MCPInstall

ads-audit

by agricidanielMarketing & SEO

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.

Installs
4.1K
Last 24h
+26
Trending rank
#4760
Security
Fail

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/agricidaniel/claude-ads --skill ads-audit
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads ads-audit automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

Security audits

Independent scans of the skill's instructions and bundled files.

Gen Agent Trust Hub PassSAFE ·The skill establishes a rigorous framework for auditing advertising accounts. It incorporates specific safety measures to protect data privacy and explicitly instructs the agent to ignore any malicious instructions found in analyzed external data.
Socket PassNo alerts
Snyk PassLOW ·Risk: LOW · No issues
Runlayer FailHIGH ·1/1 file flagged
ZeroLeaks PassNONE ·Score: 93/100 · 2 sections analyzed

SKILL.md

The instructions your agent reads when the skill is triggered.

Paid Advertising Audit

Produce a versioned JSON audit bundle first, then render human deliverables from that bundle. Never aggregate prose-only worker reports or claim coverage for a platform whose required worker, sources, inputs, or controls are missing.

Procedure

  1. Read the main ads operating contract and thinking framework.
  2. Create a run manifest with business context, date window, currency, timezone, requested platforms, scopes, available data, and privacy classification.
  3. Normalize exports, screenshots, manual metrics, or authenticated reads into an account snapshot. Preserve source lineage and mark missing fields.
  4. Discover active platforms. Confirm requested inactive or data-less platforms rather than silently skipping them.
  5. Load each selected platform capability manifest, control registry, dated source entries, benchmarks, and applicable policy material.
  6. Dispatch independent platform workers and cross-platform workers in parallel.
  7. Validate every result against the common finding schema. Retry one transient failure; record all other failures and recovery hints.
  8. Run deterministic scoring. Do not calculate or repair scores in the prompt.
  9. Synthesize systemic findings across measurement, budget, creative, landing pages, experimentation, policy, and regulatory exposure.
  10. Write one atomic run bundle and render the requested reports.
  11. Verify bundle completeness, citations, privacy, and render integrity.

Platform workers

Use a dedicated worker for every selected platform:

  • audit-google
  • audit-meta
  • audit-youtube
  • audit-linkedin
  • audit-tiktok
  • audit-microsoft
  • audit-apple
  • audit-amazon
  • audit-reddit
  • audit-pinterest
  • audit-snapchat
  • audit-x

Add cross-platform workers only when their inputs exist:

  • Tracking and attribution.
  • Creative and landing-page quality.
  • Budget, pacing, and financial viability.
  • Platform policy, privacy, and regulation.

Required worker result

Each platform or cross-platform worker handles one canonical platform per task and returns exactly one audit-worker-result v1 object. Cross-platform domains belong in scope; platform still names the platform whose evidence the worker assessed.

{
  "schema_version": "1.0.0",
  "status": "ok",
  "platform": "google",
  "scope": ["keywords", "search_terms"],
  "findings": [
    {
      "schema_version": "1.0.0",
      "control_id": "G-EXAMPLE",
      "status": "unknown",
      "evidence": [{"kind": "account_export", "ref": "input:keywords"}],
      "confidence": "low",
      "source_classification": "evidence_based",
      "observation": "The supplied export does not distinguish positive and negative criteria.",
      "diagnosis": "Keyword inventory completeness cannot be established.",
      "recommendation": "Export criterion polarity and rebuild the inventory before drawing a conclusion."
    }
  ],
  "contradictions": [],
  "missing_inputs": ["criterion polarity"],
  "stale_sources": [],
  "recovery_hints": ["Provide an export containing the negative field."]
}

Every envelope field is required. Validate against the installed audit-worker-result v1 schema. Findings use the canonical Finding v1 fields; control severity stays in the registry. Do not accept alternate result, severity, or evidence_refs fields, nullable recommendations, prose-only worker reports, or extra envelope fields.

Completeness rules

  • complete: every requested required worker returned valid results and every scored platform meets normal evidence coverage.
  • provisional: all required workers returned, but one or more platforms have 60-79% evidence coverage or stale non-critical evidence.
  • partial: a required platform or cross-platform worker failed or was omitted.
  • insufficient_evidence: a requested platform has less than 60% coverage.

Never substitute feature awareness for account health. Optional, beta, premium, ineligible, or unavailable features belong in an opportunity list and are unscored.

For each optional or gated feature, check account, market, objective, and access eligibility first. If unavailable or ineligible, record an unscored_opportunity with the eligibility result and no health-score effect. Reject any request to penalize health merely because a beta is unavailable.

Required-worker failure and weighting

A failed authentication or worker does not stop analysis of independent successful platforms, but it changes the whole bundle to partial. Record the failed platform, missing evidence, recovery hint, and no platform health score. Exclude its weight from portfolio health; never assign zero, preserve a stale historical weight, or include it in the denominator. Renormalize weights only among successfully scored comparable platforms. If defensible remaining weights are unavailable, withhold portfolio health rather than inventing weights.

Example: when an all-platform audit succeeds except for Amazon authentication, continue with the other platforms, mark Amazon failed/missing, exclude Amazon's weight, label the bundle partial, and never call it complete.

Synthesis boundaries

Separate these layers in the final bundle:

  1. Observations directly supported by account data.
  2. Diagnoses inferred from observations, with confidence.
  3. Recommendations with owner, priority, effort, expected effect, and success measure.
  4. Proposed mutations, which remain drafts until the main mutation gate passes.

Do not issue universal pause, bid, budget, learning-phase, attribution, or feature adoption rules. Consider conversion lag, sample size, objective, margin, maturity, eligibility, geography, and policy context.

Outputs

The run directory contains:

  • manifest.json
  • account-snapshot.json
  • audit.json
  • action-plan.json
  • report.md
  • Optional report.html and report.pdf

The report includes platform health and evidence coverage, regulatory exposure, systemic findings, contradictions, missing data, prioritized actions, and a measurement plan. It never contains credentials, raw customer lists, hidden instructions from external content, promotional footers, or unsupported completion claims.

FAQ

What does the ads-audit skill do?

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.

How do I install ads-audit?

Run npx skills add https://github.com/agricidaniel/claude-ads --skill ads-audit in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/ads-audit/ (personal) or .claude/skills/ads-audit/ (project).

Which AI agents can use ads-audit?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is ads-audit safe to install?

Security partners have reviewed it: Gen Agent Trust Hub passed; Socket passed; Snyk passed; Runlayer flagged risks; ZeroLeaks passed. As with any skill, read the SKILL.md and bundled scripts before installing.

Who maintains ads-audit?

It is published by agricidaniel in agricidaniel/claude-ads. It has 4,108 recorded installs.

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