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/optimizernpx skills add chanktb/claude-google-ads --skill optimizergit 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/optimizer)<a href="https://agentmods.dev/skills/chanktb/claude-google-ads/optimizer"><img src="https://agentmods.dev/badge/skills/chanktb/claude-google-ads/optimizer.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 | $0.00139 | $0.03155 |
| Opus 5 | $0.00069 | $0.01577 |
| Sonnet 5 | $0.00028 | $0.00631 |
| Haiku 4.5 | $0.00014 | $0.00315 |
Grade A, and why
google-ads-optimizer 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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Google Ads — Optimizer (act)
Find what's dragging the account and fix it — with data backing and root-cause reasoning, not symptom
swatting. The optimizer proposes changes; it applies them only through pusher (approval gate). For a
scored health check use audit; this skill is about performance and money.
Operating rules
- Every recommendation has data backing (specific numbers, not vague advice).
- Read everything from
account-context.yaml(margin_tiers, brand_terms, guardrails, AOV). If the context is missing, runsetupfirst — never optimize a live account without it. - Read the context
connectionsblock first. If store/GA4 ismissing, do the in-platform analysis and label every true-ROAS / store-revenue conclusion UNVERIFIED — connect store/GA4; never fabricate a store-revenue figure to compute "true ROAS". Prefer guiding the user to connect over guessing. - 3-source attribution (see
${CLAUDE_PLUGIN_ROOT}/references/optimization-playbook.md): store revenue = ground truth; Google Ads in-platform = for Smart Bidding; GA4 = channel mix. A 20-35% Ads-vs-GA4 gap is normal. - Honor guardrails: change-event cooldown, ignore paused, margin-tier ROAS. Don't flag a house-brand line
for low ROAS above its tier
min_roas.
Model dispatch (run cheap, decide expensive) — see ${CLAUDE_PLUGIN_ROOT}/references/model-tier-dispatch.md
- Scout (
haiku) — runningtiering.pyandsearch_term_miner.py(scripts return their own output). - Routine (
sonnet) — STEP 1 performance pull + store/GA4 fetch; STEP 3 dual-source search-term pull (per-PMaxcampaign_search_term_insightloop). Dispatch asgeneral-purposesub-agents; return raw, don't conclude. - Judge (main session) — tier verdicts, what to block vs keep (esp. never-block-brand), STEP 5 profitability call, STEP 6 root-cause, STEP 7 dated action plan. The numbers come cheap; the decisions stay here.
STEP 1 — Collect performance
Scope to the active set first (ENABLED + impressions in the window) — never tier or "optimize" a
campaign that hasn't served in the period; entity status=ENABLED can include long-dead campaigns' assets
(see ${CLAUDE_PLUGIN_ROOT}/skills/audit/references/gaql-notes.md). Then pull campaign performance, search terms (≤30d or explicit dates),
and asset-group performance via the MCP; pull store revenue + GA4 channel mix via the data-source/GA4
fallback chain. Note what's unavailable.
What ships with it
2 files 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 · 161 lines · 139 tokens per session scan A 2bce1b8fcf29
google-ads-optimizer is a skill published in the GitHub repository chanktb/claude-google-ads (11 stars, last pushed 1mo ago), licensed MIT. It adds 139 tokens to every session and 3,155 once invoked, about $0.0007 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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