SkillAtlasSkill 详情

ad-account-auditor

This is the open-source content repository behind

审核状态:已审核Quality 80Security 88

复制安装命令

用 Codex 或 Claude 安装复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它先审查 Skill 页面再帮你安装。

复制前请先查看来源、License 和安全提示。

项目 README

来源文件:README.md

抓取于 2026年9月4日

Skill Store — Marketplace Repository

This is the open-source content repository behind Skill Store. It stores every approved Agent Skill, the records that go with it, and the automated security audits published with each skill.

This repo is a companion to the Skill Store platform, not the place to submit skills. Skills are added through skillstore.io — its review pipeline writes to this repo automatically. Please do not open a pull request here to add a skill; PRs adding skills will be closed. See Contributing a skill below.

Installing a skill

The recommended way to install any skill is the skillstore CLI — one command works for both Claude Code and Codex:

npx skillstore add author/skill-name

For example:

npx skillstore add aiskillstore/code-review

It downloads the skill and drops it into the right skills/ directory for your tool. Claude Code auto-discovers it; for Codex, restart the session.

Prefer to do it by hand, or installing via Claude Web? See the full Installation Guides for every method (CLI, manual, and ZIP upload) and the scope directories (~/.agents/skills/, .claude/skills/, ~/.claude/skills/, .codex/skills/, …).

Contributing a skill

Submit through the platform — not through a pull request:

  1. Go to skillstore.io/submit.
  2. Enter the GitHub repository URL that contains your SKILL.md.
  3. Your submission runs through automated security analysis.
  4. A maintainer reviews and approves it.
  5. On approval, the skill is published here and appears on skillstore.io.

What makes a valid skill

  • SKILL.md — the skill definition (required, per the Agent Skills spec)
  • Supporting files the skill references (optional)
  • LICENSE (recommended)

Security audit

Every submission is scanned automatically before it can be published. The audit flags things like:

  • Dangerous code patterns (eval, exec, raw system commands)
  • File access outside the project scope
  • Network calls to external hosts
  • Obfuscated or minified code
  • Credential / secret handling

Security analysis is report-only: findings inform maintainers and users, but a risk result does not automatically block an otherwise approved skill from being published. See our Security Trust Center for the methodology, limitations, and risk-level definitions.

Live Security Passport example:

Skillstore security

Repository layout

.
├── skills/        # Approved, published skills (one folder each, with SKILL.md)
├── pending/       # Submissions awaiting review
├── packages/
│   ├── cli/       # The `skillstore` CLI (npx skillstore add …)
│   └── skillstore/
├── schemas/       # JSON schemas for skill records
├── scripts/       # Maintenance & scoring scripts
└── .github/workflows/   # Submission, audit, and sync automation

The contents of this repo are maintained by Skill Store's automated pipeline. Manual changes are limited to maintainers.

Links

License

The marketplace catalog is MIT-licensed. Individual skills carry their own licenses — check each skill's LICENSE file.

数据与 AI内容与创作

中风险

  • 来源需自行核对维护者身份。
  • 未检测到明显脚本安装指令。
  • 未检测到明显外部权限要求。
  • 未检测到高风险命令。
  • 扫描发现:1 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/aiskillstore/marketplace.git
  3. 将 "skills/aaron-he-zhu/ad-account-auditor" 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/aiskillstore/marketplace.git
  3. 将 "skills/aaron-he-zhu/ad-account-auditor" 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/aiskillstore/marketplace.git
  3. 将 "skills/aaron-he-zhu/ad-account-auditor" 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/aiskillstore/marketplace.git
  3. 将 "skills/aaron-he-zhu/ad-account-auditor" 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/aiskillstore/marketplace.git
  3. 将 "skills/aaron-he-zhu/ad-account-auditor" 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: ad-account-auditor
slug: aaron-ad-account-auditor
displayName: "Ad Account Auditor · 付费广告账户审计"
summary: "付费广告账户审计/ROAS评分"
description: 'Use when auditing a paid ad account for incremental contribution, wasted spend, or measurement integrity before scaling; runs a typed 20-item ROAS profile with verified vetoes and a SHIP/FIX/BLOCK/UNDECIDED gate on own exported data. Not for campaign structure design — use campaign-architect; not for creative production — use ad-creative-builder. 付费广告账户审计/ROAS评分'
version: "19.0.0"
license: Apache-2.0
compatibility: "Claude Code and compatible agent-skill hosts"
homepage: "https://github.com/aaron-he-zhu/aaron-marketing-skills"
when_to_use: "Use when checking whether a paid account or portfolio is safe to launch or scale. Requires normalized own-data outcomes, attribution windows, currency, conversion lag, and business constraints."
argument-hint: "<campaign + outcome exports> <currency/window/lag> [profile]"
allowed-tools: WebFetch
class: auditor
metadata: {"author": "aaron-he-zhu", "version": "19.0.0", "discipline": "ad", "phase": "activate", "geo-relevance": "medium", "hermes": {"tags": ["marketing", "ad", "activate"], "category": "ad"}, "openclaw": {"emoji": "🎯", "homepage": "https://github.com/aaron-he-zhu/aaron-marketing-skills"}}

Ad Account Auditor

Audit one paid-media account or portfolio for incremental contribution and operating quality under declared constraints. Platform-reported ROAS is one input, never the objective or truth set by itself.

When This Must Trigger

  • Before launching, materially increasing spend, or changing a risky bid/targeting strategy.
  • When tracking, attribution inflation, unsafe placements, claims, or wasted spend are in doubt.
  • When the user requests a ROAS/RQS account audit from their exports.

Quick Start

Audit this USD account for direct response using 7-day click, 3-day lag, and $120 CAC ceiling.
Run the incremental-profit profile against the holdout and order-ID exports.

Skill Contract

Reads: one normalized account/portfolio evidence set. Writes: only a permissioned v3 artifact. Done when: required context and all 20 states are explicit, vetoes use verified evidence, and scorer output is reported without executing spend changes.

This skill judges. conversion-signal-qa, attribution-reconciler, campaign-architect, ad-creative-builder, and budget-pacing-monitor build/fix the inputs. Never enable campaigns, change bids, upload audiences, or scale budgets without separate explicit approval.

Data Sources

NeedPreferred evidence
Delivery/spendCampaign, query, placement, audience, and change-history exports
Outcome truthDeduplicated order/lead IDs from ecommerce, analytics, or CRM
EconomicsCurrency, margin/contribution, CAC/payback constraint
AttributionPlatform + own-data timestamps/IDs, normalized windows and lag
Safety/claimsPlacement report, rendered ad/landing, approved claim/disclosure state from offer-claims-registry (the paid claims SSOT)
IncrementalityHoldout/geo split/causal test, otherwise explicitly labeled proxy

Instructions

Runtime and Setup

Read ../../../references/auditor-runbook.md, scoring-semantics.md, roas-benchmark.md, and the ROAS catalog entry. Standalone installs use bundled immutable references/auditor-runtime.md; never fetch mutable main. Before deterministic calls, follow runtime-invocation.md, resolve AARON_SKILLS_ROOT="${CLAUDE_PLUGIN_ROOT:-$(git rev-parse --show-toplevel 2>/dev/null || true)}", and require the scorer, validator, and typed catalogs. If unavailable, return score_state: NOT_SCORED / score_confidence: not_scored with no gate verdict or persistent artifact.

Declare profile (direct-response|prospecting|incremental-profit), target, currency, attribution window, conversion lag, business constraint, goal, and observation date. If any required context is missing, return NEEDS_INPUT/UNDECIDED.

Evidence and Scoring

  1. Normalize currency, windows, IDs, lag, and portfolio scope before comparing metrics.
  2. Score all 20 R1..S5 criteria from the benchmark with source/date/type/confidence.
  3. Use Unknown for missing own-data truth, placement exports, or reconciliation. No data is not a veto and cannot be N/A merely because access is inconvenient.
  4. Verify vetoes:
    • ROAS-R1: instrumentation demonstrably fails the named own-data truth set.
    • ROAS-R2: material double-counting/inflation is demonstrated.
    • ROAS-O1: material claim/disclosure failure against the offer-claims-registry approved state.
    • ROAS-O2: applicable platform/restricted-category violation.
    • ROAS-A1: placement evidence demonstrates a material safety breach.
  5. Run the typed scorer. Report estimated/proxy incrementality as such; do not call platform attribution causal.

§2 ROAS Worked Examples

  • Complete direct-response profile, raw 78, no veto/fail: DONE/SHIP, final 78.
  • Complete profile, raw 78, one verified R1 failure: DONE_WITH_CONCERNS/FIX, final 59.
  • Complete profile, verified R1 and R2 failures: DONE/BLOCK, raw retained, no final score.
  • Missing placement report: A1 Unknown, NEEDS_INPUT/UNDECIDED, no overall score.

§3 ROAS Guardrails

  • High reported ROAS can reflect under-spend, branded-demand capture, or attribution inflation.
  • Learning-phase disruption is an S2 finding, not an automatic veto.
  • ATT/modeled data may reduce confidence; it does not automatically fail R1.
  • Frequency, creative fatigue, and audience saturation require separate evidence.
  • Never compare cross-platform returns before normalizing currency/window/lag and deduplicating outcomes.

§5 ROAS Translation

Lead with business impact and evidence. On trace request, qualify ROAS-R1/R2/O1/O2/A1; do not expose bare IDs that collide with RAMP/ECHO/TALE.

Report and Verdict

Begin with the auditor-runbook's exact typed conversation header. Never replace status, verdict, or score_state with prose; list each explicitly missing qualified item as ``ID: `unknown``` before findings.

Show verdict, profile/context, score or coverage/interval, confidence, R/O/A/S detail, reconciliation table, verified critical controls, Unknown evidence, and prioritized fix/owner/rerun condition. The scorer owns status/verdict and the 59 ceiling.

Validation Checkpoints

  • Scope/currency/window/lag/constraint/goal are explicit.
  • Own-data outcome truth is separated from platform self-report.
  • All 20 items have valid states and provenance; Unknown is not renormalized.
  • Veto failures are positively verified.
  • No spend/account mutation occurred without separate approval.

Persistence

Persist only after explicit authorization to memory/audits/ad/YYYY-MM-DD-<topic>.md. Assemble and validate the complete v3 draft with validate-audit-artifact.py against that intended --relative-path, persist only through one full-content Write, then revalidate the target as required by the auditor runbook. Edit/shell/MCP mutations of the reserved sink are unsupported. Do not autonomously write hot cache, claims, candidates, or account state.

Reference Materials

Next Best Skill

发现问题?提交给管理员复核

评分:

评论 (0)

暂无评论,成为第一个评论者吧!