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/chanktb/claude-google-ads/experimentsnpx skills add chanktb/claude-google-ads --skill experimentsgit clone --depth 1 https://github.com/chanktb/claude-google-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/chanktb/claude-google-ads/experiments)<a href="https://agentmods.dev/skills/chanktb/claude-google-ads/experiments"><img src="https://agentmods.dev/badge/skills/chanktb/claude-google-ads/experiments.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00106 | $0.00837 |
| Opus 5 | $0.00053 | $0.00418 |
| Sonnet 5 | $0.00021 | $0.00167 |
| Haiku 4.5 | $0.00011 | $0.00084 |
Grade A, and why
google-ads-experiments 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 5d 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.
How it starts
The opening of the file, as written. The whole thing — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Google Ads — Experiments
Turn a "let's test X" into a clean experiment: one variable, enough volume to learn something, a defined readout. This is how the split menus in the builders get validated — not by guessing.
Model dispatch (run cheap, decide expensive) — see ${CLAUDE_PLUGIN_ROOT}/references/model-tier-dispatch.md
- Scout (
haiku) — STEP 2significance.pyrun. - Routine (
sonnet) — pulling conversions-per-arm estimates / recent volume for the feasibility check. Dispatch asgeneral-purpose; return raw numbers. - Judge (main session) — STEP 1 variable choice, STEP 3 design + decision rule, STEP 4 platform mapping, STEP 5 readout call (against the pre-stated rule, not early noise). The power math is mechanical; designing a test that can conclude is judgment.
STEP 1 — Pick ONE variable
From the catalog (campaign-level: bidding strategy, tROAS level, brand-exclusion on/off, new-customer mode,
split structure A vs B from ${CLAUDE_PLUGIN_ROOT}/references/pmax-split-strategies.md; ad-group/asset-group-level: copy theme,
audience signal, landing page). Change exactly one — never bundle variables (you won't know what moved).
STEP 2 — Feasibility check (don't run a test that can't conclude)
Using ${CLAUDE_PLUGIN_ROOT}/references/forecasting-and-benchmarks.md, estimate conversions per arm over the planned runtime.
If each arm won't accumulate enough conversions to detect a meaningful difference, say so and either:
extend the runtime, increase budget, pick a higher-volume variable, or skip the test. A test too small
to reach significance is worse than no test — it invites false conclusions.
Runnable check: python ${CLAUDE_PLUGIN_ROOT}/skills/experiments/scripts/significance.py --conv-per-arm N [--mde 0.15] →
smallest detectable lift + whether the run is powered for your target effect.
STEP 3 — Design
- Control vs variant, 50/50 split, one variable.
- Primary success metric (e.g. ROAS, CPA, conversions) + guardrail metrics (don't win on CPA while tanking volume).
- Minimum runtime (cover learning + at least 2-4 weeks; avoid mid-experiment changes).
- Pre-state the decision rule: what result ships the variant, what reverts it.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago First seen · 56 lines · 106 tokens per session scan A 18f3f13775b4
google-ads-experiments is a skill published in the GitHub repository chanktb/claude-google-ads (11 stars, last pushed 1mo ago), licensed MIT. It adds 106 tokens to every session and 837 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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