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ads-audit

Run a source-grounded paid-advertising audit for one or more of Google, Meta, YouTube, LinkedIn, TikTok, Microsoft, Apple, Amazon, Reddit, Pinterest, Snapchat, and X. Use for full ad checks, account health reviews, paid-media diagnostics, partial audits after authentication or worker failure, missing-platform weighting, beta-feature eligibility and scoring, spend audits, tracking audits, or prioritized opportunities and risks.

71

Quality

89%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

85%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.

A lean, well-sequenced instruction body with excellent validation and error-recovery structure and a concrete canonical result schema. Its weaknesses are unresolvable references to the operating contract, capability manifests, and finding/worker schemas, and the absence of a bundle that would let a reader actually load those materials.

Suggestions

Add explicit file paths for the artifacts the procedure depends on (e.g., 'Read ads/contract.md' and 'Load references/manifests/<platform>.json') or include them in a references/ directory so the operating contract, capability manifests, control registry, and audit-worker-result v1 schema are resolvable.

Show the exact invocation for dispatching platform workers (e.g., the agent prompt template or command for 'audit-google') so the dispatch step is copy-paste executable rather than implied.

State where the common Finding v1 schema lives and how severity is looked up from the control registry, since validation in step 7 currently depends on schemas the body only names.

DimensionReasoningScore

Conciseness

The body is dense operational instruction with no explanation of concepts Claude already knows ('Run deterministic scoring. Do not calculate or repair scores in the prompt.'), and the long JSON block is a canonical schema spec rather than padding, matching the 'every token earns its place' anchor.

5 / 5

Actionability

Provides exact worker names, a complete canonical JSON result example, numeric coverage thresholds (60-79%, <60%), and named output files, but key execution inputs are referenced without resolvable locations ('Read the main ads operating contract', 'Load each selected platform capability manifest, control registry', 'the installed audit-worker-result v1 schema' — no paths and no bundle files exist).

4 / 5

Workflow Clarity

The 11-step procedure is clearly sequenced with explicit validation checkpoints and feedback loops ('Validate every result against the common finding schema. Retry one transient failure; record all other failures and recovery hints', 'Verify bundle completeness, citations, privacy, and render integrity') plus a worked failure/weighting example.

5 / 5

Progressive Disclosure

Sections are well-organized, but the skill depends on external materials (the 'ads operating contract', 'platform capability manifest', 'control registry', 'audit-worker-result v1 schema') that are neither given file paths nor present in a references/ directory, leaving navigation to the core detail material unclear.

3 / 5

Total

17

/

20

Passed

Description

88%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.

A strong description that explicitly states what the skill does and when to use it, with specific enumerated capabilities and strong platform-name triggers. Its only gaps are a few natural synonyms (PPC, SEM, paid search/social) and minor overlap risk with broader analytics skills.

DimensionReasoningScore

Specificity

Lists many concrete actions ('full ad checks, account health reviews, paid-media diagnostics, partial audits after authentication or worker failure, missing-platform weighting, beta-feature eligibility and scoring, spend audits, tracking audits, prioritized opportunities and risks') with comprehensive coverage of the audit domain, matching the 5 anchor.

5 / 5

Completeness

Explicitly answers both what ('Run a source-grounded paid-advertising audit for one or more of Google, Meta, ...') and when ('Use for full ad checks, account health reviews, ... prioritized opportunities and risks') with a concrete, enumerated 'Use for' trigger clause.

5 / 5

Trigger Term Quality

Good natural keyword coverage including the 12 platform names, 'paid-advertising audit', 'ad checks', 'spend audits', and 'account health reviews', but common user variations like 'PPC', 'SEM', 'paid search', 'paid social', and 'campaigns' are missing, so it falls short of the 5 anchor's synonym comprehensiveness.

4 / 5

Distinctiveness Conflict Risk

The paid-advertising audit niche with a 12-platform list is clearly distinct, but trigger phrases like 'tracking audits' and 'spend audits' carry minor overlap risk with general marketing-measurement or analytics skills, matching 'mostly distinct; minor overlap risk'.

4 / 5

Total

18

/

20

Passed

Validation

100%

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

AgriciDaniel/claude-ads

Path

skills/ads-audit/SKILL.md

Reviewed

Table of Contents

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