klingai-reference-architecture

tessl i github:jeremylongshore/claude-code-plugins-plus-skills --skill klingai-reference-architecture

Execute production-ready reference architecture for Kling AI video platforms. Use when designing scalable video generation systems. Trigger with phrases like 'klingai architecture', 'kling ai system design', 'video platform architecture', 'klingai production setup'.

58%

Overall

Validation — 81%

Implementation — 22%

Activation — 90%

SKILL.md

Review

Evals

Validation

81%

Warnings & errors only

Criteria	Description	Result
allowed_tools_field	'allowed-tools' contains unusual tool name(s)	Warning
metadata_version	'metadata' field is not a dictionary	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	13 / 16 Passed

Implementation

22%

This skill is essentially a placeholder that provides no actionable guidance for building Kling AI video platforms. It lists abstract steps without concrete implementation details, code examples, or specific architecture patterns. The content defers all substance to external files while the main skill body offers nothing Claude can execute.

Suggestions

Add concrete, executable code examples showing actual microservices setup, queue configuration, or Kubernetes deployment manifests for Kling AI integration

Replace abstract steps like 'Choose Pattern' with specific patterns (e.g., 'Use async job queue pattern: submit to Redis -> worker polls -> webhook on completion') with actual implementation code

Include at least one complete, copy-paste ready architecture component (e.g., a Docker Compose file, a worker service skeleton, or API gateway configuration)

Add validation checkpoints with specific commands (e.g., 'Verify queue connectivity: redis-cli ping', 'Test API auth: curl -H "Authorization: Bearer $TOKEN" https://api.klingai.com/v1/health')

Dimension	Reasoning	Score
Conciseness	The content is relatively brief but includes some unnecessary padding like the overview paragraph and prerequisites section that explain concepts Claude already knows (distributed systems, cloud infrastructure basics).	2 / 3
Actionability	The instructions are entirely abstract and vague ('Choose Pattern', 'Design Components', 'Plan Infrastructure') with no concrete code, commands, specific architecture diagrams, or executable examples. It describes rather than instructs.	1 / 3
Workflow Clarity	The 5 steps listed are high-level labels without any concrete guidance, validation checkpoints, or feedback loops. There's no actual workflow - just abstract phase names that don't tell Claude what to actually do.	1 / 3
Progressive Disclosure	References to external files (errors.md, examples.md) are present and one-level deep, but the main content is too thin to serve as a useful overview. The skill offloads all substance to referenced files without providing actionable quick-start content.	2 / 3
	Total	6 / 12 Passed

Activation

90%

The description has strong completeness with explicit 'Use when' and trigger phrases, and excellent distinctiveness due to the specific Kling AI focus. However, the specificity of capabilities is weak - 'Execute production-ready reference architecture' is vague and doesn't describe concrete actions the skill performs (e.g., deploying services, configuring APIs, setting up pipelines).

Suggestions

Replace 'Execute production-ready reference architecture' with specific concrete actions like 'Deploy video generation pipelines, configure Kling AI API integrations, set up scaling infrastructure'

Add specific architectural components or outputs the skill produces (e.g., 'Creates deployment configs, API wrappers, monitoring dashboards')

Dimension	Reasoning	Score
Specificity	Names the domain (Kling AI video platforms) and mentions 'reference architecture' and 'scalable video generation systems', but lacks concrete actions like 'deploy', 'configure', 'monitor', or specific architectural components.	2 / 3
Completeness	Clearly answers both what ('Execute production-ready reference architecture for Kling AI video platforms') and when ('Use when designing scalable video generation systems') with explicit trigger phrases listed.	3 / 3
Trigger Term Quality	Includes explicit trigger phrases covering natural variations: 'klingai architecture', 'kling ai system design', 'video platform architecture', 'klingai production setup' - good coverage of terms users would actually say.	3 / 3
Distinctiveness Conflict Risk	Very specific niche targeting Kling AI specifically with distinct triggers like 'klingai architecture' and 'klingai production setup' - unlikely to conflict with generic video or architecture skills.	3 / 3
	Total	11 / 12 Passed

Reviewed

14 days ago

Table of Contents

Validation Implementation Activation

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