klingai-storage-integration
Download and store Kling AI generated videos in cloud storage (S3, GCS, Azure). Use when persisting videos or building CDN pipelines. Trigger with phrases like 'klingai storage', 'save klingai video', 'kling ai s3 upload', 'klingai cloud storage'.
84
82%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Passed
No known issues
Quality
Discovery
100%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 strong skill description that clearly defines a specific niche (Kling AI video storage in cloud), provides concrete actions and platforms, and includes explicit trigger phrases. It effectively answers both what the skill does and when to use it, with distinctive enough terminology to avoid conflicts with other skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: 'Download and store Kling AI generated videos in cloud storage' with explicit platforms '(S3, GCS, Azure)' and mentions 'CDN pipelines'. These are concrete, actionable capabilities. | 3 / 3 |
Completeness | Clearly answers both 'what' (download and store Kling AI videos in cloud storage) and 'when' (use when persisting videos or building CDN pipelines), with explicit trigger phrases provided. | 3 / 3 |
Trigger Term Quality | Includes natural trigger phrases like 'klingai storage', 'save klingai video', 'kling ai s3 upload', 'klingai cloud storage' which cover multiple variations a user might say. Also includes cloud provider names (S3, GCS, Azure) as natural keywords. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive niche combining Kling AI video generation with cloud storage. The specific combination of 'Kling AI' + 'cloud storage/S3/GCS/Azure' makes it very unlikely to conflict with generic video or generic cloud storage skills. | 3 / 3 |
Total | 12 / 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, actionable code for downloading Kling AI videos and uploading to three major cloud providers. Its main strengths are executable, copy-paste ready code and clear section organization. Weaknesses include missing validation/error handling in the pipeline, a bug where cleanup is unreachable after early returns, and the content being somewhat long for a single SKILL.md without splitting provider-specific details into separate files.
Suggestions
Add validation checkpoints to the end-to-end pipeline: verify download succeeded (check file size > 0), verify upload succeeded (check response/existence), and fix the unreachable cleanup step by storing the result before removing the file.
Split the three cloud provider upload sections into separate reference files (e.g., S3.md, GCS.md, AZURE.md) and keep only the download function and pipeline overview in SKILL.md.
Add error handling/retry logic for the download step, since Kling CDN URLs are temporary and the download could fail due to expiration or network issues.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is mostly efficient with executable code, but includes some unnecessary elements like print statements, docstrings Claude doesn't need, and the `make_public()` comment. The metadata preservation section adds bulk that could be trimmed. Overall reasonably lean but could be tighter. | 2 / 3 |
Actionability | All code examples are fully executable with proper imports, concrete function signatures, and copy-paste ready implementations for all three cloud providers. The end-to-end pipeline ties everything together with a clear, runnable function. | 3 / 3 |
Workflow Clarity | The end-to-end pipeline has a clear numbered sequence (generate → poll → download → upload → cleanup), but lacks validation checkpoints. There's no error handling for failed downloads, no verification that uploads succeeded, and the cleanup step at line 5 is unreachable due to the early returns. Missing feedback loops for these potentially fragile operations caps this at 2. | 2 / 3 |
Progressive Disclosure | The content is well-sectioned with clear headers for each provider, but it's a fairly long monolithic file. The three cloud provider sections could be split into separate reference files with just a quick-start example inline. The Resources section at the end provides useful external links but internal progressive disclosure is lacking. | 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 | |
- Commit
70e9fa4
- Reviewed
Table of Contents
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.