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'.
Install with Tessl CLI
npx tessl i github:jeremylongshore/claude-code-plugins-plus-skills --skill klingai-reference-architecture57
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
89%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 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 |
Implementation
22%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
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 |
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.
Validation — 13 / 16 Passed
Validation for skill structure
| 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 | |
- Reviewed
Table of Contents
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