klingai-usage-analytics
Build usage analytics and reporting for Kling AI video generation. Use when tracking patterns, analyzing costs, or building dashboards. Trigger with phrases like 'klingai analytics', 'kling ai usage report', 'klingai metrics', 'video generation stats'.
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-usage-analytics/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 description that clearly identifies its niche (Kling AI video generation analytics), provides explicit 'when' guidance with trigger phrases, and is distinctive enough to avoid conflicts. The main weakness is that the capability actions could be more specific—listing concrete outputs like specific report types, metric calculations, or dashboard components would strengthen it.
Suggestions
Add more specific concrete actions, e.g., 'calculate cost-per-video, track generation success rates, summarize daily/weekly usage volumes' to improve specificity.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (Kling AI video generation analytics) and some actions (tracking patterns, analyzing costs, building dashboards), but the actions are somewhat generic and not deeply specific—e.g., it doesn't specify what kinds of reports, what metrics are tracked, or what dashboard components are built. | 2 / 3 |
Completeness | Clearly answers both 'what' (build usage analytics and reporting for Kling AI video generation) and 'when' (use when tracking patterns, analyzing costs, or building dashboards) with explicit trigger phrases provided. | 3 / 3 |
Trigger Term Quality | Includes multiple natural trigger terms: 'klingai analytics', 'kling ai usage report', 'klingai metrics', 'video generation stats', plus contextual phrases like 'tracking patterns', 'analyzing costs', 'building dashboards'. Good coverage of variations a user might say. | 3 / 3 |
Distinctiveness Conflict Risk | Highly specific niche—Kling AI video generation analytics is a very distinct domain. The trigger terms are unique enough ('klingai analytics', 'klingai metrics') that this is unlikely to conflict with other skills. | 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.
The skill provides solid, executable Python code for Kling AI usage analytics with good structure across event logging, aggregation, cost analysis, and export. However, it reads more like a code library than a skill guide—it lacks workflow sequencing showing how to integrate these components, and the full implementations inline make the file longer than ideal for a SKILL.md overview.
Suggestions
Add a brief workflow section at the top showing the integration sequence: 1) Initialize logger, 2) Instrument video generation calls, 3) Run daily reports, 4) Verify output—with a validation step to confirm events are being logged correctly.
Move the full class implementations into separate bundle files (e.g., event_logger.py, analytics.py) and keep SKILL.md focused on usage patterns, key API surfaces, and quick-start examples.
Add a concrete integration example showing how KlingEventLogger.log_submission() and log_completion() wrap around an actual Kling API call.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The code is mostly efficient and well-structured, but the full class implementations are quite lengthy. Some methods like print_report and cost_analysis could be more concise. The skill assumes Claude's competence with Python but includes more boilerplate than strictly necessary for a skill file. | 2 / 3 |
Actionability | All code is fully executable, copy-paste ready Python with complete class definitions, concrete method signatures, and clear data structures. The event logger, analytics aggregator, cost analysis, and CSV export are all immediately usable. | 3 / 3 |
Workflow Clarity | The skill presents components (logger, aggregator, cost analysis, export) but doesn't explicitly sequence them into a workflow with validation checkpoints. There's no guidance on how to integrate the logger into the video generation pipeline, or how to verify that events are being logged correctly before running analytics. | 2 / 3 |
Progressive Disclosure | The content is organized into clear sections with headers, but all implementation details are inline in a single file. The full class implementations (~150+ lines of code) could be split into referenced files, with SKILL.md providing just the overview, key patterns, and usage examples. | 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 | |
- Commit
d41e58e
- Reviewed
Table of Contents
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.