ads-amazon
Audit Amazon Ads profiles, regions, Sponsored Products, Sponsored Brands, Sponsored Display, DSP, portfolios, targeting, search…
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
Claude Code
$ npx -y skills add AgriciDaniel/claude-ads --skill ads-audit --agent claude-code
Codex
$ npx skills add AgriciDaniel/claude-ads --skill ads-audit -a codex -y
Cursor
$ npx skills add AgriciDaniel/claude-ads --skill ads-audit -a cursor -y
Gemini CLI
$ npx skills add AgriciDaniel/claude-ads --skill ads-audit -a gemini-cli -y
GitHub Copilot
$ npx skills add AgriciDaniel/claude-ads --skill ads-audit -a github-copilot -y
OpenCode
$ npx skills add AgriciDaniel/claude-ads --skill ads-audit -a opencode -y
Windsurf
$ npx skills add AgriciDaniel/claude-ads --skill ads-audit -a windsurf -y
name: ads-audit description: "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."
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.
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.
Use a dedicated worker for every selected platform:
Add cross-platform workers only when their inputs exist:
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.
scored platform meets normal evidence coverage.
60-79% evidence coverage or stale non-critical evidence.
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.
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.
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 p
Claude Ads is a free, open source skill for Claude Code. Give it your ad account data and it tells you what is working, what is broken, and what to fix first, across twelve ad platforms. It is made for agencies, consultants, and in-house marketing teams.
Audit Amazon Ads profiles, regions, Sponsored Products, Sponsored Brands, Sponsored Display, DSP, portfolios, targeting, search…
Audit Apple Ads measurement, AdServices and AdAttributionKit, campaign and keyword structure, Search Match, App Store placements,…
Audit cross-platform attribution, conversion definitions, reporting windows, GA4, AdServices and AdAttributionKit, MMPs, browser…
Plan and review paid-media budgets, bidding, pacing, marginal return, targets, constraints, and allocation across supported…
Research competitor paid-ad presence, messaging, creative, formats, landing pages, keyword and auction signals, transparent ad…
Create source-grounded paid-ad campaign concepts, messaging, copy, creative briefs, and production plans from a validated brand…
When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use…
Comprehensive SEO analysis for any website or business type. Full site audits, single-page analysis, technical SEO (crawlability,…
Turn the current project website into a short, polished, shareable launch video using Hyperframes. Use when someone says "/brag",…
Full website GEO+SEO audit with parallel subagent delegation. Orchestrates a comprehensive Generative Engine Optimization audit…