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klingai-text-to-video

Generate videos from text prompts with Kling AI. Use when creating videos from descriptions, learning prompt techniques, or building T2V pipelines. Trigger with phrases like 'kling ai text to video', 'klingai prompt', 'generate video from text', 'text2video kling'.

64

Quality

77%

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./plugins/saas-packs/klingai-pack/skills/klingai-text-to-video/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

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 complete executable examples covering the main use cases (basic generation, camera control, native audio). Its main weakness is moderate verbosity—prompt engineering tips, cost tables, and full JWT boilerplate inflate the token count without adding critical value for Claude. The async workflow is demonstrated in code but could benefit from more explicit step-by-step sequencing with validation checkpoints.

Suggestions

Remove or drastically shorten the prompt engineering tips table—Claude already knows how to write descriptive prompts and doesn't need examples of lighting cues or camera language.

Extract the JWT authentication helper into a shared utility reference rather than including the full implementation inline, since it's boilerplate not specific to text-to-video.

Add explicit numbered workflow steps (1. Authenticate → 2. Submit task → 3. Poll with backoff → 4. Verify video URL accessible) above the code example to improve workflow clarity.

DimensionReasoningScore

Conciseness

Generally efficient with good use of tables for parameters, costs, and errors. However, some sections like the prompt engineering tips and the full JWT auth boilerplate add bulk that could be trimmed—Claude knows how to generate JWTs and write good prompts. The parameter table is useful but the cost reference and prompt tips are borderline unnecessary.

2 / 3

Actionability

Provides fully executable Python code with complete authentication, task creation, and polling logic. Camera control and native audio examples are concrete and copy-paste ready with specific parameter values and ranges documented.

3 / 3

Workflow Clarity

The async workflow (create task → poll → get result) is demonstrated in code with status checking and error handling, but there's no explicit validation checkpoint or feedback loop beyond the polling. For an API-based workflow this is adequate but the sequence could be more explicitly called out as numbered steps with verification guidance.

2 / 3

Progressive Disclosure

Content is well-structured with clear section headers and the external resource links at the bottom are helpful. However, with no bundle files, all content is inline in one file—the parameter reference table, prompt tips, and cost reference could be split into separate files for a cleaner overview. For a standalone skill of this length (~120 lines of content), the organization is reasonable but slightly heavy.

2 / 3

Total

9

/

12

Passed

Description

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 skill description that clearly identifies its niche (Kling AI text-to-video), provides explicit trigger guidance, and is highly distinctive. Its main weakness is that the capability listing could be more concrete—specifying particular actions like configuring video parameters, choosing styles, or handling specific output formats would strengthen specificity.

Suggestions

Add more concrete actions beyond 'generate videos from text prompts', such as 'configure video resolution, aspect ratio, and style parameters' or 'iterate on prompt wording for better results'.

DimensionReasoningScore

Specificity

Names the domain (Kling AI text-to-video) and some actions ('generate videos from text prompts', 'learning prompt techniques', 'building T2V pipelines'), but the actions are not deeply concrete—e.g., it doesn't specify what aspects of video generation it handles (resolution, style, parameters, etc.).

2 / 3

Completeness

Clearly answers both 'what' (generate videos from text prompts with Kling AI) and 'when' (explicit 'Use when...' clause with trigger scenarios and a 'Trigger with phrases like...' section).

3 / 3

Trigger Term Quality

Includes a strong set of natural trigger terms: 'kling ai text to video', 'klingai prompt', 'generate video from text', 'text2video kling'. These cover both branded and generic variations a user would naturally say.

3 / 3

Distinctiveness Conflict Risk

Highly distinctive due to the specific tool name 'Kling AI' and the narrow focus on text-to-video generation. Unlikely to conflict with other skills unless another Kling AI skill exists.

3 / 3

Total

11

/

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.

Validation9 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

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

Repository
jeremylongshore/claude-code-plugins-plus-skills
Reviewed

Table of Contents

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