SEO and content

Page-2 product and category queries ranked by conversion potential

Finds Search Console queries at positions 8 to 20 on product and category pages, scores them by impressions times the page's organic conversion rate from GA4, drops marketplace-dominated SERPs, and previews new SEO titles and meta descriptions in WordPress.

When to use it

When you want quick SEO wins from product and category pages sitting on page 2 that already convert well.

What you get

The top 10 scored page-2 queries with current and proposed titles and meta descriptions, written to WordPress SEO fields after your approval.

Does it change anything in my account?

Only with your approval. The assistant first shows a preview of every change and waits for your go-ahead.

Works with

Search Console, GA4, SEO Intelligence, WordPress

Fill in before sending

[site][GA4 property][minimum impressions][product URL pattern, e.g. /urun/][category URL pattern, e.g. /kategori/]

Find page-2 quick wins on pages that actually convert. Site: [site], GA4 property: [GA4 property]. Step 1: candidate queries from Search Console, last 90 days. - Pull page,query rows with a high limit. - Keep rows with average position 8 to 20 and at least [minimum impressions] impressions. - Keep only product and category URLs: [product URL pattern, e.g. /urun/] and [category URL pattern, e.g. /kategori/]. Exclude /blog/ and other informational paths. Step 2: conversion data from GA4. - List the key events and confirm the purchase event name. - For the same 90 days, get organic sessions and purchases per landing page (landing page with channel as the second dimension, plus landing page × event if there are several key events). Page conversion rate = organic purchases / organic sessions. - Score each query: impressions × page conversion rate. Pages with no organic purchases score 0; list them separately, do not drop them silently. Step 3: live SERP for the top 15 by score. - Check seo_history first and reuse stored SERPs. - Count how many of the top 10 organic results belong to these marketplaces and large retailers: [marketplace and big retailer domains, e.g. trendyol.com, hepsiburada.com, amazon.com.tr, n11.com]. If 5 or more, mark the query "hard" and drop it. Step 4: current SEO fields. - Product and page URLs: find the post id by slug with wordpress_list_posts (type product for WooCommerce products), then read its SEO title and meta description. - Category URLs are archives, not posts: find the term id with wordpress_terms and read the archive's SEO fields by term id. Deliver the top 10 remaining queries in a table with these columns: - query - page - position - impressions - click-through rate - page conversion rate - score - current title and meta description - proposed title (≤ 60 characters) - proposed meta description (≤ 155 characters) The proposed title must include the query. Do not change a title that already contains it, unless its click-through rate is below 2%. If two queries point to the same page, write one title that serves the higher-scoring query. Show the WordPress SEO changes as a preview (page id or term id, old value, new value) and wait for my approval before writing anything. After I approve, write them with wordpress_set_seo and purge the cache.

Find page-2 quick wins on pages that actually convert. Site: [site], GA4 property: [GA4 property].

Step 1: candidate queries from Search Console, last 90 days.
- Pull page,query rows with a high limit.
- Keep rows with average position 8 to 20 and at least [minimum impressions] impressions.
- Keep only product and category URLs: [product URL pattern, e.g. /urun/] and [category URL pattern, e.g. /kategori/]. Exclude /blog/ and other informational paths.

Step 2: conversion data from GA4.
- List the key events and confirm the purchase event name.
- For the same 90 days, get organic sessions and purchases per landing page (landing page with channel as the second dimension, plus landing page × event if there are several key events). Page conversion rate = organic purchases / organic sessions.
- Score each query: impressions × page conversion rate. Pages with no organic purchases score 0; list them separately, do not drop them silently.

Step 3: live SERP for the top 15 by score.
- Check seo_history first and reuse stored SERPs.
- Count how many of the top 10 organic results belong to these marketplaces and large retailers: [marketplace and big retailer domains, e.g. trendyol.com, hepsiburada.com, amazon.com.tr, n11.com]. If 5 or more, mark the query "hard" and drop it.

Step 4: current SEO fields.
- Product and page URLs: find the post id by slug with wordpress_list_posts (type product for WooCommerce products), then read its SEO title and meta description.
- Category URLs are archives, not posts: find the term id with wordpress_terms and read the archive's SEO fields by term id.

Deliver the top 10 remaining queries in a table with these columns:
- query
- page
- position
- impressions
- click-through rate
- page conversion rate
- score
- current title and meta description
- proposed title (≤ 60 characters)
- proposed meta description (≤ 155 characters)

The proposed title must include the query. Do not change a title that already contains it, unless its click-through rate is below 2%. If two queries point to the same page, write one title that serves the higher-scoring query.

Show the WordPress SEO changes as a preview (page id or term id, old value, new value) and wait for my approval before writing anything. After I approve, write them with wordpress_set_seo and purge the cache.

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More prompts

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A ranked list of keyword clusters with volume, difficulty and the right page type or ad group for each.

When to use itWhen you are starting keyword research for a business and only have a few seed keywords.

I sell [product or service] in [city or country]. Start from these seed keywords: [3 to 5 seed keywords].

First check seo_history so you reuse earlier results instead of paying for the same query twice. Then use Keyword Planner and SEO keyword research to collect ideas with average monthly searches, competition and keyword difficulty.

Clean the list:
- remove keywords under 30 monthly searches
- remove job, free, DIY, course and competitor-brand terms unless they clearly fit my business
- merge close variants and plurals

Cluster the rest by search intent, with one cluster per distinct need a searcher has. For each cluster give:
- cluster name and head keyword
- 5 to 10 supporting keywords
- combined monthly volume and average difficulty
- intent: informational, commercial or transactional
- best use: service page, blog post or a Google Ads ad group

Rank the clusters by volume and fit for a small business with a limited budget. Mark the top 3 to start with and explain why in one line each.

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Turn search terms that convert in ads into organic content clusters

Uses 90 days of converting Google Ads and Microsoft Ads search terms to find topics where you pay for conversions but have weak or no organic visibility. Returns a prioritized content plan and a preview-only list of keyword additions.

When to use itWhen you pay for conversions on search terms but are unsure which topics deserve organic content so you can rely less on ads.

I want to use my ad data to decide what to write about. Use the last 90 days on every source.

1. Pull my Google Ads search terms for the last 90 days (days=90, or an explicit start and end date), sorted by conversions. Keep only terms with at least 1 conversion. If the response is truncated, page through until you have every converting term.
2. If Microsoft Ads is connected, find the default account and pull its search terms for the same 90 days. Keep only terms with at least 1 conversion and add them to the pool, marking which platform each term came from.
3. Group all converting terms into topic clusters by search intent. For each cluster show total cost, conversions and cost per conversion, split by platform where both are present.
4. Find my Search Console property (use [site property] if I named one) and pull organic data for the last 90 days with dimension query, plus a page,query pull so you can see which page ranks for each query. Use a high enough limit to cover the long tail.
5. Match each cluster to the Search Console queries that express the same intent. Use your judgement for close variants and wording differences, and say which queries you counted. Report organic clicks, impressions and average position per cluster, and the page that ranks best for it if there is one.

Classify each cluster:
- Content gap: converts in ads, organic average position worse than 10 or no organic impressions. I should build a page.
- Improve: converts in ads, organic position 4 to 10. Name the existing page that needs strengthening.
- Covered: organic position 1 to 3. Note it as a possible place to test lower ad spend later.

Deliver a table sorted by ad conversions, then the top 5 content-gap clusters with a suggested page title and URL slug for each.

Separately, list my Google Ads keywords (list_entities, entity keywords) and show converting Google search terms that are not yet keywords in the account. For Microsoft terms, just flag them; do not check or change Microsoft keywords. Do not add anything. If you think some Google terms should be added as exact match, tell me which ad group each one belongs in and run add_keywords with confirm=false so I see the exact preview. Wait for my approval before applying.

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Cluster the searches that find my Business Profile and fix my services

Clusters of the real Google Search and Maps queries behind your listing, compared with your services and categories, plus a previewed fix.

When to use itWhen your Business Profile gets views but you suspect your services and categories do not match what people really search for on Google and Maps.

For my Business Profile location [location name], pull the search terms people used on Google Search and Maps to find the listing over the last 3 months.

Cluster the terms into:
- brand searches
- service searches, with one cluster per service
- "near me" and neighborhood or district searches
- problem-based searches, such as a symptom or an issue the customer has

For each cluster show total impressions and the top 5 terms.

Then read my listing details: primary and additional categories, and the services list. Compare them with the clusters and tell me:
- service clusters with real demand that are missing from my services list
- services I list that get almost no searches
- whether an additional category would match a strong cluster. Search the category names to confirm they exist.

Draft the new service entries, with a name and a description of up to 300 characters for each, written around the words people actually search.

Show me the exact changes as a preview first. Do not update the listing until I approve.

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A 12-month calendar showing when each keyword cluster peaks, when to publish content and when to raise or lower ad budgets.

When to use itWhen you want to time content and ad budgets around real seasonal demand instead of guessing when interest peaks.