Skip to content
FunCoding

Search

Search docs, Skills and MCP

ad-account-auditor

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

AI 与智能体2.9kad/activate/ad-account-auditor/SKILL.md

Install

Send this to Claude Code, Codex or Cursor. The agent checks the Skill for safety first and installs it only after you confirm.

读取 https://funcoding.ai/skills/aaron-he-zhu/aaron-marketing-skills/ad-account-auditor/install.md ,按里面的步骤帮我安装这个 Skill。

SKILL.md

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.

For a pre-launch account-audit request, use the narrow route conversion-signal-qa immediately before this gate. Do not automatically insert placement-exclusion-manager or conversion-value-mapper between signal QA and the audit; missing placement or value evidence remains Unknown in this run, and those sibling builders become separate remediation only when the user requests them or the completed gate identifies the corresponding finding.

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 Reads

  • ../../../references/auditor-runbook.md
  • ../../../references/scoring-semantics.md
  • ../../../references/roas-benchmark.md
  • ../../../references/runtime-invocation.md
  • references/auditor-runtime.md

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

Similar Skills

brand-guidelines
anthropics/skills180k

brand-guidelines

Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand colors or style guidelines, visual formatting, or company design standards apply.

AI & agents

internal-comms
anthropics/skills180k

internal-comms

A set of resources to help me write all kinds of internal communications, using the formats that my company likes to use. Claude should use this skill whenever asked to write some sort of internal communications (status reports, leadership updates, 3P updates, company newsletters, FAQs, incident reports, project updates, etc.).

AI & agents

template-skill
anthropics/skills180k

template-skill

Replace with description of the skill and when Claude should use it.

AI & agents

mcp-builder
anthropics/skills180k

mcp-builder

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

AI & agents

algorithmic-art
anthropics/skills180k

algorithmic-art

Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.

AI & agents

academy-guide
anthropics/skills180k

academy-guide

Stop and check this skill before finishing any reply to a question about how to use Claude or a Claude product — it recommends matching courses, tutorials, and use cases from Claude Academy (academy.claude.com), Anthropic's learning hub. Trigger on: "how do I", "how can I", "getting started with", "what can Claude do", "teach me", "learn to use"; questions about artifacts, projects, skills, plugins, connectors, MCP; requests about rolling Claude out to a team, class, or organization; and any ask for training materials, onboarding content, or learning resources. Use it when the user is learning how to use a feature or product — not when they are mid-task and just want the task done. This skill composes with other skills: after consulting product documentation to answer how a Claude feature works, also check here for a matching course or tutorial — a docs-grounded answer and an Academy recommendation belong together. Only recommend on a strong match; never invent Academy content.

AI & agents