SEO and content

Push page-two keywords to page one with better snippets

Finds queries ranking in positions 4 to 15 whose CTR lags the site's own benchmark, filters out unstable rankings, and rewrites WordPress meta titles and descriptions after row-by-row approval.

When to use it

When many of your keywords sit on page two or low on page one and you want more clicks without building new content.

What you get

A before/after table of new meta titles and descriptions for the top 10 pages, applied in WordPress only for the rows you approve, plus a baseline table to compare results in 4 weeks.

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, SEO Intelligence, WordPress

Fill in before sending

[domain][minimum impressions, e.g. 100]

Find quick ranking wins on [domain]: queries already close to page one where a better title and description can earn more clicks. 1. Run gsc_list_sites and pick the property for [domain]. Run list_wordpress_sites and pick the matching WordPress site. 2. Pull Search Console data for the last 28 days with dimension page,query. Keep the query and page pairs with an average position between 4 and 15 and at least [minimum impressions, e.g. 100] impressions. 3. Build the CTR benchmark from the site's own data, since no tool provides an industry benchmark. Group all page,query rows that meet the impression minimum into position bands 4-6, 7-10 and 11-15, and calculate the median CTR of each band. Flag pairs whose CTR is at least 30% below their band's median. Show me the band medians so I can see the reference. 4. Check stability. If a query is tracked, read its point series with seo_rank_history (target [domain], days 30). If it is not tracked, pull gsc_performance with dimension date, filtered to that query and its page, for the last 30 days. Drop any keyword whose position moved more than 5 places in that window, and note which source you used for each. 5. Take the top 10 remaining pairs by impressions, flagged ones first. Match each ranking URL to its WordPress ID: search wordpress_list_posts (types post and page) using words from the slug and confirm the returned link equals the URL. If the URL is a category or tag archive, find its term_id with wordpress_terms. If a URL cannot be matched, say so and leave it out. 6. Read the current meta title and description with wordpress_seo. Note fields that are empty and fall back to the plugin's template. 7. Draft a new title of up to 60 characters and a new description of up to 155 characters for each. Use the query naturally, state a concrete benefit and avoid clickbait. Show a before/after table with these columns: URL, query, position, impressions, CTR, band median, current title, new title (character count), current description, new description (character count). Do not apply anything yet; I will approve row by row. After I approve, call wordpress_set_seo only for the approved rows (id for pages and posts, term_id for archives, overwrite=true where a value already exists). Read each one back with wordpress_seo to confirm it saved, then run wordpress_purge_cache once. Check one updated page with site_read_url (outline=true) to confirm the live title changed. Finally give me a table with each URL, today's date as the change date, and its baseline position and CTR, so in 4 weeks I can rerun gsc_performance for the 28 days after the change date and compare.

Find quick ranking wins on [domain]: queries already close to page one where a better title and description can earn more clicks.

1. Run gsc_list_sites and pick the property for [domain]. Run list_wordpress_sites and pick the matching WordPress site.
2. Pull Search Console data for the last 28 days with dimension page,query. Keep the query and page pairs with an average position between 4 and 15 and at least [minimum impressions, e.g. 100] impressions.
3. Build the CTR benchmark from the site's own data, since no tool provides an industry benchmark. Group all page,query rows that meet the impression minimum into position bands 4-6, 7-10 and 11-15, and calculate the median CTR of each band. Flag pairs whose CTR is at least 30% below their band's median. Show me the band medians so I can see the reference.
4. Check stability. If a query is tracked, read its point series with seo_rank_history (target [domain], days 30). If it is not tracked, pull gsc_performance with dimension date, filtered to that query and its page, for the last 30 days. Drop any keyword whose position moved more than 5 places in that window, and note which source you used for each.
5. Take the top 10 remaining pairs by impressions, flagged ones first. Match each ranking URL to its WordPress ID: search wordpress_list_posts (types post and page) using words from the slug and confirm the returned link equals the URL. If the URL is a category or tag archive, find its term_id with wordpress_terms. If a URL cannot be matched, say so and leave it out.
6. Read the current meta title and description with wordpress_seo. Note fields that are empty and fall back to the plugin's template.
7. Draft a new title of up to 60 characters and a new description of up to 155 characters for each. Use the query naturally, state a concrete benefit and avoid clickbait.

Show a before/after table with these columns: URL, query, position, impressions, CTR, band median, current title, new title (character count), current description, new description (character count).

Do not apply anything yet; I will approve row by row. After I approve, call wordpress_set_seo only for the approved rows (id for pages and posts, term_id for archives, overwrite=true where a value already exists). Read each one back with wordpress_seo to confirm it saved, then run wordpress_purge_cache once. Check one updated page with site_read_url (outline=true) to confirm the live title changed.

Finally give me a table with each URL, today's date as the change date, and its baseline position and CTR, so in 4 weeks I can rerun gsc_performance for the 28 days after the change date and compare.

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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:
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- 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

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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.
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Classify each cluster:
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- 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.

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

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