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ad-creative

Create, iterate, and scale paid ad creative for Google Ads, Meta, LinkedIn, TikTok, and similar platforms. Use when generating headlines, descriptions, primary text, or large sets of ad variations for testing and performance optimization.

67

Quality

81%

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

67%Scale 1-5

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

This is a well-structured skill that provides actionable frameworks for ad creative generation and iteration at scale. Its strengths are the clear output formats, platform specs, and the iteration workflow with performance data analysis. Its main weaknesses are verbosity (explaining copywriting fundamentals Claude already knows) and some unverifiable tool/file references.

Suggestions

Trim the 'Writing Quality Standards' section significantly — Claude already knows basic copywriting principles like active voice and specificity. Keep only the ad-specific guidance (e.g., RSA combination rules).

Move the full platform specs tables to the referenced references/platform-specs.md file and keep only a brief summary or the most critical limits inline.

Verify and clarify tool integration commands — the bash example references 'node tools/clis/google-ads.js' which may not exist. Either confirm these tools are available or remove the specific paths.

DimensionReasoningScore

Conciseness

The skill is quite long (~300+ lines) and includes some sections that explain concepts Claude already knows (e.g., what RSAs are, basic copywriting principles like 'active voice over passive'). The platform specs tables are useful but could be more concise. The 'Writing Quality Standards' section largely restates copywriting fundamentals. However, the structured formats and iteration frameworks add genuine value.

3 / 5

Actionability

The skill provides concrete output formats with character counts, CSV templates, iteration log structures, and specific platform specs. The workflow steps are clear and executable. However, the tool integration commands (google-ads, meta-ads) appear to reference tools that may not actually exist in the environment, and the bash example references a path that isn't validated. The copy generation guidance is more framework than executable code.

4 / 5

Workflow Clarity

The skill has clear multi-step workflows for both generation and iteration modes, with explicit validation steps (Step 3: Validate Against Specs). The iteration workflow includes analysis → generation → documentation. The batch generation workflow has waves and quality filters. Minor gap: no explicit error recovery if platform upload fails or if performance data is malformed.

4 / 5

Progressive Disclosure

The skill references external files (references/platform-specs.md, references/generative-tools.md) for detailed content, keeping the main file as an overview. It also references related skills and a product-marketing-context.md file. However, since no bundle files were provided, we can't verify these references exist. The main file itself is still quite long and could offload more content (e.g., the full platform specs tables could live in the referenced file).

4 / 5

Total

15

/

20

Passed

Description

95%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.

This is a strong skill description that clearly communicates its purpose, provides explicit trigger guidance, and uses natural terminology that marketers would employ. It names specific platforms and ad components, making it both discoverable and distinct from general copywriting or content creation skills. Minor improvement could come from listing one or two more specific actions.

DimensionReasoningScore

Specificity

Lists several specific actions ('create, iterate, and scale paid ad creative', 'generating headlines, descriptions, primary text, large sets of ad variations') and names specific platforms. Minor gaps—could mention things like CTA generation, audience targeting copy, or format-specific constraints.

4 / 5

Completeness

Clearly answers both 'what' (create, iterate, and scale paid ad creative for multiple platforms) and 'when' ('Use when generating headlines, descriptions, primary text, or large sets of ad variations for testing and performance optimization'). The 'Use when' clause is explicit and contains concrete trigger phrases.

5 / 5

Trigger Term Quality

Excellent coverage of natural terms users would say: 'paid ad', 'ad creative', 'Google Ads', 'Meta', 'LinkedIn', 'TikTok', 'headlines', 'descriptions', 'primary text', 'ad variations', 'testing', 'performance optimization'. These are the exact terms marketers use.

5 / 5

Distinctiveness Conflict Risk

Highly distinctive niche—paid advertising creative across specific named platforms. The combination of platform names, ad-specific terminology, and the focus on variations/testing makes this unlikely to conflict with general copywriting or marketing skills.

5 / 5

Total

19

/

20

Passed

Validation

90%

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

Validation10 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

10

/

11

Passed

Repository
administrakt0r/AI-Agents-Safe-Coding-Skills
Reviewed

Table of Contents

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