klingai-batch-processing

Process multiple video generation requests efficiently with Kling AI. Use when generating batches of videos or building content pipelines. Trigger with phrases like 'klingai batch', 'kling ai bulk', 'multiple videos klingai', 'klingai parallel generation'.

Quality

—

Does it follow best practices?

Impact

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No eval scenarios have been run

Securityby

Passed

No known issues

Quality

Content

80%

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

The content is lean and highly actionable with executable code, but a batch workflow lacks validation/verification feedback loops and ignores available reference bundle files that should be factored out.

Suggestions

Add an explicit validation/verification feedback loop to the batch workflow (e.g., check task statuses, retry failed submissions, and confirm results before declaring success) to raise workflow_clarity.

Reference the existing bundle files from the body (e.g., link references/batch-with-retry-logic.md for retry behavior and references/errors.md for rate-limit handling) so detail is one level deep rather than inline.

De-duplicate the repeated text2video payload across the sync, async, and callback sections into a shared helper to tighten the content.

Dimension	Reasoning	Score
Conciseness	The body is lean and mostly executable code with short headers, assuming Claude's competence and avoiding concept explanations; minor near-duplicate payloads across sections keep it from being perfectly tight.	3 / 3
Actionability	All examples are executable, copy-paste-ready Python with real endpoints and JWT auth rather than pseudocode or vague direction.	3 / 3
Workflow Clarity	Code is organized into stages but a batch operation lacks explicit validation/verification checkpoints and error-recovery feedback loops, which the rubric caps at 2.	2 / 3
Progressive Disclosure	Five reference bundle files exist but the body never references them, keeping all content inline in a single long file rather than splitting it out with signaled navigation.	2 / 3
	Total	10 / 12 Passed

Description

90%

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 clearly states what the skill does and when to use it, with natural trigger phrases that distinguish it from other skills. Specificity could be slightly higher by listing more concrete actions, but it is otherwise strong.

Dimension	Reasoning	Score
Specificity	Names the domain and a couple of actions ('Process multiple video generation requests', 'building content pipelines') but does not list multiple distinct concrete actions like the score-3 anchor.	2 / 3
Completeness	Explicitly answers both what ('Process multiple video generation requests efficiently with Kling AI') and when ('Use when generating batches... Trigger with phrases like...').	3 / 3
Trigger Term Quality	Provides natural trigger phrases users would say ('klingai batch', 'kling ai bulk', 'multiple videos klingai', 'klingai parallel generation') with good coverage of common variations.	3 / 3
Distinctiveness Conflict Risk	Clear niche (Kling AI batch video generation) with distinct triggers unlikely to fire for unrelated skills.	3 / 3
	Total	11 / 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

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