Create and publish a job board blog post with AI

First, connect Cavuno to your AI client. Cavuno MCP supplies your domain, existing blog posts, authors, and tags. Your AI client uses its browser and any research tools you have connected to decide whether an article should exist, research it, and create something worth publishing.

A new article needs more than a keyword and a generated draft. The agent should check existing Cavuno posts, confirm that the search intent suits an article, identify concrete information gain, and verify the saved result.

Try it

If you want the agent to find a topic

Start with keyword research or a content gap analysis. Then ask:

Use the strongest opportunity from that research. Check it against my existing Cavuno posts and the current search results, then tell me whether it should become a new article, an update to an existing post, or nothing at all. Do not write it unless a new article is the right format and you can name the information gain we will add.

If you have a draft or source material

Paste the text, attach the file, or open the source page. Source material is an input, not proof that a new article deserves its own URL. Ask:

Evaluate whether this material should become a new article for my job board. Check my existing Cavuno posts for overlap, verify the search intent and claims, and identify what useful information is missing from the material and the strongest ranking results. If a new article is justified, use the material as a source rather than simply rewriting it.

If you only have a topic

Give the agent a concrete audience and subject. For example:

Evaluate a practical guide to preparing for a venture capital associate interview for my job board. Check my existing Cavuno posts for cannibalisation, inspect the live search results, and decide whether the search intent calls for an article. If it does, show me the reader problem, page type, information gain, and proposed outline before drafting it.

Research and browser access come from your AI client. Cavuno MCP supplies the context from your job board and stores the post.

Run the publishing workflow

Once the opportunity is clear, ask the agent to complete the article:

Create the article using the approved opportunity. Read the strongest current results rather than relying on snippets. Turn their omissions into specific information gain: original examples, a useful decision framework, a stronger template, first-party knowledge, or a more precise workflow. Write each major section separately against the strongest competing section for that subtopic, then assemble the article and remove repetition. Complete an internal link audit using my existing Cavuno posts. Save the finished article as a Cavuno draft, read it back, and show me the title, slug, metadata, links, and final content. Do not publish it yet.

When the draft is ready:

Publish the approved draft and send me the public URL.

What the agent will do

1. Build the content inventory. Use Cavuno MCP to build a compact, paginated post inventory, identify likely overlaps, then fetch their full content in batches. It also checks relevant public pages on your job board so an article does not compete with a page that already satisfies the same need.
2. Define the opportunity. State the reader, problem, target query or topic, search intent, expected page type, business value, and the natural next step your job board can offer.
3. Run the cannibalisation decision. Choose between creating a new article, refreshing an existing post, merging overlapping content, deferring the idea, or rejecting it. If another Cavuno post already owns the intent, the agent should not create a competing URL.
4. Research the live results. Open and read the current ranking pages, classify what searchers expect, and record what each strong result covers, believes, and omits. Product, directory, forum, or job-listing results are evidence that an advice article may be the wrong page type.
5. Plan concrete information gain. Name the exact examples, opinions, decision rules, templates, first-party observations, tables, or workflows the article will add. “Longer,” “more comprehensive,” and “more detail” are not information gain.
6. Gate the outline. Check that the proposed structure matches the search intent, answers the important questions, avoids SERP sameness, and would leave the reader able to do something the strongest results do not. If it cannot, stop before drafting.
7. Write section by section. Benchmark each major section against the strongest result for that subtopic, draft a more useful section, then assemble the article and remove repeated definitions, examples, caveats, and calls to action.
8. Complete the internal link audit. Add useful outgoing links to existing Cavuno posts and identify existing posts that should link back to the new article. Each suggestion should name the source, destination, anchor text, placement, and purpose.
9. Run the publication check. Verify the claims, title, excerpt, meta title, meta description, slug, author, tags, links, and any useful visuals. The article should answer its main promise near the beginning and contain no behind-the-scenes SEO commentary.
10. Save and verify. Create the Cavuno draft, read it back through Cavuno MCP, and confirm that the persisted title, slug, status, metadata, links, and key sections match the reviewed article.
11. Publish when asked. Publish that same reviewed draft, confirm the published readback, and return the public URL.

Not every plausible keyword deserves a new article. Stopping after the opportunity or cannibalisation check is a successful result when the wrong page would waste effort or compete with existing content. Finding declining existing posts is a separate workflow: use refresh job board blog posts with Google Search Console.