google-ads-keyword-strategy-planner

google-ads-keyword-strategy-planner is a skill for Claude Code from Ad-Superpowers/ad-superpowers-plugin. It costs 100 tokens per session (5,633 once invoked), scanned A, original, MIT.

A guide for planning Google Ads keyword strategies. It explains keyword match types, search-term analysis, keyword grouping, and negative keywords, which prevent ads from showing for unwanted searches.

In plain words
What is it for?
Use it to review search terms, choose match types, group keywords, and build negative keyword lists.
Why use it?
It helps turn real search data into a focused keyword list and reduce wasted clicks from irrelevant searches. It also clarifies when to use broad, phrase, or exact matching.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the ad-superpowers plugin — 120 skills, 35 commands, 5 agents, 1 MCP server shipped together

Good fit Use it to review search terms, choose match types, group keywords, and build negative keyword lists.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ad-superpowers/ad-superpowers-plugin/google-ads-keyword-strategy-planner
Install

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.

Any agent
npx skills add Ad-Superpowers/ad-superpowers-plugin --skill google-ads-keyword-strategy-planner
Clone the repo
git clone --depth 1 https://github.com/Ad-Superpowers/ad-superpowers-plugin

Made for: Claude Code.

Or install ad-superpowers, the plugin that ships this one along with the rest of its 120 skills, 35 commands, 5 agents, 1 MCP server.

Wrote 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.

agentmods badge for google-ads-keyword-strategy-planner

README.md
[![agentmods](https://agentmods.dev/badge/skills/ad-superpowers/ad-superpowers-plugin/google-ads-keyword-strategy-planner/github.svg)](https://agentmods.dev/skills/ad-superpowers/ad-superpowers-plugin/google-ads-keyword-strategy-planner)
Your own site
<a href="https://agentmods.dev/skills/ad-superpowers/ad-superpowers-plugin/google-ads-keyword-strategy-planner"><img src="https://agentmods.dev/badge/skills/ad-superpowers/ad-superpowers-plugin/google-ads-keyword-strategy-planner/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.

agentmods 80×15 button for google-ads-keyword-strategy-planner

Your own site · 80×15
<a href="https://agentmods.dev/skills/ad-superpowers/ad-superpowers-plugin/google-ads-keyword-strategy-planner"><img src="https://agentmods.dev/badge/skills/ad-superpowers/ad-superpowers-plugin/google-ads-keyword-strategy-planner.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 100 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,633 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00100 $0.05633
Opus 5.5 $0.00040 $0.02253
Sonnet 5.5 $0.00020 $0.01127
Haiku 4.5 $0.00010 $0.00563

Measured 1mo ago against content hash a03355f42247, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

google-ads-keyword-strategy-planner 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 1mo 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.

plugin/skills/google-ads-keyword-strategy-planner/SKILL.md · 783 lines

How it starts

The opening of the file, as written. The whole thing — 783 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Keyword Strategy Planner

Complete guide for setting up an effective Google Ads keyword strategy with match types, negatives, and search terms analysis.

Pull Search Terms Data via MCP First

Always start with real data before planning strategy:

# Get search term performance for the account
google_ads_run_gaql(query="
  SELECT search_term_view.search_term,
         search_term_view.status,
         metrics.clicks, metrics.impressions, metrics.ctr,
         metrics.conversions, metrics.cost_micros,
         metrics.cost_per_conversion,
         campaign.name, ad_group.name
  FROM search_term_view
  WHERE segments.date DURING LAST_30_DAYS
    AND metrics.clicks > 5
  ORDER BY metrics.conversions DESC
  LIMIT 100
")

# Get keyword performance with Quality Score
google_ads_run_gaql(query="
  SELECT ad_group_criterion.keyword.text,
         ad_group_criterion.keyword.match_type,
         ad_group_criterion.quality_info.quality_score,
         metrics.clicks, metrics.conversions,
         metrics.cost_per_conversion, metrics.search_impression_share
  FROM keyword_view
  WHERE segments.date DURING LAST_30_DAYS
    AND campaign.status = 'ENABLED'
    AND ad_group_criterion.status = 'ENABLED'
  ORDER BY metrics.cost_per_conversion DESC
  LIMIT 50
")

Quick Decision Guide

WHICH MATCH TYPE STRATEGY FITS YOUR CASE?
│
├──► NEW ACCOUNT / LIMITED DATA
│   └──► BROAD MATCH + SMART BIDDING
│       ├── Maximum reach
│       ├── Let AI discover queries
│       └── Strict negative management
│
├──► PROVEN KEYWORDS / HIGH VOLUME
│   └──► PHRASE MATCH (Core)
│       ├── Balance between reach and control
│       ├── Intent preserved
│       └── Predictable performance
│
├──► BRAND / HIGH-VALUE KEYWORDS
│   └──► EXACT MATCH
│       ├── Maximum control
│       ├── Premium bidding
│       └── Quality traffic
│
├──► AI MAX CAMPAIGN (2026)
│   └──► KEYWORDS ARE OPTIONAL SIGNALS
│       ├── AI Max matches based on landing page + assets
│       ├── Keywords guide intent, not control reach
│       ├── Focus on strong negatives + audience signals
│       └── Smart Bidding Exploration enabled by default
│
└──► MODERN BEST PRACTICE (2026+)
    └──► HYBRID APPROACH
        ├── Broad Match + Smart Bidding (discovery)
        ├── Phrase Match (core volume)
        ├── Exact Match (top performers)
        └── Aggressive negatives

Read the full file on GitHub · 783 lines

Changes

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.

  1. 1mo ago First seen · 783 lines · 100 tokens per session scan A a03355f42247

Subscribe to this mod's changes

google-ads-keyword-strategy-planner is a skill published in the GitHub repository Ad-Superpowers/ad-superpowers-plugin (5 stars, last pushed 29d ago), licensed MIT. It adds 100 tokens to every session and 5,633 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-09-11.

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