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ads-audit

Claude-first, portable paid-media operations for agencies, consultants, and

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项目 README

来源文件：README.md

抓取于 2026年8月2日

Claude Ads, Claude-first paid-media operations across twelve advertising platforms

Claude Ads

Claude-first, portable paid-media operations for agencies, consultants, and in-house performance teams.

Claude Ads turns authorized exports or account reads into source-grounded audits, plans, creative workflows, experiments, monitoring, and reports. It is read-only by default. Live changes stay disabled until the exact platform and operation pass approval, idempotency, verification, audit, and rollback gates.

[!NOTE] Claude Ads ships from two homes: the public release at AgriciDaniel/claude-ads (MIT, no membership required) and the community mirror at AI-Marketing-Hub/claude-ads, where AI Marketing Hub Pro members get early access and direct collaboration.

Validated inputs flow through bounded workers into schema-valid findings and deterministic reports

What it does

  • Audits paid-media accounts with dated evidence and explicit confidence.
  • Plans campaigns, channels, budgets, measurement, and experiments.
  • Creates copy, image, video, and product-photo briefs and assets.
  • Monitors pacing, delivery, tracking, fatigue, policy, and performance.
  • Produces versioned JSON, then renders Markdown, HTML, and optional PDF.
  • Drafts safe account changes without applying them by default.
  • Reports missing data, stale sources, contradictions, and partial failures.

Platforms

Twelve first-class paid-advertising platform surfaces

SegmentPlatforms
Search, video, and socialGoogle Ads, Meta Ads, YouTube Ads, LinkedIn Ads, TikTok Ads, Microsoft Advertising, Reddit Ads, Snapchat Ads, X Ads
Commerce and retail mediaApple Ads, Amazon Ads, Pinterest Ads

Each platform has a focused skill, audit worker, control reference, capability declaration, and testable routing surface. The capability manifest is the authoritative record for live reads and writes.

Commands

Standalone installs use /ads. Claude Code plugins are namespaced and use /claude-ads:ads. Both load the same ads/SKILL.md contract.

CommandOutcome
/ads setupCreate the client, account, KPI, privacy, and guardrail profile
/ads audit [all|platform|scope]Run a complete or scoped evidence-backed audit
/ads planBuild channel, campaign, budget, competitor, and measurement plans
/ads createProduce copy, image, video, or product-photo assets
/ads launch --draftDraft a campaign mutation plan without changing the account
/ads monitorReview pacing, delivery, tracking, fatigue, policy, and performance
/ads optimize --draftDraft evidence-backed optimization changes
/ads experimentDesign or read out a controlled test
/ads reportRender a validated JSON run bundle
/ads research refreshRefresh platform, policy, API, benchmark, and ecosystem evidence
/ads validateValidate contracts, runs, capabilities, maturity, or release readiness
/ads status, /ads nextShow current status and the highest-priority blocker

Platform shortcuts such as /ads google, /ads meta, /ads amazon, and /ads reddit route to the matching platform audit.

Demo

Claude Ads command discovery inside Claude Code

The GIF shows the original command-discovery experience. The v2 command table above and the platform table are current and authoritative.

Installation

Claude Code is the canonical runtime. Codex, Gemini, Cursor, Windsurf, Goose, and compatible Agent Skills hosts can consume the same skill files where their runtime supports them.

Prefer the host's native plugin flow or a tagged release archive with a verified SHA-256 checksum. Never pipe a remote installer directly to a shell.

For Claude Code, the native plugin flow is:

/plugin marketplace add AgriciDaniel/claude-ads
/plugin install claude-ads@ai-marketing-hub-claude-ads

Or install from a local clone of the public repository:

git clone https://github.com/AgriciDaniel/claude-ads.git
cd claude-ads
bash install.sh --source=local

Select another standalone host explicitly:

bash install.sh --target=codex --source=local
bash install.sh --target=gemini --source=local --no-deps

PowerShell uses the same managed ownership model:

git clone https://github.com/AgriciDaniel/claude-ads.git
Set-Location claude-ads
.\install.ps1 -Source local

Managed dependencies support CPython 3.11 and 3.12 on the declared Linux, macOS, and Windows wheel matrix. Unsupported interpreters fail before the destination changes. Use --no-deps or -NoDeps for a skill-only install.

Browser capture requires an operator-installed Playwright browser payload. PDF rendering requires the host's WeasyPrint and Pango system libraries. These are documented in the external runtime dependency manifest.

Uninstall only manifest-owned files:

bash uninstall.sh --target=claude

The PowerShell equivalent is uninstall.ps1.

Architecture

One conductor dispatches platform and cross-platform workers, then validates and renders a canonical JSON bundle

One conductor owns scope, policy, aggregation, and final artifacts. Workers analyze bounded slices and return schema-valid findings. Required-worker failure makes the run partial. It is never silently presented as a complete audit.

The canonical result is versioned JSON. Markdown, HTML, and PDF are renderings of the same validated run bundle.

Scoring and evidence

Health and evidence coverage remain separate; scoring requires an approved platform profile

Controls use pass, fail, unknown, or not_applicable.

  • Health, evidence coverage, regulatory exposure, and opportunities stay separate.
  • Unknown controls reduce evidence coverage without changing known health.
  • Coverage of 80% or more is graded, 60 to 79% is provisional, and below 60% is insufficient evidence.
  • Optional, beta, premium, unavailable, and ineligible features stay unscored.
  • A disabled or unapproved platform profile produces no health score.
  • A failed platform is excluded from portfolio scoring and makes the run partial.

See the scoring reference and production implementation in claude_ads_core/scoring.py.

Account safety

All adapters are read-only by default. Applying a change requires:

  1. A tested and enabled capability for the exact operation.
  2. Explicit account and object IDs.
  3. A human-readable before and after diff with blast radius.
  4. Owner approval within account-defined ceilings.
  5. An idempotency key, audit destination, rollback, and verification window.
  6. Verification that remote state still matches the mutation precondition.

Missing ceilings mean no write. Permanent deletion is not supported in v2. Credentials belong in environment variables, an OS keychain, or an approved secret manager. They never belong in the repository, profiles, reports, or logs.

Evidence and release controls

The public-safe control-plane/ records product boundaries, dated sources, claims, capabilities, safety rules, privacy rules, ecosystem decisions, and release requirements.

  • No source means no current platform claim.
  • No implementation, fixture, and test means no capability claim.
  • No approval and rollback means no account mutation.
  • No independent verification means no release.

See the release requirements and publishing policy.

Development

Create a virtual environment and run the complete suite:

python3.12 -m venv .venv
.venv/bin/python -m pip install --no-deps -e .
.venv/bin/python -m pip install --require-hashes --only-binary=:all: -r requirements.lock
.venv/bin/python -m pip install --require-hashes --only-binary=:all: -r requirements-dev.lock
.venv/bin/python -m pip check
.venv/bin/python -m pytest -q

Useful focused checks:

python -m claude_ads_core --version
python -m claude_ads_core validate finding path/to/finding.json
bash -n install.sh uninstall.sh

Repository map

ads/                  main skill, interface metadata, and shared references
skills/               platform and lifecycle skills
agents/               platform, cross-platform, research, and verifier workers
claude_ads_core/      typed contracts, adapters, validation, and scoring
control-plane/        evidence, capability, safety, maturity, and release state
scripts/              browser, creative, reporting, and release helpers
evals/                routing and behavioral evaluation cases
tests/                deterministic, security, installer, and adapter tests

Privacy

Client data, raw private research, captured prompts, credentials, account exports, and agent transcripts must never enter Git history, reports, or release archives. Keep credentials in environment variables, an OS keychain, or an approved secret manager.

License

Original Claude Ads code and documentation are available under the MIT License. Third-party APIs, trademarks, documentation, and cited artifacts remain subject to their own terms. Review the source ledger and third-party notices before importing external work.

其他

低风险

  • 来源需自行核对维护者身份。
  • 未检测到明显脚本安装指令。
  • 可能需要外部 token、网络权限或第三方服务。
  • 未检测到高风险命令。
  • 扫描发现：0 条。

Codex — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库：git clone https://github.com/AgriciDaniel/claude-ads.git
  3. 将 "skills/ads-audit" 文件夹复制到 Codex 的 skills 目录中。
  4. 重启 Codex 让新的 skill 生效。

Codex — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Codex 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Codex 让新的 skill 生效。

Claude Code — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库：git clone https://github.com/AgriciDaniel/claude-ads.git
  3. 将 "skills/ads-audit" 文件夹复制到 Claude Code 的 skills 目录中。
  4. 重启 Claude Code 让新的 skill 生效。

Claude Code — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Claude Code 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Claude Code 让新的 skill 生效。

Cursor — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库：git clone https://github.com/AgriciDaniel/claude-ads.git
  3. 将 "skills/ads-audit" 文件夹复制到 Cursor 的 skills 目录中。
  4. 重启 Cursor 让新的 skill 生效。

Cursor — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Cursor 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Cursor 让新的 skill 生效。

GitHub Copilot — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库：git clone https://github.com/AgriciDaniel/claude-ads.git
  3. 将 "skills/ads-audit" 文件夹复制到 GitHub Copilot 的 skills 目录中。
  4. 重启 GitHub Copilot 让新的 skill 生效。

GitHub Copilot — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 GitHub Copilot 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 GitHub Copilot 让新的 skill 生效。

Windsurf — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库：git clone https://github.com/AgriciDaniel/claude-ads.git
  3. 将 "skills/ads-audit" 文件夹复制到 Windsurf 的 skills 目录中。
  4. 重启 Windsurf 让新的 skill 生效。

Windsurf — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Windsurf 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Windsurf 让新的 skill 生效。
查看 SKILL.md 原文
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."

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 finding fields

Each worker returns conclusions, not files:

{
  "status": "ok",
  "platform": "google",
  "findings": [
    {
      "control_id": "G-EXAMPLE",
      "result": "pass|fail|unknown|not_applicable",
      "severity": "critical|high|medium|info",
      "confidence": "high|medium|low|none",
      "source_classification": "evidence_based|practitioner|contested|folklore",
      "observation": "What the supplied data demonstrates",
      "evidence_refs": ["input:...", "source:..."],
      "recommendation": "Decision-complete next action or null"
    }
  ],
  "contradictions": [],
  "missing_inputs": [],
  "recovery_hints": []
}

Validate against the repository schema rather than relying on this illustrative fragment when the installed schema is available.

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.

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