klingai-debug-bundle
Set up logging and debugging for Kling AI API integrations. Use when troubleshooting video generation or building observability. Trigger with phrases like 'klingai debug', 'kling ai logging', 'klingai troubleshoot', 'debug kling video generation'.
64
77%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./plugins/saas-packs/klingai-pack/skills/klingai-debug-bundle/SKILL.mdQuality
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.
This is a solid skill description with excellent trigger terms and completeness. It clearly identifies both what the skill does and when to use it, with distinctive Kling AI-specific terminology. The main weakness is that the capability description could be more specific about the concrete actions performed (e.g., configuring log levels, capturing API request/response payloads, setting up retry monitoring).
Suggestions
Add more specific concrete actions like 'configure log levels, capture API request/response payloads, set up error tracking, monitor retry behavior' to improve specificity.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (Kling AI API logging/debugging) and mentions some actions ('set up logging and debugging', 'troubleshooting video generation', 'building observability'), but doesn't list multiple concrete specific actions like configuring log levels, capturing API responses, setting up error handlers, etc. | 2 / 3 |
Completeness | Clearly answers both 'what' (set up logging and debugging for Kling AI API integrations) and 'when' (troubleshooting video generation, building observability) with explicit trigger phrases provided. | 3 / 3 |
Trigger Term Quality | Includes good natural trigger terms: 'klingai debug', 'kling ai logging', 'klingai troubleshoot', 'debug kling video generation', plus contextual terms like 'troubleshooting video generation' and 'observability'. These cover multiple natural phrasings a user might use. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive — targets a very specific niche (Kling AI API debugging/logging) with unique trigger terms that are unlikely to conflict with other skills. The combination of 'Kling AI' + 'debug/logging' creates a clear, narrow scope. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
64%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a solid, actionable skill with fully executable code for debugging Kling AI integrations. Its main strength is the completeness of the debug client implementation and supporting tools. Weaknesses include the lengthy inline code that could be better organized across files, and the lack of an explicit troubleshooting workflow with decision points and validation checkpoints.
Suggestions
Add an explicit troubleshooting decision tree: 'If HTTP 401 → check credentials; if task fails → inspect task_status_msg; if timeout → increase max_attempts or check task complexity'
Extract the KlingDebugClient class into a bundle file (e.g., kling_debug_client.py) and reference it from SKILL.md to improve progressive disclosure and reduce inline verbosity
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is mostly efficient with executable code, but the debug client is quite lengthy (~80 lines) and could be tightened. The log entry format example is useful but the overall content is on the verbose side for what it teaches. No egregious explanations of things Claude already knows, though. | 2 / 3 |
Actionability | Fully executable Python code and bash scripts throughout. The debug client, usage example, diagnostic script, and task inspector are all copy-paste ready with concrete implementations including JWT auth, request tracing, polling, and log dumping. | 3 / 3 |
Workflow Clarity | The usage section shows a clear try/finally pattern ensuring logs are always saved, and the polling loop has status checking. However, there's no explicit troubleshooting workflow (e.g., 'if auth fails, do X; if task fails, check Y') and no validation checkpoints for the diagnostic process beyond the bash script's basic auth check. | 2 / 3 |
Progressive Disclosure | The content is well-sectioned with clear headers (Debug Client, Usage, Log Format, Diagnostic Script, Task Inspector, Resources). However, the main debug client is a large inline code block that could benefit from being in a separate file, and there are no bundle files to reference. The external resource links at the end are helpful but minimal. | 2 / 3 |
Total | 9 / 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 — 9 / 11 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 | 9 / 11 Passed | |
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