klingai-camera-control
Control camera movements in Kling AI video generation. Use when creating cinematic shots, pans, tilts, zooms, or dolly moves. Trigger with phrases like 'klingai camera', 'kling ai camera motion', 'klingai cinematic', 'klingai pan zoom'.
80
77%
Does it follow best practices?
Impact
Pending
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-camera-control/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 solid description with excellent trigger terms and completeness, clearly specifying both when to use the skill and what domain it covers. Its main weakness is that the capability description could be more specific about what concrete actions the skill performs (e.g., generating camera motion parameters, creating prompt syntax) rather than just listing camera movement types. The distinctiveness is strong due to the Kling AI branding.
Suggestions
Add more specific concrete actions describing what the skill produces—e.g., 'Generates camera motion parameters and prompt syntax for Kling AI video generation' rather than just 'Control camera movements'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (Kling AI camera movements) and lists some actions (pans, tilts, zooms, dolly moves), but doesn't describe what the skill actually does with these—e.g., does it generate config, set parameters, produce prompts? The actions are more like categories than concrete operations. | 2 / 3 |
Completeness | Clearly answers both 'what' (control camera movements in Kling AI video generation) and 'when' (explicit 'Use when' clause with trigger scenarios and a 'Trigger with phrases like' section). Both components are explicitly stated. | 3 / 3 |
Trigger Term Quality | Includes good natural trigger terms: 'klingai camera', 'kling ai camera motion', 'klingai cinematic', 'klingai pan zoom', plus general terms like 'cinematic shots', 'pans', 'tilts', 'zooms', 'dolly moves'. These cover both branded and descriptive terms users would naturally say. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive due to the specific 'Kling AI' branding combined with camera movement focus. The trigger terms are uniquely prefixed with 'klingai', making conflicts with other skills very unlikely. | 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 reference for Kling AI camera control with executable examples and useful reference tables. Its main weakness is verbosity from repetitive code examples that could be condensed, and the lack of any workflow guidance around polling for task completion or verifying output. The structure is good but the content length could be reduced by ~40% without losing information.
Suggestions
Consolidate the four nearly identical cinematic shot examples into one full example plus a table mapping shot types to their config field and value, saving significant tokens.
Add a brief note about polling the task status endpoint to verify video generation completed successfully, which is a critical validation step for async API operations.
Consider moving the full code examples to a separate EXAMPLES.md file and keeping SKILL.md as a concise overview with the parameter schema, quick reference table, and links.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is mostly efficient but includes some redundancy — four very similar full code examples that differ only in one config field could be condensed into one example plus a reference table. The examples are repetitive in structure, inflating token count without proportional value. | 2 / 3 |
Actionability | Fully executable Python code examples with real API endpoints, specific parameter values, and copy-paste ready requests. The quick reference table and error handling table provide concrete, specific guidance for every scenario. | 3 / 3 |
Workflow Clarity | The skill is primarily a reference for a single API parameter rather than a multi-step workflow, so sequencing needs are minimal. However, there's no guidance on checking the async task result (polling for completion, verifying the generated video), which is a validation gap for an API-based generation task. | 2 / 3 |
Progressive Disclosure | The content is well-structured with clear sections and tables, and includes external resource links. However, the four nearly identical code examples could be collapsed or moved to a separate examples file, and the inline content is somewhat long for a SKILL.md overview. | 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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