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'.
64
77%
Does it follow best practices?
Impact
—
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-crafted skill description that excels in completeness and distinctiveness, with explicit 'Use when' and 'Trigger with' clauses and a clear niche around Kling AI image-to-video. The main weakness is that the capability description is somewhat narrow—it could benefit from listing more specific actions or parameters (e.g., setting motion intensity, choosing aspect ratios, configuring duration).
Suggestions
Add more specific concrete actions beyond 'animate static images into video', such as 'configure motion parameters, set video duration, choose camera movements' to improve specificity.
| 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 concrete actions beyond that single capability. 'Building I2V pipelines' adds some specificity but remains somewhat abstract. | 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 like...' section providing concrete activation phrases). | 3 / 3 |
Trigger Term Quality | Includes strong natural trigger terms covering multiple variations: 'klingai image to video', 'kling ai animate image', 'klingai img2vid', 'animate picture klingai', plus natural phrases like 'converting images to video' and 'adding motion to stills'. Good coverage of how users would phrase requests. | 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 ('klingai', 'kling ai') 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 features of the Kling AI image-to-video API. Its main strengths are concrete, copy-paste-ready code and clear documentation of mutual exclusivity constraints. Weaknesses include a somewhat monolithic structure that could benefit from splitting reference material into separate files, and missing pre-submission validation steps (e.g., verifying image accessibility and mask dimension matching before API calls).
Suggestions
Add a pre-submission validation checklist or code snippet that verifies image URL accessibility and mask dimension matching before making the API call, to create a proper feedback loop for error prevention.
Consider moving the full parameter reference table and error handling table into a separate REFERENCE.md file, keeping SKILL.md focused on the workflow and key examples with links to the reference.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is mostly efficient with good use of tables and code examples, but includes some unnecessary detail like the full parameter reference table that Claude could look up, and the error handling table adds moderate bulk. The auth boilerplate is repeated conceptually but shown once, which is reasonable. | 2 / 3 |
Actionability | Provides fully executable Python code for every feature (basic animation, image_tail, dynamic_masks, static_mask), including JWT auth setup, polling logic, and concrete JSON payloads. All examples are copy-paste ready with realistic parameters. | 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 before sending, and the mutual exclusivity rules are documented but not integrated into a pre-submission validation step. | 2 / 3 |
Progressive Disclosure | Content is well-structured with clear section headers and logical progression from basic to advanced features. However, the full parameter reference, all code examples, and constraint tables are all inline in a single file (~150 lines), and some of this (like the detailed parameter table or error handling) could be split into referenced files for better organization. The two external resource links at the end are helpful but minimal. | 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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