DEV Community: Vicky Lalwani

Building a custom GPT or Claude agent for marketing reporting

Vicky Lalwani — Fri, 30 Oct 2026 21:34:55 +0000

TL;DR

A marketing reporting agent should read approved data sources, answer fixed weekly questions, and never invent numbers. On ChatGPT you build that with Custom GPT instructions, optional knowledge files and Actions that call your API. (OpenAI GPT Actions) On Claude you connect tools through MCP connectors and keep the workflow in a Project so the same rules stick. (Claude connectors overview) Start with read only tools, a written metric dictionary and a human check before anything goes to a client or boss.

What this agent is for

Most marketing teams waste Monday morning copying Ads, Analytics and CRM numbers into slides. An agent can draft that pack if you constrain it hard.

It should:

pull the same metrics every week
explain changes in plain words
flag missing data instead of guessing
refuse writes to live ad accounts unless you later add a separate, approved tool

It should not:

invent ROAS
change bids
email clients on its own
browse random blogs for "benchmarks" and present them as your data

Custom GPT vs Claude agent

Piece	Custom GPT (ChatGPT)	Claude agent style setup
Instructions	GPT builder instructions	Project instructions + skills
Files	Uploaded knowledge	Project files / Library
Live data	GPT Actions via OpenAPI	MCP connectors / tools
Auth	API key or OAuth on Actions	Connector auth on your account
Risk control	Consequential actions need user confirm	Tool allowlists + approvals in your app

OpenAI Actions turn natural language into API calls described by an OpenAPI schema. You set auth, paste the schema and test in Preview. (Configuring actions in GPTs) Claude reaches external systems through MCP connectors that expose tools, prompts and resources. (Connectors overview)

Pick the host your team already lives in. The design rules below stay the same on both.

The 10 step build

1. Write the weekly question list

Lock ten questions before you touch tools. Example set:

Spend by channel vs last week
Primary conversions vs last week
Cost per primary conversion
Top three campaigns by spend
Top three campaigns by conversions
Tracking anomalies (zero conversion days, spikes)
Search terms or creative notes that need a human
Pipeline or lead quality note from CRM if connected
Open risks
Recommended next tests (labelled as suggestions)

If a question needs a metric you cannot define, drop the question.

2. Publish a metric dictionary

One sheet, shared with the team:

Metric name	Source	Exact definition	Primary or note only
Paid search spend	Google Ads API	Cost in account currency, account timezone	primary
Primary conversions	Google Ads	Only conversions marked primary for bidding	primary
Qualified leads	CRM	Stage = SQL, created this week	primary
Sessions	GA4	Total users sessions, site filter X	note

The agent must quote these names only. Synonyms cause fake trends.

3. Choose read only tools first

Minimum viable tools:

get_ads_summary(date_range, account_id)
get_analytics_summary(date_range, property_id)
get_crm_summary(date_range) optional
get_metric_dictionary()

No update_campaign. No send_email. Add those months later, if ever, behind approvals.

OpenAI production notes for Actions expect HTTPS, sensible rate limits and clear schema descriptions because the model uses those descriptions to pick endpoints. (Production notes)

4. Put definitions in instructions, not vibes

Instruction block pattern:

Role: marketing reporting assistant for [brand]
Data rules: only use tool results; if a tool fails, say so
Date rules: use account timezone; default to last complete Mon to Sun
Output rules: start with a scoreboard table; then drivers; then risks; then suggested tests
Forbidden: inventing numbers; comparing to "industry averages" unless a tool returns them

OpenAI's Actions getting started guide stresses instructions that name the action and parameters the model should use. (Getting started with GPT Actions)

5. Add a thin API in front of vendor APIs

Do not give the model raw Google Ads or Meta tokens with write scopes. Your API should:

authenticate the caller
accept only allowlisted account ids
fetch and aggregate
return compact JSON under size limits
log every call

OpenAI documents payload and timeout limits for Actions. Keep responses small and structured. (Production notes)

6. Mark consequential paths

If you later allow a tool that changes anything, mark it so the user must confirm. OpenAI supports consequential action flags so ChatGPT asks before running them. (Production notes) Until then, keep the agent read only.

7. Test with broken data on purpose

Test	Expected behaviour
API returns empty week	Agent says data missing; no fake table
Conversion tag broke mid week	Agent flags zero days; asks for human check
User asks to raise budgets	Agent refuses; no tool exists
User asks for competitor spend	Agent says unknown unless a licensed tool exists
Two accounts selected	Agent separates tables; no blended fake total unless asked

8. Fix the output template

Force the same markdown every run:

## Scoreboard
(table)

## What moved
(2 to 5 bullets tied to rows)

## Tracking and data risks
(bullets or "none found")

## Suggested tests
(bullets labelled suggestion)

Humans skim. Machines extract tables. Both win.

9. Decide who presses send

The agent drafts. A person posts to Slack or email after a two minute read. That single habit prevents most embarrassment.

10. Review monthly

Each month ask:

Which questions wasted time?
Which metrics were redefined in the business but not in the dictionary?
Which tools timed out?
Did anyone ask for writes you should still refuse?

Sample instruction snippet you can adapt

You are a marketing reporting assistant for [Brand].
Only use tool results for numbers.
If a tool errors or returns empty data, say what failed.
Default range: last complete Mon-Sun in the account timezone.
Never invent benchmarks.
Output the scoreboard table first.
Label suggestions as suggestions.

Paste tool names exactly as they appear in your OpenAPI operationId fields so the model can bind steps to Actions. (Getting started)

Security notes teams skip

Separate read credentials from write credentials at the API layer
Rotate keys when staff leave
Store raw exports outside the model context when they are huge
Treat connector access on Claude like production access; review who can add connectors. (Connectors overview)
Disable the agent publicly; keep it private to your workspace

When not to build this yet

Wait if conversion tracking is broken, if nobody agrees what a lead means, or if the only goal is "an AI demo for the board". Fix definitions first. The agent amplifies whatever you feed it.

Soft next step

If you want this packaged as part of a wider marketing automation build, I outline that work on vickylalwani.net/services/ai-automation. Use the ten steps above so any builder inherits the same read only rules.

Sources

OpenAI, GPT Actions introduction: https://developers.openai.com/api/docs/actions/introduction
OpenAI Help, Configuring actions in GPTs: https://help.openai.com/en/articles/9442513-configuring-actions-in-gpts
OpenAI, Getting started with GPT Actions: https://developers.openai.com/api/docs/actions/getting-started
OpenAI, Production notes on GPT Actions: https://developers.openai.com/api/docs/actions/production
Anthropic, Connectors, skills, and plugins: https://claude.com/docs/extend/overview

A free AI visibility audit checklist you can run this week

Vicky Lalwani — Fri, 30 Oct 2026 21:13:45 +0000

TL;DR

An AI visibility audit checks whether ChatGPT, Perplexity, Google AI Overviews, Claude and Gemini name or cite your brand for the questions buyers already ask. Run a fixed prompt panel, record who gets named, fix crawl and content gaps, then earn mentions on the third party pages those engines already trust. Google says there is no special AI markup required. Useful, crawlable pages still come first. (Google Search Central)

Why this audit exists

Buyers now ask answer engines before they click ten blue links. If your brand never appears in those answers, your Google ranking alone will not save the deal.

This checklist is the audit I run before any GEO or AEO work. It is free to copy. You need a spreadsheet, a browser and about half a day for a focused niche.

What "visible" means here

I score three outcomes for each prompt:

Outcome	What you record	Why it matters
Mention	The answer names your brand	Buyers see you as an option
Citation	The answer links or attributes a source URL of yours	You get a path back to a page you control
Position	Rough place in a shortlist (1st, mid, absent)	First named brands get more attention

A mention without a citation still helps. A citation without a clear brand name is weaker. Both beat silence.

The 10 step checklist

1. Write the buyer prompts, not the brand prompts

List 40 to 80 questions a real buyer would type. Include country variants if you sell in more than one market.

Good examples:

best GEO consultant for Australian SaaS
freelance Google Ads expert UK ecommerce
Meta Ads consultant for US lead gen clinics

Bad examples:

is [your brand] good
review of [your brand]

Brand prompts measure reputation. Buyer prompts measure discovery. Start with discovery.

2. Fix the panel before you run it

For each prompt, note:

market (AU, UK, US or global)
intent (hire, learn, compare)
your hoped for page URL
the competitor set you expect to see

Keep the panel stable for 30 days so next month's run is comparable.

3. Query the same engines the same way

I use five surfaces when the topic is commercial:

ChatGPT (web browsing on, logged out if you can)
Perplexity
Google AI Overviews or AI Mode where shown
Claude
Gemini

Run them in the same week. Note the date. Engines change. Dated rows age honestly.

4. Capture the raw answer, not a summary

Paste the full shortlist into your sheet. Save a screenshot for disputed rows. Record:

brands named
URLs cited
whether your site appeared
whether a directory, Reddit thread or listicle appeared instead

Third party pages often carry more weight than your own site. That matches what many practitioners report when they study citations, and it matches the GEO research finding that content shape changes how often engines pick a source. (Aggarwal et al., GEO paper on arXiv)

5. Score crawl access first

Before you rewrite copy, check the boring gates Google lists for AI features:

robots.txt allows crawling
important text is in HTML, not only images
pages are indexable and eligible for snippets
internal links reach the pages you care about
structured data matches visible text

Google's AI features guidance is clear: there are no extra AI files or special schemas required beyond normal Search eligibility. (AI features and your website)

If ChatGPT never cites you, but Google also cannot index the page, fix indexing before you talk about "GEO tactics".

6. Check answer shape on your key pages

Open your top service and guide pages. Ask:

Does the first screen answer the question in plain words?
Is there a short definition a model can quote?
Is there a comparison table?
Are claims dated and sourced?
Would a stranger understand the page without your pitch deck?

Google's generative AI guide pushes non commodity content with a clear point of view, not recycled tip lists. (AI optimization guide)

7. Map the sources the engines already cite

From your sheet, count which domains appear most often for your prompts. Typical winners:

industry listicles
review directories
Reddit and Quora threads
research posts with methods and numbers
official docs

Your outreach list is that domain set, not a random guest post blast.

8. Patch entity facts

Pick one canonical bio block and reuse it:

who you are
where you work from
which markets you serve
what you actually do
what proof you can show without inventing clients

Then align site schema, LinkedIn, Crunchbase and directory profiles to the same facts. Conflicting bios make models guess.

9. Ship one citable asset from the gaps

If the panel shows you lose on "how to choose" queries, write that guide with a table and sources. If you lose on technical prompts, publish a small study with method and sample size. The GEO paper showed that some content revisions raised visibility in generative answers by large margins in their tests, with results varying by domain. Treat that as motivation to test, not as a guarantee for your niche. (arXiv:2311.09735)

10. Re run the same panel in 30 days

Change only what you changed on purpose. Report:

mention rate
citation rate
share of prompts where you appear in the top three named brands
which third party URLs still dominate

Share of voice by prompt beats vanity screenshots.

Sample scoring table

Copy this into your sheet:

Prompt	Market	Engine	Mentions you?	Cites you?	Top cited domains	Fix next
best GEO consultant Australia	AU	ChatGPT	no	no	listicle A, agency B	earn mention on listicle A
freelance PPC consultant UK	UK	Perplexity	no	no	directory C	finish directory profile
Meta Ads audit checklist	US	Google AIO	yes	yes	your guide	keep fresh, add FAQ

What not to do

Do not spam Reddit with "hire me" threads. Do not buy fake reviews. Do not publish a hundred near duplicate "best of" pages for every city. Google calls scaled, manipulative variants out in its AI optimization guidance. (AI optimization guide)

Do not treat llms.txt as a ranking switch. Google says it does not require special AI files for AI Overviews or AI Mode. (AI features docs)

A one week schedule

Day 1: Build the prompt panel and competitor list.

Day 2: Run ChatGPT and Perplexity.

Day 3: Run Google AI surfaces, Claude and Gemini.

Day 4: Crawl and content checks on your top 10 URLs.

Day 5: Build the third party source map.

Day 6: Fix the highest impact page and one entity profile.

Day 7: Write the 30 day retest plan and owners.

Soft next step

If you want a worked example of how this audit feeds a full GEO programme, I keep a plain language GEO service outline on vickylalwani.net. Use the checklist above first. Bring your sheet to any consultant you hire so they work from evidence, not vibes.

Sources

Google Search Central, "AI Features and Your Website": https://developers.google.com/search/docs/appearance/ai-features
Google Search Central, "Google's Guide to Optimizing for Generative AI Features on Google Search": https://developers.google.com/search/docs/fundamentals/ai-optimization-guide
Aggarwal et al., "GEO: Generative Engine Optimization", arXiv:2311.09735: https://arxiv.org/abs/2311.09735