ads-audit
Skill10k starsChanged 17 days agoMITView on GitHub
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.
We split this skill into the rules it gives an agent
10 rules. 0 are common, 8 are distinctive, and 2 are very specific to this project.
0
Common
Most of the 11 edited versions we read keep these.
8
Distinctive
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 14
Read the main ads operating contract and thinking framework.
- Distinctive: line 15
Create a run manifest with business context, date window, currency, timezone, requested platforms, scopes, available data, and privacy …
- Distinctive: line 26
Run deterministic scoring.
- Distinctive: line 29
Write one atomic run bundle and render the requested reports.
- Distinctive: line 30
Verify bundle completeness, citations, privacy, and render integrity.
- Distinctive: line 34
Use a dedicated worker for every selected platform:
- Distinctive: line 49
Add cross-platform workers only when their inputs exist:
- Very specific: line 88
Every envelope field is required.
- Distinctive: line 103
Never substitute feature awareness for account health.
- Distinctive: line 134
Do not issue universal pause, bid, budget, learning-phase, attribution, or feature adoption rules.
“Common” is measured: kept by at least 6 in 10 of the 11 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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
- Read the main
adsoperating contract and thinking framework. - Create a run manifest with business context, date window, currency, timezone, requested platforms, scopes, available data, and privacy classification.
- Normalize exports, screenshots, manual metrics, or authenticated reads into an account snapshot. Preserve source lineage and mark missing fields.
- Discover active platforms. Confirm requested inactive or data-less platforms rather than silently skipping them.
- Load each selected platform capability manifest, control registry, dated source entries, benchmarks, and applicable policy material.
- Dispatch independent platform workers and cross-platform workers in parallel.
- Validate every result against the common finding schema. Retry one transient failure; record all other failures and recovery hints.
- Run deterministic scoring. Do not calculate or repair scores in the prompt.
- Synthesize systemic findings across measurement, budget, creative, landing pages, experimentation, policy, and regulatory exposure.
- Write one atomic run bundle and render the requested reports.
- Verify bundle completeness, citations, privacy, and render integrity.
Platform workers
Use a dedicated worker for every selected platform:
audit-googleaudit-metaaudit-youtubeaudit-linkedinaudit-tiktokaudit-microsoftaudit-appleaudit-amazonaudit-redditaudit-pinterestaudit-snapchataudit-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 worker result
Each platform or cross-platform worker handles one canonical platform per task and
returns exactly one audit-worker-result v1 object. Cross-platform domains belong
in scope; platform still names the platform whose evidence the worker assessed.
{
"schema_version": "1.0.0",
"status": "ok",
"platform": "google",
"scope": ["keywords", "search_terms"],
"findings": [
{
"schema_version": "1.0.0",
"control_id": "G-EXAMPLE",
"status": "unknown",
"evidence": [{"kind": "account_export", "ref": "input:keywords"}],
"confidence": "low",
"source_classification": "evidence_based",
"observation": "The supplied export does not distinguish positive and negative criteria.",
"diagnosis": "Keyword inventory completeness cannot be established.",
"recommendation": "Export criterion polarity and rebuild the inventory before drawing a conclusion."
}
],
"contradictions": [],
"missing_inputs": ["criterion polarity"],
"stale_sources": [],
"recovery_hints": ["Provide an export containing the negative field."]
}Every envelope field is required. Validate against the installed
audit-worker-result v1 schema. Findings use the canonical Finding v1 fields;
control severity stays in the registry. Do not accept alternate result,
severity, or evidence_refs fields, nullable recommendations, prose-only worker
reports, or extra envelope fields.
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:
- Observations directly supported by account data.
- Diagnoses inferred from observations, with confidence.
- Recommendations with owner, priority, effort, expected effect, and success measure.
- 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.jsonaccount-snapshot.jsonaudit.jsonaction-plan.jsonreport.md- Optional
report.htmlandreport.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.
Trending
More talk lately.thin dataabout the whole repository
Who's talking about AgriciDaniel/claude-ads
- sudosubin/agents.nix“claude-code-plugins.github.agricidaniel.claude-ads: init at 2.1.0”GitHub · Oct 28, 2026
- sudosubin/agents.nix“claude-code-marketplaces.github.agricidaniel.claude-ads: init at 2.1.0”GitHub · Oct 28, 2026
- sudosubin/agents.nix“agent-skills.github.agricidaniel.claude-ads: 2.0.2 → 2.1.0”GitHub · Oct 16, 2026
- khairyahmed394-gif/Vd“Import AgriciDaniel/claude-ads into claude-ads/”GitHub · Oct 10, 2026
- sudosubin/agents.nix“agent-skills.github.agricidaniel.claude-ads: v2.0.2 → refs/tags/v2.0.2”GitHub · Oct 3, 2026
The biggest 5 of 14 mentions linking to the repository that our crawl found. Each line is their own headline.
See all 14
Talked about on GitHub and the web (14 times).
On GitHub · 11
- Oct 28, 2026claude-code-plugins.github.agricidaniel.claude-ads: init at 2.1.0 — sudosubin/agents.nix0 reactions
- Oct 28, 2026claude-code-marketplaces.github.agricidaniel.claude-ads: init at 2.1.0 — sudosubin/agents.nix0 reactions
- Oct 16, 2026agent-skills.github.agricidaniel.claude-ads: 2.0.2 → 2.1.0 — sudosubin/agents.nix0 reactions
- Oct 10, 2026Import AgriciDaniel/claude-ads into claude-ads/ — khairyahmed394-gif/Vd0 reactions
- Oct 3, 2026agent-skills.github.agricidaniel.claude-ads: v2.0.2 → refs/tags/v2.0.2 — sudosubin/agents.nix0 reactions
6 more on GitHub
- Sep 25, 2026agent-skills.github.agricidaniel.claude-ads: init at 2.0.2 — sudosubin/agents.nix0 reactions
- May 16, 2026Install claude-ads skill — msmarketing-fi/claude0 reactions
- Apr 14, 2026[REQ] Skill add request — sickn33/agentic-awesome-skills0 reactions · 1 comment
- Apr 13, 2026Audit candidate: AgriciDaniel/claude-ads — xiaolai/nlpm0 reactions · 5 comments
- Mar 9, 2026[SUBMISSION] AgriciDaniel/claude-ads — ProSkillsMD/proskills0 reactions · 5 comments
- Mar 9, 2026[SUBMISSION] claude-ads — ProSkillsMD/proskills0 reactions · 3 comments
On the web · 3
- Mar 24, 2026Best Claude Code Skills in 2026 - The Complete Guide — google.com
- Feb 15, 2026claude-ads - AI Agents on GitHub (10.3k★) | SkillsLLM — google.com
- Feb 11, 2026Ads Report by AgriciDaniel — AI Skill for Any LLM — google.com
Other versions11 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 11
- coopersimson96/ai-content-system★ 17same name
- Littlpinguin/marketing-cockpit-template★ 8same name
- megastep/codex-skills★ 4same name
- omarmfouad25/google-marketing-agent★ 4same name
- Maxymize/maxym-ai-ads★ 2same name
- F0rger123/claude-meta-ads-god-mode★ 1same name
- get-proofpilot/claude-skills-catalog★ 1same name
- Biznomad/claude-skills★ 0same name
- chriswestt/claude-skills★ 0same name
- devaimani/openai-ads-mcp★ 0same name
1 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository35 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- adsSkill
- ads-amazonSkill
- ads-appleSkill
- ads-attributionSkill
- ads-budgetSkill
- ads-competitorSkill
- ads-createSkill
- ads-creativeSkill
- ads-dnaSkill
- ads-generateSkill
- ads-googleSkill
- ads-landingSkill
- ads-launchSkill
- ads-linkedinSkill
- ads-mathSkill
- ads-metaSkill
- ads-microsoftSkill
- ads-monitorSkill
- ads-optimizeSkill
- ads-photoshootSkill
- ads-pinterestSkill
- ads-planSkill
- ads-redditSkill
- ads-reportSkill
- ads-researchSkill
- ads-server-side-trackingSkill
- ads-setupSkill
- ads-snapchatSkill
- ads-testSkill
- ads-tiktokSkill
- ads-validateSkill
- ads-xSkill
- ads-youtubeSkill
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

