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

—

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Quality

Content

92%

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

The content is concise, highly actionable, and presents a clear polling workflow with validation, but it does not leverage the provided reference bundle: no bundle files are linked from the body, leaving progressive disclosure underdeveloped.

Suggestions

Link the existing references/ files from the body (e.g., add an 'Advanced' section pointing to advanced-parameters.md, prompt-engineering.md, batch-generation.md, and errors.md) so detailed material is one level deep and clearly signaled.

Move the duplicate basic example out of the body and point to basic-text-to-video.md to reduce overlap between SKILL.md and the bundle.

Trim the small inline duplication (e.g., the basic request mirrors references/basic-text-to-video.md) to keep the overview a true pointer to the bundle.

Dimension	Reasoning	Score
Conciseness	The body is lean and executable — an endpoint, a parameter table, complete Python snippets, and compact reference tables — without explaining concepts Claude already knows; nearly every token earns its place.	3 / 3
Actionability	Provides fully executable, copy-paste-ready code including JWT auth, request construction, and a polling loop, plus concrete parameter values and worked examples for camera control and audio.	3 / 3
Workflow Clarity	The create-task → poll → branch-on-status workflow is clearly sequenced with an explicit validation checkpoint (status == 'succeed' / 'failed' with a raised error on failure), giving a feedback loop for error recovery.	3 / 3
Progressive Disclosure	The body is well-organized but self-contained, and none of the six bundle files in references/ are linked or signaled from the body, so content that could be split out (advanced params, prompt engineering, batch, errors) is either inline or stranded in unreferenced files.	2 / 3
	Total	11 / 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, specific, and clearly states both capability and trigger conditions with natural user phrasing. It is well-distinguished from other skills by the Kling AI / text-to-video niche.

Dimension	Reasoning	Score
Specificity	Lists several concrete actions ('Generate videos from text prompts', 'creating videos from descriptions', 'learning prompt techniques', 'building T2V pipelines') rather than vague language, matching the multiple-concrete-actions anchor.	3 / 3
Completeness	Explicitly answers both what ('Generate videos from text prompts with Kling AI') and when ('Use when creating videos from descriptions, learning prompt techniques, or building T2V pipelines'), with an explicit trigger clause, matching the full what-and-when anchor.	3 / 3
Trigger Term Quality	Provides natural phrases users would say ('kling ai text to video', 'klingai prompt', 'generate video from text', 'text2video kling') with good coverage of common variations, matching the good-coverage anchor.	3 / 3
Distinctiveness Conflict Risk	Tied to the specific Kling AI text-to-video niche with distinct brand + task triggers, making it unlikely to fire for unrelated skills, matching the clear-niche anchor.	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
Commit: 3022dd3

Reviewed: 1 day ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.