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ad-policy-compliance

Checks ad creative and landing pages against platform advertising policies for Google, Meta, LinkedIn, TikTok, and Reddit. Use when pre-checking ads for policy compliance, fixing rejected ads, reviewing restricted content, or navigating industry-specific ad rules; verify sensitive categories against live platform policy before launch.

Quality

82%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

71%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 highly actionable, concrete policy checklists with excellent violation→fix examples and a usable pre-launch template, and the review workflow is clearly sequenced. Its main weaknesses are redundancy and staleness-prone dated details inflating token cost, and the absence of any progressive disclosure — all five platform and six industry deep dives are inlined in one very long file instead of being split into reference files.

Suggestions

Split the per-platform sections (Google, Meta, LinkedIn, TikTok, Reddit) and per-industry sections (Finance, Healthcare, Alcohol, Political) into separate files under references/ (e.g., references/google-ads.md, references/industries.md), keeping SKILL.md as a concise overview with well-signaled one-level-deep links.

De-duplicate overlapping coverage: political, alcohol, financial/crypto, and trademark rules each appear in 2–3 sections (e.g., Meta Special Ad Categories vs. the Political Advertising section, Google trademark policies vs. the trademark comparison table) — consolidate each topic into a single canonical location.

Move volatile, time-stamped details (the "20% text rule removed 2021" note, X/Twitter political-ad ban reversal dates, appeal response times and success-rate percentages) into a clearly labeled live-policy/deprecated section or replace them with a single "verify against live platform policy" instruction so they don't silently go stale.

DimensionReasoningScore

Conciseness

The checklists are dense and information-rich rather than padded, but political, alcohol, financial, and trademark rules are each repeated across 2–3 sections, and volatile time-stamped details ("removed 2021", "banned since 2019, reversed 2023", appeal success-rate percentages) will go stale without being isolated in an old-patterns/deprecated section. Not a 2 since no basic concepts are over-explained, but clearly needs tightening.

3 / 5

Actionability

Fully concrete guidance: violation→fix tables with exact rewrite examples ("FAST SHIPPING on ALL ORDERS" → "Fast Shipping on All Orders"), specific safe-alternative phrasings, a rejection table mapping cause to fix per platform, appeal paths, and a copy-paste pre-launch checklist template — specific examples cover the common cases.

5 / 5

Workflow Clarity

A clear sequence exists (universal checks → platform-specific checks → industry-specific checks → APPROVED/BLOCKED result gate) with checklist checkpoints and a structured output template. Minor gaps: it never explicitly instructs how to combine the sections for a given engagement or how to proceed when platform and industry rules conflict, and the post-review feedback loop (what to do after a fix is applied) is only implicit. This is a review skill, so the destructive/batch validation cap does not apply.

4 / 5

Progressive Disclosure

The body is well-sectioned with clear headers, but it is a ~570-line monolithic document with no bundle files at all (no references/, scripts/, or assets/ exist). The per-platform deep dives (Google/Meta/LinkedIn/TikTok/Reddit sections) and per-industry rules clearly belong in separate one-level-deep reference files, matching the anchor "content that should be separate is inline"; it scores above 2 only because section structure and navigation within the file are good.

3 / 5

Total

15

/

20

Passed

Description

87%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 strong: it explicitly answers both what and when with concrete, natural trigger phrases, names all five target platforms, and stays in third-person voice with no fluff. The only weaknesses are minor — slightly generic core action verbs and a few missing natural keyword variants such as "disapproved" or "ad review".

DimensionReasoningScore

Specificity

Names concrete objects ("ad creative and landing pages") and all five platforms (Google, Meta, LinkedIn, TikTok, Reddit), plus actions like "fixing rejected ads" and "verify sensitive categories", but the core capability is a single verb ("checks") with variants, leaving minor coverage gaps relative to the comprehensive anchor 5.

4 / 5

Completeness

Clearly states what it does ("Checks ad creative and landing pages against platform advertising policies for Google, Meta, LinkedIn, TikTok, and Reddit") and explicitly answers when via "Use when pre-checking ads for policy compliance, fixing rejected ads, reviewing restricted content, or navigating industry-specific ad rules" with concrete trigger phrases, in third-person voice.

5 / 5

Trigger Term Quality

Natural user phrases like "policy compliance", "fixing rejected ads", "pre-checking ads", and "before launch" are present, but common variations such as "ad disapproved" or "ad review" are missing, placing it at good-but-not-comprehensive keyword coverage.

4 / 5

Distinctiveness Conflict Risk

Occupies a clear niche (ad policy compliance across five named platforms) with distinct triggers ("rejected ads", "restricted content", "industry-specific ad rules") that are unlikely to fire for unrelated skills.

5 / 5

Total

18

/

20

Passed

Validation

93%

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

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (575 lines); consider splitting into references/ and linking

Warning

Total

15

/

16

Passed

Repository

Synter-Media-AI/plugin

Path

skills/ad-policy-compliance/SKILL.md

Reviewed

Table of Contents

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