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

75%

Does it follow best practices?

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Securityby

Passed

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tessl review fix ./plugins/saas-packs/klingai-pack/skills/klingai-usage-analytics/SKILL.md

Quality

Content

68%Weight 40%Scale 1-5

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

The body delivers strong, executable code but fails at progressive disclosure by inlining everything and ignoring its own reference bundle, and it lacks explicit workflow validation for batch/export operations. Tightening the overview to point at the existing reference files would lift both structure and token efficiency.

Suggestions

Replace the inlined code blocks in SKILL.md with a concise overview and link out to the existing references/ files (e.g., analytics-engine.md, export-to-csv.md, report-generator.md) so content is split one level deep and clearly signaled.

Add an explicit usage workflow with validation checkpoints — e.g., verify the JSONL log exists and is non-empty before aggregating, and confirm row counts after export_usage_csv — to satisfy the batch-operation feedback-loop requirement.

Have references/examples.md contain actual examples rather than 'See code examples above', since it currently points back to the body and adds no progressive-disclosure value.

Dimension	Reasoning	Score
Conciseness	The body is mostly lean code with brief section headers and avoids explaining concepts Claude already knows, though inlining the full implementation rather than summarizing keeps it longer than necessary for an overview.	4 / 5
Actionability	Provides complete, executable, copy-paste-ready Python classes and functions (KlingEventLogger, UsageAnalytics, cost_analysis, export_usage_csv) covering the common cases end-to-end.	5 / 5
Workflow Clarity	The components imply a sequence (log → aggregate → analyze cost → export) but there are no explicit checkpoints or validation, and batch/export operations without verification cap this at 3 per the rubric.	3 / 5
Progressive Disclosure	All implementation is inlined in SKILL.md while five bundle files exist in references/ (analytics-engine.md, errors.md, examples.md, export-to-csv.md, report-generator.md) but are never linked from the body, so content that belongs in separate files is inlined and references are not signaled.	2 / 5
	Total	14 / 20 Passed

Description

82%Weight 40%Scale 1-5

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 with a clear what/when structure and explicit, natural trigger phrases tied to a distinct niche. Its only weakness is moderate specificity — the action verbs stay generic relative to the fuller capability set shown in the body.

Dimension	Reasoning	Score
Specificity	Names the domain ('Kling AI video generation') and two concrete actions ('usage analytics and reporting'), but the action list is not comprehensive — logging, cost analysis, and CSV export from the body are not surfaced in the description.	3 / 5
Completeness	Clearly answers 'what' ('Build usage analytics and reporting for Kling AI video generation') and 'when' ('Use when tracking patterns, analyzing costs, or building dashboards') with concrete trigger phrases.	5 / 5
Trigger Term Quality	Provides explicit natural phrases users would say ('klingai analytics', 'klingai metrics', 'video generation stats') with some variation, though a few common synonyms or file-format terms are missing.	4 / 5
Distinctiveness Conflict Risk	Scoped to a clear niche (Kling AI video generation analytics) with distinct, product-specific trigger phrases, giving minimal overlap with other skills.	5 / 5
	Total	17 / 20 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: 8585d6d

Reviewed: 13 days ago

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