist to offload the detailed script or batch processing logic. | 2 / 3 |
Total | 10 / 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 | |
- Commit
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