klingai-job-monitoring
Track and monitor Kling AI video generation task status. Use when building dashboards, tracking batch jobs, or debugging stuck tasks. Trigger with phrases like 'klingai job status', 'kling ai monitor', 'track klingai task', 'klingai progress'.
68
83%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Passed
No known issues
Quality
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 clear 'when' triggers and a distinctive niche around Kling AI task monitoring. The main weakness is that the specific capabilities could be more concrete—listing actual actions the skill performs (e.g., 'query task status APIs, display progress percentages, retry failed tasks') rather than just use-case scenarios. Overall it performs well for skill selection purposes.
Suggestions
Add more specific concrete actions the skill performs, e.g., 'query task status, display progress, list failed tasks, retry stuck jobs' instead of just 'track and monitor'.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (Kling AI video generation) and a general action (track and monitor task status), but doesn't list multiple specific concrete actions beyond tracking/monitoring. 'Building dashboards, tracking batch jobs, debugging stuck tasks' are use cases rather than specific capabilities the skill performs. | 2 / 3 |
Completeness | Clearly answers both 'what' (track and monitor Kling AI video generation task status) and 'when' (building dashboards, tracking batch jobs, debugging stuck tasks) with explicit trigger phrases provided. | 3 / 3 |
Trigger Term Quality | Includes multiple natural trigger phrases like 'klingai job status', 'kling ai monitor', 'track klingai task', 'klingai progress' as well as broader terms like 'dashboards', 'batch jobs', 'stuck tasks'. Good coverage of variations users might say. | 3 / 3 |
Distinctiveness Conflict Risk | Very specific niche targeting Kling AI video generation task monitoring. The 'klingai' and 'kling ai' terms are highly distinctive and unlikely to conflict with other skills. | 3 / 3 |
Total | 11 / 12 Passed |
Implementation
77%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a strong, actionable skill with executable code covering single-task polling, batch tracking, stuck detection, and monitoring loops. The workflow is well-sequenced with proper error handling and timeout management. Minor improvements could be made in conciseness (trimming unused imports and slight verbosity) and progressive disclosure (the batch tracker could be split into a separate reference file).
Suggestions
Remove unused imports (datetime, Optional if not needed) and tighten the overview to just the endpoint list without restating what a task_id is.
Consider extracting the BatchTracker class and utility functions into a referenced helper file (e.g., batch_tracker.py) to keep SKILL.md as a concise overview with usage examples.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is mostly efficient with executable code and a useful status table, but includes some unnecessary elements like the `datetime` import that's never used, the `Optional` typing import, and the `print_report` method could be tighter. The overview sentence explaining what a task_id is adds minimal value. | 2 / 3 |
Actionability | Fully executable Python code throughout — polling, batch tracking, stuck detection, and the monitor loop are all copy-paste ready with concrete API endpoints, JWT auth setup, and real status values. The code is complete and functional. | 3 / 3 |
Workflow Clarity | The task lifecycle table clearly defines states and transitions. The batch monitor loop shows a clear sequence (submit → monitor → report → detect stuck). Error handling is present throughout with try/except blocks, timeout handling, and stuck task detection providing feedback loops for recovery. | 3 / 3 |
Progressive Disclosure | Content is well-structured with clear sections progressing from single task polling to batch monitoring, but the file is fairly long (~120 lines of code) and could benefit from splitting the BatchTracker class and utilities into a referenced module. The external resource links at the end are helpful but minimal. | 2 / 3 |
Total | 10 / 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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