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klingai-ci-integration

Integrate Kling AI video generation into CI/CD pipelines. Use when automating video content in GitHub Actions or GitLab CI. Trigger with phrases like 'klingai ci', 'kling ai github actions', 'klingai automation', 'automated video 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

65%

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

The body is highly actionable with complete, executable code for GitHub Actions, GitLab CI, and the generation script, but it underuses its own reference bundle by inlining everything and omits verification checkpoints for batch operations. Tightening the inline code and pointing to the existing references would raise both conciseness and progressive disclosure.

Suggestions

Replace the full inline Python script, GitHub Actions YAML, and GitLab CI YAML with concise overviews that link to the matching references/ files (video-generation-script.md, github-actions-workflow.md, gitlab-ci-configuration.md) so SKILL.md stays a lean entry point.

Add an explicit verification step to the batch and CI workflows (e.g., confirm every submitted task reached 'succeed' and assert the uploaded artifact exists) to satisfy the validation-checkpoint requirement.

Link the existing references/examples.md and references/errors.md from the body so users can discover batch patterns and troubleshooting guidance that currently sit unused in the bundle.

Dimension	Reasoning	Score
Conciseness	The prose is lean and avoids explaining concepts Claude already knows, but ~190 lines of full inline code (the entire Python script, GitHub Actions YAML, and GitLab CI YAML) duplicate content already present in the references/ bundle, so it is not as tight as a true overview should be.	2 / 3
Actionability	Provides fully executable, copy-paste-ready code: a complete argparse-based generation script with JWT auth and submit/poll logic, complete GitHub Actions and GitLab CI workflows, and a concrete secret-management table.	3 / 3
Workflow Clarity	The generation script has a clear submit->poll->succeed/failed/timeout sequence, but the batch-generation and CI workflows lack validation or verification checkpoints (e.g., confirm every batch task succeeded, verify the uploaded artifact), which caps the score at 2 per the rubric.	2 / 3
Progressive Disclosure	Six relevant bundle files exist under references/, but the body never signals or links to them and instead inlines their content, so navigation is absent and content that should be separate is inline.	2 / 3
	Total	9 / 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 is well-structured with an explicit Use-when clause and natural trigger phrases, making it clear and distinct. Its only weakness is specificity, since it describes one integrated action rather than enumerating multiple discrete capabilities.

Dimension	Reasoning	Score
Specificity	Names a concrete domain and action ("Integrate Kling AI video generation into CI/CD pipelines") but states a single integrated action rather than enumerating multiple distinct concrete capabilities, so it falls short of the score-3 anchor.	2 / 3
Completeness	Clearly answers both what ("Integrate Kling AI video generation into CI/CD pipelines") and when via an explicit "Use when..." clause plus trigger phrases, satisfying the score-3 anchor.	3 / 3
Trigger Term Quality	Explicitly lists four natural trigger phrases ("klingai ci", "kling ai github actions", "klingai automation", "automated video generation") with phrasing variations a user would actually say, matching the good-coverage anchor.	3 / 3
Distinctiveness Conflict Risk	The Kling-AI-plus-CI/CD niche with platform-specific triggers is a clear, narrow scope unlikely to fire for unrelated skills.	3 / 3
	Total	11 / 12 Passed

Validation

81%

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 — 13 / 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
referenced_paths_exist	Referenced path issues: 3 missing	Warning

	Total	13 / 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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