Claude Ads is a Claude Code skill for managing paid-media operations across 12 advertising platforms, using account data or exports to produce audits, plans, creative workflows, experiments, monitoring, and reports. Agencies, consultants, and in-house performance teams use it for source-based analysis and controlled account work. The catalogue entries are its platform-specific skills, workers, and supporting instructions.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add AgriciDaniel/claude-ads --skill ads-attributiongit clone --depth 1 https://github.com/AgriciDaniel/claude-adsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/agricidaniel/claude-ads/ads-attribution)<a href="https://agentmods.dev/skills/agricidaniel/claude-ads/ads-attribution"><img src="https://agentmods.dev/badge/skills/agricidaniel/claude-ads/ads-attribution/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/agricidaniel/claude-ads/ads-attribution"><img src="https://agentmods.dev/badge/skills/agricidaniel/claude-ads/ads-attribution.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00091 | $0.00385 |
| Opus 5 | $0.00046 | $0.00192 |
| Sonnet 5 | $0.00018 | $0.00077 |
| Haiku 4.5 | $0.00009 | $0.00038 |
Grade A, and why
ads-attribution scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Attribution Audit
- Read the main
adscontract and normalized account snapshots. - Declare the business conversion, value, data window, timezone, currency, and decision the attribution analysis must support.
- Inventory every browser, server, platform, analytics, MMP, offline, and app attribution source with its identity, counting, deduplication, and privacy rules.
- Reconcile comparable events and explain differences caused by eligibility, view-through rules, consent, modeled data, conversion lag, thresholds, or scope.
- Separate measurement quality from platform-reported performance.
- Return findings, contradictions, confidence, missing evidence, and a measurement improvement plan through the common JSON contract.
Do not assume one platform is ground truth, add incompatible reports together, or recommend an attribution model without the operator's decision context.
Comparability gate
Reject aggregation until the sources share, or are explicitly normalized to, the same conversion event and value definition, attribution window, click/view scope, counting method, deduplication identity, timezone, currency, attribution model, and modeled-data treatment. Until then, report the values side by side with their definitions; do not compute a total.
Example: Meta seven-day conversions and Google thirty-day conversions are incompatible. Refuse to add them, reconcile windows and definitions first, and only aggregate a newly comparable dataset.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 33 lines · 91 tokens per session scan A e121015d4c2e
ads-attribution is a skill published in the GitHub repository AgriciDaniel/claude-ads (9,090 stars, last pushed 1mo ago), licensed MIT. It adds 91 tokens to every session and 385 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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