Ad Account Auditor
SkillMediaYour AI can audit your paid ad account exports for wasted spend, incremental contribution, and measurement problems before you scale budgets. It runs a 20-item return-on-ad-spend profile, backs its findings with verified issues, and ends with a clear verdict: ship, fix, block, or undecided.
Available today. Use it from your connected AI after setup.
No other account needed.
Export the ad account data you want reviewed, then ask your AI to run the audit before you increase budgets. It works on data you export yourself.
Then ask your AI: use the Ad Account Auditor skill
What your AI can do with it
- Audit ad account exports for wasted spend
- Check whether ads are driving incremental results
- Spot measurement problems that could distort your numbers
- Run a 20-item return-on-ad-spend profile
- Give a clear verdict: ship, fix, block, or undecided
- Back blocking decisions with verified issues
What this skill tells your AI
The instructions your AI receives, as published by aaron-he-zhu/aaron-marketing-skills in ad/activate/ad-account-auditor/SKILL.md and read by ahel’s review.
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
| Need | Preferred evidence |
|---|---|
| Delivery/spend | Campaign, query, placement, audience, and change-history exports |
| Outcome truth | Deduplicated order/lead IDs from ecommerce, analytics, or CRM |
| Economics | Currency, margin/contribution, CAC/payback constraint |
| Attribution | Platform + own-data timestamps/IDs, normalized windows and lag |
| Safety/claims | Placement report, rendered ad/landing, approved claim/disclosure state from offer-claims-registry (the paid claims SSOT) |
| Incrementality | Holdout/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.mdreferences/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
- Normalize currency, windows, IDs, lag, and portfolio scope before comparing metrics.
- Score all 20
R1..S5criteria from the benchmark with source/date/type/confidence. - 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.
- 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 theoffer-claims-registryapproved state.ROAS-O2: applicable platform/restricted-category violation.ROAS-A1: placement evidence demonstrates a material safety breach.
- 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
- Tracking: conversion-signal-qa
- Claims/disclosures: offer-claims-registry — the approved claim/disclosure state behind
ROAS-O1 - Attribution: attribution-reconciler
- Structure/audience: campaign-architect
- Pacing: budget-pacing-monitor
Signals
- GitHub stars
- 3k
- Forks
- 359
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
ad-account-auditor- Source
- github.com/aaron-he-zhu/aaron-marketing-skills