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 naveedharri/benai-skills --skill ads-googlegit clone --depth 1 https://github.com/naveedharri/benai-skillsWrote 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/naveedharri/benai-skills/ads-google)<a href="https://agentmods.dev/skills/naveedharri/benai-skills/ads-google"><img src="https://agentmods.dev/badge/skills/naveedharri/benai-skills/ads-google/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/naveedharri/benai-skills/ads-google"><img src="https://agentmods.dev/badge/skills/naveedharri/benai-skills/ads-google.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00074 | $0.01237 |
| Opus 5 | $0.00037 | $0.00619 |
| Sonnet 5 | $0.00015 | $0.00247 |
| Haiku 4.5 | $0.00007 | $0.00124 |
Grade A, and why
ads-google 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 11d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- ads-google — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Google Ads Deep Analysis
Process
- Collect Google Ads account data (export, Change History, Search Terms Report)
- Read
ads/references/google-audit.mdfor full 74-check audit - Read
ads/references/benchmarks.mdfor Google-specific benchmarks - Read
ads/references/scoring-system.mdfor weighted scoring - Evaluate all applicable checks as PASS, WARNING, or FAIL
- Calculate Google Ads Health Score (0-100)
- Generate findings report with action plan
What to Analyze
Conversion Tracking (25% weight)
- Google tag (gtag.js) installed and firing on all pages
- Enhanced Conversions active (hashed first-party data)
- Consent Mode v2 implemented (required for EU/EEA)
- Conversion actions mapped correctly (primary vs secondary)
- Offline conversion import configured (for lead gen)
- Server-side tagging via GTM (recommended for accuracy)
- Attribution model: data-driven preferred (last-click as fallback only)
- Conversion lag analysis (are conversions still trickling in?)
Wasted Spend (20% weight)
- Search Terms Report reviewed (last 30 days minimum)
- Negative keyword coverage adequate (shared lists + campaign-level)
- Display placement audit (exclude low-quality sites)
- Invalid click rate within norms (<10%)
- Broad Match only used with Smart Bidding (NEVER without it)
- Brand/non-brand campaigns separated
- Geographic targeting precise (no wasted international spend)
Account Structure (15% weight)
- Campaign-level organization follows business logic
- Ad groups themed tightly (15-20 keywords max per group)
- RSA ad groups have ≥3 active ads
- PMax campaigns structured correctly (asset groups, signals)
- SKAGs evaluated (migrate to themed groups if present)
- Campaign labels/naming conventions consistent
Keywords (15% weight)
- Match type strategy appropriate (Exact → Phrase → Broad progression)
- Quality Score distribution (aim ≥7 average)
- Low QS keywords flagged (<5 = FAIL, 5-6 = WARNING)
- Keyword cannibalization check (same keywords in multiple campaigns)
- Impression share tracked for top keywords
- Keyword bid adjustments set for devices/locations/audiences
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.
- 11d ago First seen · 128 lines · 74 tokens per session scan A 0b4f7e44422c
ads-google is a skill published in the GitHub repository naveedharri/benai-skills (61 stars, last pushed 7d ago), licensed MIT. It adds 74 tokens to every session and 1,237 once invoked, about $0.0004 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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