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'.

Quality

82%

Does it follow best practices?

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Securityby

Passed

No known issues

Quality

Content

65%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is actionable with complete executable code, but it is a monolithic inline dump that ignores its own reference bundle, and it lacks validation checkpoints for its batch operations. Linking the existing reference files and adding verify steps would raise the weakest dimensions.

Suggestions

Replace the inline Analytics Aggregator/Cost/Export code with brief overviews that link to the existing references/ files (e.g., analytics-engine.md, export-to-csv.md, report-generator.md), keeping only a minimal quick-start example in SKILL.md.

Add a short sequenced workflow with validation checkpoints (e.g., confirm JSONL logs exist before aggregating, verify expected event counts after summarizing) for the batch aggregation and cost-analysis steps.

Reconcile the divergent implementations: the body's UsageAnalytics is JSONL-based while references/analytics-engine.md uses an in-memory dataclass model with get_summary/get_performance_metrics — pick one model or clearly state how they relate.

Dimension	Reasoning	Score
Conciseness	The body is code rather than explanatory fluff and avoids re-explaining concepts Claude knows, but it dumps four full class/function implementations inline that could be tightened or offloaded, given bundle files already exist.	2 / 3
Actionability	All four sections contain complete, executable Python (KlingEventLogger, UsageAnalytics, cost_analysis, export_usage_csv) that is copy-paste ready with concrete field names and logic.	3 / 3
Workflow Clarity	Sections are present but there is no sequenced workflow and no validation/verification checkpoints for batch log aggregation and multi-day cost analysis, which the rubric caps at 2 for batch operations.	2 / 3
Progressive Disclosure	Five bundle files exist in references/ (analytics-engine.md, errors.md, examples.md, export-to-csv.md, report-generator.md) but the body never links to them, and they use a divergent implementation, so content that should be separate is inline and references are unsignaled.	2 / 3
	Total	9 / 12 Passed

Description

100%

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 is well-constructed: it states concrete capabilities, gives explicit 'Use when' guidance plus natural trigger phrases, and occupies a distinctive niche. Third-person/infinitive voice is maintained throughout.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'Build usage analytics and reporting', 'tracking patterns', 'analyzing costs', 'building dashboards' — matching the anchor for multiple specific concrete actions.	3 / 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 triggers.	3 / 3
Trigger Term Quality	Provides explicit natural trigger phrases users would say ('klingai analytics', 'kling ai usage report', 'klingai metrics', 'video generation stats'), giving good coverage of common variations.	3 / 3
Distinctiveness Conflict Risk	Targets a clear niche (Kling AI video-generation analytics) with distinct, product-specific triggers, making conflict with other skills unlikely.	3 / 3
	Total	12 / 12 Passed

Validation

87%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 14 / 16 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	14 / 16 Passed

Repository: jeremylongshore/claude-code-plugins-plus-skills
Path: plugins/saas-packs/klingai-pack/skills/klingai-usage-analytics/SKILL.md
Commit: b2e8c53

Reviewed: 5 days ago

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