Claude skills for Google Ads are reusable instructions that tell Claude how to perform a specific advertising task, such as auditing a campaign or preparing a search-term review. To work with live account data, a skill also needs a connected data source—often an MCP server—and clear limits on what Claude may change. Here’s how to build a practical audit skill, connect it to Google Ads, and keep consequential edits under human approval.
What a Claude skill does—and what it doesn’t
A Claude skill is a set of task instructions that Claude can use when relevant or when you invoke it. In Claude Code, a skill can live in a folder with a SKILL.md file; its instructions can define the task, required inputs, and expected output. Anthropic’s documentation describes the skill format and how Claude Code discovers and uses skills; it does not make a skill a Google Ads integration or grant account permissions. Anthropic’s Claude Code skills documentation explains the supported structure.
For an advertising workflow, keep three components distinct:
- The skill defines the job: for example, compare recent search-term performance with the account’s conversion goals and flag candidates for review.
- The connection provides data or actions. An MCP server can expose tools to an AI client, but the tools and available operations depend on that server’s implementation and configuration. The MCP tools documentation describes how servers make tools available to clients.
- The account authorization determines which Google Ads customer data or operations the connected service can access. A prompt cannot override permissions or create access that has not been configured.
This separation matters in agency work. You can reuse the same audit method across accounts while changing the connected customer, date range, or approval process. But a well-written skill cannot make missing conversion values reliable, resolve account-access issues, or ensure an available tool supports every Google Ads feature.
Why use a skill for paid-search work?
Without a reusable procedure, an analyst may ask for an audit one day and receive a different date range, metric set, or recommendation format the next. A skill can make those choices explicit. That is useful when a growth team needs comparable account reviews, or when an agency wants a strategist to check the same evidence before proposing a budget or targeting change.
The practical gain is consistent analysis structure, not guaranteed optimization. A skill can ask Claude to report the campaign, period, comparison period, metric definitions, evidence, and caveats together. This makes it easier for a person to reproduce a finding in Google Ads and decide whether it warrants action.
Google Ads reporting is built around selectable resources, segments, metrics, and date ranges, so the query design affects what a report can show. The Google Ads API reporting guide explains those reporting concepts and query behavior; a skill should therefore name the fields and period it needs instead of asking vaguely for “performance.” Google’s reporting documentation is the reference for the API’s reporting model.
For instance, a search-term review is more useful when it distinguishes search-term cost and conversions from keyword-level statistics, states the date window, and identifies which conversion action is being used. A skill can require those details before it summarizes evidence. It should also flag when the available report omits a field or uses a different attribution view, rather than filling the gap with an assumption.
Repeatable inputs and outputs also help with handoffs. An account manager can give a reviewer a structured list of evidence and proposed checks, while the reviewer can challenge a specific premise—such as the conversion action or comparison period—instead of redoing the entire analysis.
Build a Google Ads audit skill
Write the task instructions
For a first version, make the skill read-only in intent: ask Claude to investigate and recommend, not edit. In a Claude Code project, create a folder such as .claude/skills/google-ads-audit/ and add a SKILL.md file. The example below is a starting template. Adapt its metric definitions to the account’s actual goals and available reporting tools.
---
name: google-ads-audit
description: Review Google Ads performance for a specified account and date range. Use when asked for a campaign or search-term audit.
---
When asked to audit Google Ads:
1. Confirm the customer account, reporting period, comparison period, and business goal. Ask if any are missing.
2. Use only connected Google Ads reporting tools. Do not claim data was checked if a query failed or a required field was unavailable.
3. Report the campaign or search term, the exact date range, and the available cost, clicks, conversions, conversion value, and other requested metrics. State currency and conversion-action assumptions when available.
4. Separate observed data from interpretation. For each recommendation, cite the relevant account evidence and explain what could invalidate the conclusion.
5. Return: scope and assumptions; key observations; candidates for investigation; proposed next checks. Do not change budgets, bids, keywords, ads, or targeting.
The frontmatter identifies the skill and describes when it applies; the rest supplies the procedure. Anthropic documents additional skill options, so check the current format before adding features beyond this basic example. The official skill guide is the appropriate place to verify supported fields and behavior.
Connect the skill to account data
Next, configure a Google Ads data connection in the Claude environment you plan to use. With Claude Code, that means adding an MCP server through the supported MCP configuration flow; the server must be authenticated and authorized separately. Anthropic’s Claude Code MCP guide covers connecting servers, while Google’s API introduction explains the requirements and concepts for working with Google Ads API access. Claude Code MCP documentation and Google Ads API getting-started documentation describe their respective sides of the connection.
Use this setup sequence:
- Choose the environment. Confirm whether you’re using Claude Code or another Claude client. Skill support and MCP setup are client-specific; don’t assume a project skill file automatically appears in every Claude product.
- Configure the server and credentials. Use the connection instructions for the MCP provider and client. Give the connection only the account access and operations required for the intended workflow.
- Inspect available tools. Check which reporting operations are actually exposed, what inputs they require, and whether any tools can mutate account settings. Do not write instructions that promise a capability you have not verified.
- Run a narrow test. Ask for one account and one defined reporting period. Compare the returned figures with the Google Ads interface or an independently checked report before relying on the output.
- Invoke the skill and review the result. In Claude Code, use the skill through the client’s documented skill workflow. Revise the instructions if Claude skips a required field, blurs evidence and inference, or produces recommendations outside the requested scope.
A connector is not automatically an MCP server, and a server is not automatically read-only. Treat the tool list and authorization as part of the workflow design. If your team needs a hosted connection between an AI client and Google Ads, NotFair’s Google Ads MCP page describes its offering; confirm current tool coverage and permissions before building instructions around it.
Turn the analysis into a decision workflow
Make recommendations checkable
A useful audit skill should return findings that a marketer can verify, not just confident-sounding advice. Ask Claude to attach each observation to a campaign, time period, and reported metric, then label the next step as a check or a proposed change. If the available tool does not return the necessary breakdown, the skill should say so and suggest where to verify it.
For example, a recommendation to investigate a search term could name the term, the report period, observed cost and conversion data, and the account’s relevant conversion action. It should not jump from a small number of clicks to a universal claim that the term is wasteful. The marketer can then assess intent, lead quality, and the business’s own thresholds before deciding what to do.
The Google Ads API supports mutate operations for changing resources, but the existence of an API operation does not mean every connected MCP tool exposes it or that every change is suitable for automation. Google documents the API’s mutation model separately from reporting. Google’s mutate operations guide helps clarify the distinction between retrieving data and changing account resources.
Use a short, explicit decision boundary:
- Analysis: Claude may retrieve permitted reporting data and summarize it.
- Recommendation: Claude may suggest a change with evidence and an explanation of the expected trade-off.
- Execution: A person approves the specific account, resource, and proposed values before a change is made, unless a separately designed automation policy explicitly allows that operation.
For teams that do permit execution, treat the approval as a review of the exact proposed change—not a general instruction to “optimize.” Keep a record of the original value, the proposed value, the approver, and the result so a change can be investigated or reversed where the platform and operation allow it.
Where the workflow breaks
Instructions cannot repair bad inputs. If conversion tracking is misconfigured, a conversion action has changed, or a reporting period is incomplete, a skill may organize the analysis but cannot make the underlying measurement trustworthy. Require it to surface missing or uncertain data, and validate important findings against the account’s measurement setup.
Tool coverage can be narrower than the account task. An MCP server may expose reporting but not the resource, segment, or mutation needed for a particular investigation. Check the available tools before assigning the skill a task; when a query is unsupported, have Claude return the limitation and a manual verification step rather than inventing a result.
Context changes the right answer. A high cost per conversion may be acceptable for a high-value opportunity, while a cheaper conversion may be low quality. The skill should request the business goal, value assumptions, and decision threshold—or clearly label what it does not know—instead of treating an account-wide average as a universal target.
Test the skill against realistic edge cases before sharing it with a team:
- The requested date range is missing or compares different day counts.
- The account has multiple conversion actions with different business meanings.
- A report returns no data, partial data, or a different currency than expected.
- A user asks Claude to apply a recommendation even though the workflow is designed for analysis only.
These are not reasons to abandon skills. They are reasons to make the failure behavior part of the instructions and to keep account permissions aligned with the actual risk.
Start with one repeatable job
For an in-house team or agency, begin with a single recurring task—such as a weekly search-term review—and keep version one focused on reporting and recommendations. A skill is ready for broader use when another marketer can run it, identify its reporting scope, reproduce its key figures, and tell which statements are evidence versus interpretation.
Use illustrative policy limits, not supposed industry benchmarks. For example, an agency might initially ask the skill to flag terms above $100 in spend with zero recorded conversions for manual review. That number is only a sample rule: set your own threshold from account economics, conversion delay, and lead quality, and label it as a review trigger rather than an automatic exclusion rule.
Once the reporting task is reliable, consider a separate, narrowly scoped execution workflow with its own permissions and approval steps. Do not quietly expand an audit skill from “recommend” to “change” by adding one broad instruction; that changes the operational risk as well as the task.
If your team also needs a similar connected workflow for Meta campaigns, review NotFair’s Meta Ads MCP offering independently and verify its current capabilities against your use case. For teams evaluating hosted MCP connections and approval-gated advertising workflows, NotFair is one option to assess alongside the client, permissions, and review process you already use.
Authored with NotFair SEO