How to use the Marketing Attribution Advisor agent skill

What is an agent skill and what does this one do?

An agent skill is a folder of instructions and reference files that an AI agent loads when a task matches its description. This skill is named attribution and is taken from the coreyhaines31/marketingskills collection, published under the MIT licence.

Once installed, the agent approaches questions about which channels drive sales differently. It explains that attribution is an estimate rather than a single truth, compares models such as first-touch, last-touch, linear and data-driven, and recommends showing two models side by side for long sales cycles. It can also help reconcile mismatched numbers between ad platforms and analytics tools.

• Interprets attribution models and explains where each one misleads
• Reconciles conflicting numbers from Google, Meta and analytics tools
• Points to related skills for event tracking, pixels, CRM pipelines or AI search
• Includes a build track for self-hosted first-party attribution when you control the site

How do you install the attribution skill?

Download the ZIP for the skill from the page you are reading. You then have two ways to install it.

For Claude Code, unzip the download into ~/.claude/skills/ to make it available everywhere, or into a project's .claude/skills/ folder to limit it to that project.

For the Claude apps, upload the ZIP under Settings → Capabilities → Skills.

• Personal install: unzip into ~/.claude/skills/
• Project install: unzip into the project's .claude/skills/ folder
• Claude apps: Settings → Capabilities → Skills, then upload the ZIP

What does the skill need before you start?

The skill lists no specific requirements. It does not run scripts and does not use the network, so it works from the files alone. Its guidance is only as good as the data you give it.

The skill can read a file called .agents/product-marketing.md if your project has one. That file describes your business type, sales cycle and primary conversion, which shape most of the recommendations. Creating it is optional.

• No listed requirements
• Runs scripts: no
• Uses network: no
• Optional: .agents/product-marketing.md with business type, sales cycle and primary conversion

How do you trigger the skill with a natural request?

You do not need a special command. Describe the problem in plain language and include the kind of wording the skill responds to, such as attribution model, first-touch vs last-touch, my dashboards disagree, or what's my real CAC.

The skill covers two pillars. Interpretation applies to almost everyone and needs no engineering. Own your attribution is the build track, for when you control the site or app and want to track conversions yourself. Most requests start with interpretation.

• "Google says this campaign drove 120 sales but GA says 80. Which is right?"
• "Should I use first-touch or last-touch for a 4-month B2B sales cycle?"
• "What's my real customer acquisition cost across channels?"
• "How did you hear about us responses seem to disagree with our UTM data?"
• "Help me stitch my bookings from Calendly back to their source."

What kinds of answers should you expect?

Expect the agent to state that attribution is directional and that every model embeds an opinion. It will compare models in a table showing each one's credit rule, best use and how it misleads. For long sales cycles it will recommend first-touch and last-touch side by side, and for thin data it will warn that data-driven results may collapse toward last-click.

The skill also names its boundaries. Event tracking and UTMs belong to the analytics skill, ad pixels to conversion-tracking, CRM pipeline reporting to revops, and AI search measurement to ai-seo. The agent will point you to those rather than rebuilding them.

What are the safety notes and limits?

The skill runs no scripts and makes no network calls, so it does not send your data anywhere. Treat it as a reasoning aid, not a source of truth.

Do not rely on the output for high-stakes financial decisions without checking the underlying conversion data. Models can mislead when volume is thin, and the skill is not a substitute for a conversion ledger.

• Attribution is directional, not an objective true number
• Check the underlying conversion data before large budget moves
• Thin data makes data-driven models unreliable

What tips help you get better results?

Give the agent your business type, sales cycle length and primary conversion. Ask for two models side by side rather than one. Count real conversions once, against a single source of truth, and treat platform-reported credit as a separate question.

If you want the full worked math, the skill includes a reference that scores one customer journey under six rule-based models, plus a plain explanation of Shapley-based data-driven attribution.

• Share business type, sales cycle and primary conversion up front
• Ask for first-touch and last-touch side by side on long cycles
• Keep conversion counts in one ledger; explain platform credit separately