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 agentmods add skills/agricidaniel/claude-ads/ads-auditnpx skills add AgriciDaniel/claude-ads --skill ads-auditgit clone --depth 1 https://github.com/AgriciDaniel/claude-adsWhat 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 | $0.00091 | $0.01252 |
| Opus 5 | $0.00046 | $0.00626 |
| Sonnet 5 | $0.00018 | $0.00250 |
| Haiku 4.5 | $0.00009 | $0.00125 |
Grade A, and why
ads-audit 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 yesterday.
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.
How it starts
The opening of the file, as written. The whole thing — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 finding fields
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.
- yesterday First seen · 145 lines · 91 tokens per session scan A d599927da30b
ads-audit is a skill published in the GitHub repository AgriciDaniel/claude-ads (8,600 stars, last pushed 1mo ago), licensed MIT. It adds 91 tokens to every session and 1,252 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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