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

—

Does it follow best practices?

Impact

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No eval scenarios have been run

Securityby

Passed

No known issues

Quality

Content

80%

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

The body is lean and highly actionable with executable Python, but it functions as a monolithic inline implementation: the five existing reference files are never referenced from SKILL.md (and even define a divergent UsageAnalytics), and there is no sequenced workflow with validation checkpoints for the batch cost-analysis operation.

Suggestions

Replace the inline implementations in SKILL.md with a concise overview that links to the existing references (e.g., 'Event logging: see analytics-engine.md', 'Reports: see report-generator.md', 'CSV export: see export-to-csv.md', 'Errors: see errors.md') so the bundle is actually navigable.

Reconcile the divergent UsageAnalytics APIs: the body uses _read_events/daily_summary while analytics-engine.md uses record_event/get_summary — pick one and make the references consistent with the body.

Add an explicit workflow sequence with a validation checkpoint for the cost-analysis batch operation (e.g., verify logs exist for each date before summing credits, and handle empty days).

Dimension	Reasoning	Score
Conciseness	The body is lean: a one-sentence overview followed by code with no padding, no explanations of concepts Claude already knows, and no unnecessary prose, matching the 'lean and efficient; every token earns its place' anchor.	3 / 3
Actionability	Provides concrete, executable Python (KlingEventLogger, UsageAnalytics, cost_analysis, export_usage_csv) that is largely copy-paste ready, matching the 'fully executable code; copy-paste ready' anchor; minor per-block import reuse does not undermine this.	3 / 3
Workflow Clarity	Sections imply a logical pipeline (log -> aggregate -> analyze cost -> export) but there is no explicit sequencing, no validation checkpoints, and no feedback loop for the batch date-iteration in cost_analysis, matching the 'steps listed but validation gaps; checkpoints missing or implicit' anchor and triggering the batch-operation cap.	2 / 3
Progressive Disclosure	Five reference files exist (analytics-engine.md, errors.md, examples.md, export-to-csv.md, report-generator.md) but none are linked or signaled from the body, while full implementations are inlined in SKILL.md instead of being split out, matching 'references present but not clearly signaled; content that should be separate is inline'.	2 / 3
	Total	10 / 12 Passed

Description

90%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong description: third-person voice, explicit 'Use when' trigger guidance, natural trigger phrases, and a clearly bounded Kling AI analytics niche. The only weakness is that the named capabilities are somewhat abstract categories rather than a comprehensive list of concrete actions.

Dimension	Reasoning	Score
Specificity	Names the domain ('Kling AI video generation') and several actions ('Build usage analytics and reporting', 'tracking patterns, analyzing costs, or building dashboards'), but the actions are relatively high-level categories rather than a comprehensive list of concrete operations, matching the 'names domain and some actions, but not comprehensive' anchor.	2 / 3
Completeness	Explicitly 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, matching the top anchor.	3 / 3
Trigger Term Quality	Provides natural user-facing trigger phrases ('klingai analytics', 'kling ai usage report', 'klingai metrics', 'video generation stats') with good coverage of variations a user would actually say, matching the 'good coverage of natural terms' anchor.	3 / 3
Distinctiveness Conflict Risk	The 'Kling AI video generation' niche and 'klingai'-prefixed triggers are clearly distinguishable from other analytics skills and unlikely to fire for the wrong skill, matching the 'clear niche with distinct triggers' anchor.	3 / 3
	Total	11 / 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
Commit: 3022dd3

Reviewed: 1 day ago

Table of Contents

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