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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'.

59

Quality

70%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./plugins/saas-packs/klingai-pack/skills/klingai-ci-integration/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

57%Weight 40%Scale 1-5

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 with complete, executable code for the main CI/CD platforms, but it undermines progressive disclosure by inlining full files that already exist as reference bundles without linking to them, and its batch workflow lacks a validation checkpoint. Conciseness suffers from the same inlining that hurts progressive disclosure.

Suggestions

Replace the inlined full workflow YAMLs and the generate-video.py script with concise overviews that link to the existing references (github-actions-workflow.md, gitlab-ci-configuration.md, video-generation-script.md, batch-generation-from-file.md) so SKILL.md stays a lean entry point.

Add an explicit validation checkpoint to the Batch from YAML Config workflow (e.g., verify each task succeeded and the output MP4 is non-empty before declaring the run complete) to raise workflow_clarity above the batch-validation cap of 3.

Define or replace the undefined submit_async() call in the Batch snippet with the real submission logic (or a reference to generate-video.py) so the example is fully executable.

DimensionReasoningScore

Conciseness

The body is free of concept-explanation fluff, but it inlines an entire ~80-line Python script and multiple full workflow YAML files that duplicate dedicated reference files in ./references/. It is mostly efficient per-token yet could be tightened substantially by moving bulk content into the existing reference files. It is not a 4 because the inlined full-file duplication is more than a 'minor' trimming opportunity.

3 / 5

Actionability

The GitHub Actions workflow, GitLab CI config, and the generate-video.py script are complete and copy-paste ready with real argparse flags, JWT auth, and polling. It is not a 5 because the Batch section calls an undefined submit_async() helper, leaving a minor execution gap in that example.

4 / 5

Workflow Clarity

The generation script internally sequences submit -> poll -> save with succeed/failed/timeout handling, but the SKILL itself presents parallel platform configs rather than a checkpointed workflow, and the Batch from YAML Config operation has no validation/verification step. Per the rubric, a batch operation without validation caps workflow_clarity at 3, so it cannot score higher despite the script's internal status checks.

3 / 5

Progressive Disclosure

The body has clear section headers, but it inlines full workflows and a full script that have dedicated counterparts in ./references/ (github-actions-workflow.md, gitlab-ci-configuration.md, video-generation-script.md, batch-generation-from-file.md), and none of those reference files are linked or signaled anywhere in the body. This matches 'content that should be separate is inline' with references present but not clearly signaled. It is not a 2 only because the section structure itself is reasonable, not 'minimal'.

3 / 5

Total

13

/

20

Passed

Description

82%Weight 40%Scale 1-5

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-constructed: it states a clear purpose, an explicit 'Use when' clause, and concrete trigger phrases in third-person voice. Its only weakness is specificity, listing one integrated action rather than a comprehensive set of capabilities.

DimensionReasoningScore

Specificity

Names the domain ('Kling AI video generation into CI/CD pipelines') and one concrete action (integrate/automate generation), but does not enumerate multiple distinct capabilities. It sits at the 'domain plus 1-2 concrete actions, not comprehensive' anchor rather than 4, which would require several specific actions.

3 / 5

Completeness

Explicitly states what ('Integrate Kling AI video generation into CI/CD pipelines') and when ('Use when automating video content in GitHub Actions or GitLab CI') with concrete trigger phrases, matching the 'clearly and explicitly answers both what AND when' anchor. Not below 5 because the 'when' is explicit, not merely implied.

5 / 5

Trigger Term Quality

Lists several natural trigger phrases ('klingai ci', 'kling ai github actions', 'klingai automation', 'automated video generation') that a user would plausibly say. It is not a 5 because coverage of synonyms/extensions is incomplete (e.g. no 'video pipeline' or 'kling video' variants).

4 / 5

Distinctiveness Conflict Risk

The 'Kling AI + CI/CD video generation' niche with brand-specific triggers ('klingai ci', 'kling ai github actions') is clearly distinguishable with minimal conflict risk. The somewhat broad 'automated video generation' phrase keeps it from being perfectly narrow, but overall it occupies a distinct niche.

5 / 5

Total

17

/

20

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.

Validation13 / 16 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

referenced_paths_exist

Referenced path issues: 3 missing

Warning

Total

13

/

16

Passed

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

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