SEO and content

Find and resolve keyword cannibalization between client pages

Uses Search Console page and query data to find URLs splitting the same query, decides merge, differentiate or canonical per case, and applies redirects and canonicals only after preview and approval.

When to use it

When two or more of your pages compete for the same Google query and neither ranks as well as it should.

What you get

A table of cannibalized queries with a merge, differentiate or canonical decision for each, then 301 redirects and canonicals applied once you approve the preview.

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, WordPress

Fill in before sending

[property]

Check the Search Console property [property] for keyword cannibalization over the last 90 days. 1. Confirm the property with gsc_list_sites. Run list_servers to see whether the site is on Opus hosting or a connected WordPress site. 2. Pull gsc_performance with dimension page,query for 90 days, sorted by impressions, with a high limit (page through if needed). From these rows, calculate each URL's share of impressions per query. Keep queries with at least 200 total impressions where two or more URLs each get at least 10%. 3. For each case, show every competing URL with its impressions, clicks, CTR and average position for that query, plus how many other queries each URL earns 50 or more clicks on. 4. Read the competing pages (wordpress_get_post on WordPress, site_read_url with outline=true on hosting) and the current SEO fields with wordpress_seo. Summarise in one line what each page targets. 5. Decide per case: - If one URL has 70% or more of the clicks and the other has no unique query with 50 or more clicks, merge the weaker page's useful sections into the stronger one and 301 redirect the weaker URL. - If the pages serve clearly different intents, keep both and specify the title, H1 and section changes that separate them. - If they are near-duplicates that must both stay live, set a canonical on the weaker page to the stronger URL. 6. Deliver a table with these columns: query, URLs, metrics, decision, exact changes. Order by clicks at stake. Do not apply anything yet. Show the full list of content merges, redirects and canonical changes as a preview and wait for my approval. Then: - Opus hosting: preview redirects with site_redirects (mode add, confirm=false), apply with confirm=true. Change canonical tags with site_edit_file after reading the file with site_read_file. - WordPress: set canonicals with wordpress_set_seo. Apply approved content merges with wordpress_update_post. For redirects, check wordpress_list_plugins: if a redirect plugin is active, give me the source and target list to enter there. If not, offer a WPCode snippet that issues the 301s on template_redirect via wordpress_snippet, shown as a preview with confirm=false before creating it. Run wordpress_purge_cache afterwards. Finally verify each redirected URL returns 301 to the right target with site_read_url.

Check the Search Console property [property] for keyword cannibalization over the last 90 days.

1. Confirm the property with gsc_list_sites. Run list_servers to see whether the site is on Opus hosting or a connected WordPress site.
2. Pull gsc_performance with dimension page,query for 90 days, sorted by impressions, with a high limit (page through if needed). From these rows, calculate each URL's share of impressions per query. Keep queries with at least 200 total impressions where two or more URLs each get at least 10%.
3. For each case, show every competing URL with its impressions, clicks, CTR and average position for that query, plus how many other queries each URL earns 50 or more clicks on.
4. Read the competing pages (wordpress_get_post on WordPress, site_read_url with outline=true on hosting) and the current SEO fields with wordpress_seo. Summarise in one line what each page targets.
5. Decide per case:
   - If one URL has 70% or more of the clicks and the other has no unique query with 50 or more clicks, merge the weaker page's useful sections into the stronger one and 301 redirect the weaker URL.
   - If the pages serve clearly different intents, keep both and specify the title, H1 and section changes that separate them.
   - If they are near-duplicates that must both stay live, set a canonical on the weaker page to the stronger URL.
6. Deliver a table with these columns: query, URLs, metrics, decision, exact changes. Order by clicks at stake.

Do not apply anything yet. Show the full list of content merges, redirects and canonical changes as a preview and wait for my approval. Then:
- Opus hosting: preview redirects with site_redirects (mode add, confirm=false), apply with confirm=true. Change canonical tags with site_edit_file after reading the file with site_read_file.
- WordPress: set canonicals with wordpress_set_seo. Apply approved content merges with wordpress_update_post. For redirects, check wordpress_list_plugins: if a redirect plugin is active, give me the source and target list to enter there. If not, offer a WPCode snippet that issues the 301s on template_redirect via wordpress_snippet, shown as a preview with confirm=false before creating it. Run wordpress_purge_cache afterwards.
Finally verify each redirected URL returns 301 to the right target with site_read_url.

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Build intent-based keyword clusters from a few seed keywords

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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Build a seasonal content and ad calendar from keyword demand

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.