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'.

Quality

88%

Does it follow best practices?

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Securityby

Passed

No known issues

Quality

Content

77%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The content is highly actionable and token-efficient with excellent executable examples, but it fails to leverage the provided references/ bundle and lacks explicit validation checkpoints in the polling workflow.

Suggestions

Link to the existing references/ files (e.g., advanced-parameters.md, prompt-engineering.md, batch-generation.md, errors.md) from a section so detail lives one level deep instead of inline.

Add an explicit validation/feedback step in the polling loop (e.g., retry on transient failure, check HTTP status before parsing) to support error recovery.

Move the prompt-engineering tips and cost/error tables into references and keep SKILL.md as a concise overview pointing to them.

Dimension	Reasoning	Score
Conciseness	The body is lean: a parameter table, executable code blocks, and compact reference tables with no padding or explanation of concepts Claude already knows.	3 / 3
Actionability	Provides complete, executable Python examples (auth via JWT, POST, polling) plus concrete parameter values and an error-handling table that are copy-paste ready.	3 / 3
Workflow Clarity	The polling loop shows create-then-poll sequencing with succeed/failed branches, but there is no explicit validation checkpoint before acting on results and no retry feedback loop, which the rubric expects for risky/batch operations.	2 / 3
Progressive Disclosure	The body is a monolithic wall covering parameters, examples, camera control, audio, tips, cost, and errors inline, while six references/ files exist but are never linked or signaled from the body.	1 / 3
	Total	9 / 12 Passed

Description

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.

The description is concise, concrete, and follows the recommended what+when pattern with explicit trigger phrases. It uses third person and avoids fluff.

Dimension	Reasoning	Score
Specificity	Lists concrete actions ('Generate videos from text prompts', 'learning prompt techniques', 'building T2V pipelines') tied to a specific capability rather than vague verbs.	3 / 3
Completeness	Explicitly answers what ('Generate videos from text prompts with Kling AI') and when ('Use when creating videos from descriptions...') with an explicit 'Use when...' trigger clause.	3 / 3
Trigger Term Quality	Includes natural user phrases like 'kling ai text to video', 'generate video from text', and 'text2video kling' that a user would plausibly say.	3 / 3
Distinctiveness Conflict Risk	The Kling/T2V niche is narrow and the trigger phrases are specific to this tool, making conflict with unrelated skills unlikely.	3 / 3
	Total	12 / 12 Passed

Validation

87%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 14 / 16 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	14 / 16 Passed

Repository: jeremylongshore/claude-code-plugins-plus-skills
Path: plugins/saas-packs/klingai-pack/skills/klingai-text-to-video/SKILL.md
Commit: b2e8c53

Reviewed: 5 days ago

Table of Contents

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