Compares your draft against every article currently ranking on page one of Google for a target keyword, rather than guessing at what's winning.
It surfaces the ranking factors shared across the top results, the specific content gaps and depth differences between your piece and theirs, and any unique elements like tools, media, or data you're missing, then delivers a priority-ordered improvement roadmap. Needs a model that can actually browse the live SERP.
Handles heading structure at both ends of the writing process: give it a guide topic and the key points it needs to cover before you write, and it proposes an SEO-friendly H1/H2/H3 structure to draft against. Paste in a finished draft afterward along with the primary keyword, and it rewrites the existing headers to better target that keyword while keeping the structure scannable. Useful for planning long-form guides and for cleaning up headers on drafts written without much structure in mind.
Generates a spread of title tag options for one topic, each built around a different psychological trigger so you can test angles instead of guessing at one headline.
- Inputs: topic/keyword and target audience
- Produces titles across ten frameworks: benefit-driven, curiosity gap, authority, urgency, problem-agitation, contrarian, social proof, how-to, list, comparison
- Keeps every title under 60 characters with the keyword worked in naturally
- Output: a table with framework, title, character count, emotional trigger, best-use case
Suggests internal links for a specific article using a site URL inventory (URLs plus their headlines), the article's own URL, and its full text, focused on sentence-level relevance rather than keyword matching.
Each recommendation includes the exact source sentence, a natural anchor text that still makes sense read out of context, the target URL, and the reader benefit of clicking, plus a validation checklist per link. Reject any suggestion that fails the 'makes sense out of context' test.
Roots a keyword or content strategy in Clayton Christensen's Jobs-To-Be-Done framework instead of raw search volume, starting from a single seed topic.
- Input: just a seed topic
- Identifies the functional, emotional, and social jobs customers are trying to accomplish
- Translates each job into 3-5 real search queries, problem-focused versus solution-focused
- Maps every query to a funnel stage: awareness, consideration, decision, retention
- Output: a JTBD query map table plus a job prioritization matrix and content gap analysis
Takes a raw list of keywords and groups them into topic clusters presented as a table, then suggests one specific blog topic per cluster so you're not left guessing what to write about each group. Meant to run right after keyword research and before assigning topics to writers or an editorial calendar. Works especially well fed directly with the long-tail keyword output from a seed-keyword prompt.
A structured conversion audit for an underperforming landing page, built from a screenshot description, the conversion goal, current conversion rate, and target audience.
- Evaluates the above-the-fold section, headline, and value proposition, scored 1-10
- Reviews CTA placement, copy, and trust signal inventory
- Identifies specific friction points along the conversion path
- Suggests concrete A/B tests to run
- Delivers a priority-ordered fix list
- Works best with an actual screenshot attached, since it leans on above-the-fold visual signals
Builds an FAQ section that reads as genuinely local rather than a generic template with the city name swapped in, using a business description, city/region, common customer concerns, and known local competitors as inputs.
It researches and organizes questions into general local, area-specific service, local pricing/availability, and regional regulation/compliance categories, plus competitor differentiators, and outputs FAQ schema markup ready to implement. Run in deep-research mode so local regulations get verified rather than guessed.
Expands a single service page into genuinely distinct versions for multiple target cities, using the original service page content, a list of target cities, and any local market insights you have. For each city it produces a localized title tag, a unique opening paragraph, a local market context section, city-specific pain points, where to place local proof or testimonials, neighborhood mentions, and local schema markup recommendations. Keep each city variant substantially unique, since thin swapped-city-name pages risk being treated as duplicate content.