Atlas / Skills / agricidaniel / Ads Audit

Ads AuditSAFE

skills/agricidaniel/ads-audit

Claude-first paid-media operations skill for Claude Code across 12 ad platforms (Google, Meta, YouTube, LinkedIn, TikTok, Microsoft, Apple, Amazon, Reddit, Pinterest, Snapchat, X): source-grounded audits, deterministic scoring, versioned JSON reports, and capability-gated account changes.

Verdict
SAFE
Grade
B
Trust score
89 /100
Version
—
Hosts
—
License
MIT
Stars
9,768
01

Overview

Claude-first paid-media operations skill for Claude Code across 12 ad platforms (Google, Meta, YouTube, LinkedIn, TikTok, Microsoft, Apple, Amazon, Reddit, Pinterest, Snapchat, X): source-grounded audits, deterministic scoring, versioned JSON reports, and capability-gated account changes.

Read from source at commit e86534039e71OBSERVED · 2026-10-07
02

What it tells the agent

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
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:

```json
{
  "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 unsuppo
03

Trust audit

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

LayerWhat it checksResult
L0Provenance & inventoryPASS
L1Static analysis of the codeNA
L2Instruction surface (what it tells the agent)PASS
L3Class-specific surfacePASS
L4Behavioural (sandbox)SKIPPED

What the source does

Filesystem
none-observed
Network
none-observed
Shell
none-observed
Dependencies
pinned
Secrets in source
none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.

Audited 2026-10-07 · audit v0.4.1 · source sha e86534039e71full audit observations/trust-audit/skill/agricidaniel__ads-audit.json · Report an issue / request a re-scan
04

Audit history

Every audit this skill has had.

DateSourceVerdictGradeScoreChange
2026-10-07e86534039e71SAFEB89first audit
05

Questions

What does the Ads Audit skill do?

Claude-first paid-media operations skill for Claude Code across 12 ad platforms (Google, Meta, YouTube, LinkedIn, TikTok, Microsoft, Apple, Amazon, Reddit, Pinterest, Snapchat, X): source-grounded audits, deterministic scoring, versioned JSON reports, and capability-gated account changes.

Is Ads Audit safe to install?

The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.

What can Ads Audit access on my machine?

The audit observed no filesystem, network or shell use at all in its source.

How current is this page?

The grade is for one exact copy of the source (e86534039e71), read on 2026-10-07. The repository is watched, and a new audit runs when it changes — this is the first audit.

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