klingai-image-to-video
Animate static images into video using Kling AI. Use when converting images to video, adding motion to stills, or building I2V pipelines. Trigger with phrases like 'klingai image to video', 'kling ai animate image', 'klingai img2vid', 'animate picture klingai'.
80
77%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./plugins/saas-packs/klingai-pack/skills/klingai-image-to-video/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-structured skill description with excellent trigger coverage and clear 'what/when' guidance. Its main weakness is that the capability description is somewhat thin—it describes only one core action (animate images to video) without detailing specific features, parameters, or sub-capabilities. The strong branding with 'Kling AI' and multiple trigger phrase variations make it highly distinctive and easy to match.
Suggestions
Add more specific concrete actions beyond the single 'animate' capability, such as configuring motion intensity, setting video duration, choosing output resolution, or specifying camera movements.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (Kling AI image-to-video) and the core action (animate static images into video), but doesn't list multiple specific concrete actions beyond the single conversion task. No mention of configuration options, output formats, or additional capabilities. | 2 / 3 |
Completeness | Clearly answers both 'what' (animate static images into video using Kling AI) and 'when' (explicit 'Use when' clause with trigger scenarios and a 'Trigger with phrases' section providing concrete examples). | 3 / 3 |
Trigger Term Quality | Includes strong natural trigger terms: 'klingai image to video', 'kling ai animate image', 'klingai img2vid', 'animate picture klingai', 'converting images to video', 'adding motion to stills', 'I2V pipelines'. Good coverage of variations users might naturally say. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive due to the specific tool name 'Kling AI' and the narrow niche of image-to-video animation. The branded trigger terms like 'klingai' make it very 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 executable code examples covering all major Kling AI image-to-video features. Its main weaknesses are length (could benefit from splitting advanced features into separate files) and missing pre-submission validation steps for common failure modes like image accessibility and mask dimension matching. The mutual exclusivity rules are well-documented but mentioned redundantly.
Suggestions
Add a pre-submission validation step (e.g., verify image URL is accessible, check mask dimensions match source) before the API call to create a proper feedback loop for common errors
Move advanced features (motion brush, static mask, image_tail) into a separate ADVANCED.md file and keep SKILL.md as a concise overview with just the basic example and feature summary
Remove the redundant mutual exclusivity mentions from the parameter table descriptions since they're covered in the dedicated rules table
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is mostly efficient with good use of tables and code examples, but the parameter table is quite extensive and some information (like image requirements) could be more compact. The mutual exclusivity rules are repeated both in the parameter descriptions and in a dedicated table. | 2 / 3 |
Actionability | Provides fully executable Python code for all major use cases (basic animation, image_tail transitions, dynamic masks, static masks), including authentication setup, polling logic, and error handling. Code is copy-paste ready with clear variable names. | 3 / 3 |
Workflow Clarity | The basic example includes a polling loop with success/failure checks, which is good. However, there's no explicit validation workflow—no step to verify the image URL is accessible before submitting, no guidance on checking mask dimensions match the source image before sending, and no structured pre-flight checklist despite these being common failure points listed in the error table. | 2 / 3 |
Progressive Disclosure | The content is well-structured with clear sections and links to external resources at the bottom. However, the skill is quite long (~150 lines of substantive content) and could benefit from splitting advanced features (motion brush, static mask, camera control) into separate reference files while keeping the SKILL.md as a concise overview with the basic example. | 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
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- Reviewed
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