Google Marketing Ops

google-marketing-ops

> Built for Claude Code. Works on any project — clone, fill in local/project-context.md, done.

Claude Code skill for orchestrating Google Ads + Google Analytics 4 + Google Tag Manager from a single conversation. Routes user intent to the right tool, combines multiple tools when a task spans them, and provides analysis playbooks beyond raw data dumps.

> Configuration model: All project-specific values (GA property IDs, domains, file paths, weekly-report conventions) live in local/project-context.md (gitignored). The skill itself is generic and reusable across projects — clone, fill in local/project-context.md, done.

What it does

| Tool | Read | Write | Underlying integration | |---|---|---|---| | Google Ads | ✅ | ✅ | google-ads-api skill (Python SDK + ~/.google-ads.yaml) | | Google Analytics 4 | ✅ | ❌ | analytics-mcp (official Google MCP, ADC-based) | | Google Tag Manager | ✅ | ✅ | mcp__gtm__* (HTTP MCP via OAuth) |

The skill itself doesn't reimplement any of these — it routes, combines, and adds an analysis layer on top.

Why a skill (and not just MCP)

Each underlying integration exposes atomic operations. A skill is the right shape when:

1. A task crosses multiple tools (e.g., Ads change → GA verification → GTM debug)
2. Raw data needs interpretation (channel breakdown is data; "your Paid Search bounce rate jumped 30%, here's likely why" is analysis)
3. There's a fixed SOP (the weekly Ads-review closed loop)

Calling MCP directly works for one-off queries. Use this skill when you need orchestration.

Architecture

SKILL.md                         # Entry routing (~150 lines)
playbooks/
├── setup-check.md               # Credential & MCP health check (run this first on a new machine)
├── ads-config.md                # Ads write operations (bids, keywords, negative keywords, budgets, campaigns)
├── ads-analysis.md              # Ads read operations (GAQL queries, search terms, quality score)
├── ga-analysis.md               # GA4 reporting (channels, landing pages, funnels, attribution)
├── gtm-debug.md                 # GTM tag/trigger/variable management + version publishing
├── cross-tool-flows.md          # Multi-tool playbooks (Ads-after-change validation, conversion reconciliation)
└── weekly-review.md             # Weekly Ads-change closed loop (report + maintenance plan + decision log)

SKILL.md is the only file always loaded. Playbooks are read on demand based on the user's task.

Installation

1. Install dependencies

# Python tools
pipx install analytics-mcp     # GA4 MCP
pipx install google-ads        # Google Ads SDK (or pip in your project's venv)

# gcloud (for ADC authentication)
brew install --cask google-cloud-sdk     # macOS

2. Authenticate

Google Ads — put OAuth refresh-token credentials at ~/.google-ads.yaml:

developer_token: "..."
client_id: "..."
client_secret: "..."
refresh_token: "..."
login_customer_id: "..."

GA4 — Application Default Credentials with the right scopes:

gcloud auth application-default login \
  --scopes=https://www.googleapis.com/auth/analytics.readonly,https://www.googleapis.com/auth/cloud-platform

GTM — OAuth via Claude Code MCP:

# Configure GTM MCP (URL is provider-specific, see your MCP provider's docs)
claude mcp add --scope user --transport http gtm 
# Then in Claude Code, run /mcp and complete OAuth for "gtm"

3. Register GA MCP with Claude Code

claude mcp add --scope user analytics-mcp -- $(which analytics-mcp)
claude mcp list   # confirm "analytics-mcp ... ✓ Connected"

4. Install this skill

Clone into your skills directory (the standard location is ~/.claude/skills/):

cd ~/.claude/skills      # or your symlinked AI_Skills repo
git clone https://github.com//google-marketing-ops.git

Restart Claude Code so the new skill metadata is picked up.

5. Verify

In Claude Code, ask:

> "跑 setup-check"

The agent should walk through all credential/MCP checks and report any gaps.

Limitations

• GA writes (creating audiences, custom dimensions, event create rules) are not supported — the official analytics-mcp is read-only. Workarounds: edit configuration via GTM where possible, or implement a custom GA Admin API integration.
• Ads campaign creation is recommended via the web UI rather than API — the API requires too many fields to get right and the failure modes are expensive.
• Cross-tool conversion numbers will not match exactly. Ads vs GA vs GTM differ by attribution model, time zone, sampling, and de-duplication. The cross-tool-flows.md playbook explains the standard reconciliation; do not try to force them equal.

Configuration

All project-specific values are in local/project-context.md (gitignored). Copy local/project-context.md.example and fill in:

• GA Property IDs (main + secondary)
• Domains (main + secondary)
• Google Ads tools repo path (if you have one with cached CSV data)
• GTM Container ID
• Weekly-report file path conventions (only if you adopt the W-week SOP from playbooks/weekly-review.md)
• Anything else the AI should know about your specific setup

The skill itself contains no hard-coded project values. To use across multiple projects, swap out local/project-context.md accordingly.

License

TBD before open-source. Recommend MIT or Apache-2.0.

Contributing

Issues and PRs welcome once open-sourced. The skill is intentionally lean — please don't add bundled scripts unless you've observed the same pattern repeated across 3+ playbooks (YAGNI principle).

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: chjm-ai
• Source: chjm-ai/google-marketing-ops
• License: MIT

Install and usage instructions live in the source repository linked above.