klingai-performance-tuning

Optimize Kling AI for speed, quality, and cost efficiency. Use when improving generation times or finding optimal settings. Trigger with phrases like 'klingai performance', 'kling ai optimize', 'faster klingai', 'klingai quality settings'.

Quality

—

Does it follow best practices?

Impact

—

No eval scenarios have been run

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 token-efficient and packed with concrete, actionable code and data, but the code snippets are not self-contained and the skill fails to use its own reference bundle, leaving duplicate content inline and unsignaled. Workflow guidance also lacks validation checkpoints for batch operations.

Suggestions

Make the code snippets self-contained: define or point to BASE, get_headers()/auth, and the client object so examples are copy-paste ready instead of relying on undefined helpers.

Add validation/feedback to the tuning workflow (e.g., after benchmarking, compare results and confirm generation time improved before adopting a config), especially for the batch-submission step.

Move the inline benchmark and caching code into the existing references/ files (performance-profiler.md, caching-layer.md) and link to them one level deep from the body instead of duplicating the content inline.

Dimension	Reasoning	Score
Conciseness	The body is lean and code-dense with tables and a checklist; it does not explain concepts Claude already knows (no 'what is Kling AI' or library primers). Every section earns its place, matching the anchor 3 lean example rather than the padded anchor 2.	3 / 3
Actionability	Concrete code, specific timing/credit tables, and a checklist are present, but snippets reference undefined BASE, get_headers(), and client and skip imports, so they are not copy-paste ready. This fits anchor 2 ('missing key details') better than the fully-executable anchor 3.	2 / 3
Workflow Clarity	The Optimization Checklist gives a sequence, but there are no validation checkpoints or feedback loops, and batch submission guidance lacks verify-then-proceed steps. Per the rubric, batch operations without validation cap workflow clarity at 2.	2 / 3
Progressive Disclosure	Five reference files exist in references/ but none are linked or signaled from the body, and the inline benchmark and caching code duplicates content that belongs in those files. This matches anchor 2 ('references present but not clearly signaled; content that should be separate is inline').	2 / 3
	Total	9 / 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: it states a specific niche, gives explicit 'Use when' guidance, and lists natural trigger phrases. The only weakness is specificity, since 'Optimize' is a single abstract action rather than a list of concrete operations.

Dimension	Reasoning	Score
Specificity	Names the Kling AI domain and a couple of actions ("Optimize Kling AI for speed, quality, and cost efficiency", "improving generation times or finding optimal settings"), but does not list multiple specific concrete actions. It is above the vague anchor 1 yet short of the multi-action anchor 3.	2 / 3
Completeness	Explicitly states what it does ("Optimize Kling AI for speed, quality, and cost efficiency") and when to use it ("Use when improving generation times or finding optimal settings. Trigger with phrases like..."). Both what and when are explicit, matching the anchor 3 example.	3 / 3
Trigger Term Quality	Provides four natural trigger variations ("klangai performance", "kling ai optimize", "faster klangai", "klingai quality settings") giving good coverage of phrases a user would say. Not a 2 because coverage is broad rather than just 'some relevant keywords'.	3 / 3
Distinctiveness Conflict Risk	Targets a clear niche (Kling AI performance) with distinct klingai-specific trigger phrases, making it unlikely to fire for unrelated skills. Matches the anchor 3 'clear niche with distinct triggers'.	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

Discovery Implementation Validation

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