Plan your job board content strategy with AI

First, connect Cavuno to your AI client. Cavuno MCP gives the agent the real shape of your job board and existing blog. A content strategy adds the decisions that research alone does not make: who the content serves, which business goals it supports, what to publish first, how the pieces connect, and what success should look like.

Use the dedicated keyword research and content gap recipes when you need deeper work on either input. This recipe turns those findings into one coherent plan.

Try it

Ask:

Use Cavuno to understand my job board, all my published posts, and the journeys I offer job seekers and employers. Use the keyword research and content gap analysis from this conversation if they exist. Build a content strategy that connects audience problems to my business goals. Show what to publish, refresh, merge, or stop doing—and explain the priority.

Ask for an operational plan rather than a list of ideas:

Turn the strategy into a publishing roadmap. For each item, show the target audience, problem, search intent, journey stage, format, topic cluster, information-gain requirement, relevant existing Cavuno posts, internal-link role, natural next step, expected business value, effort, priority, and success measure. Sequence dependencies before the articles that rely on them.

What the agent will do

1. Use Cavuno MCP to inspect the domain, job and company mix, taxonomies, relevant board settings, and a compact, paginated post inventory. It fetches full post content in batches only when a priority, overlap, or merge decision depends on it. The agent uses that evidence to infer the niche and asks only for a business goal it cannot discover.
2. Define the priority audiences and problems. A strategy can serve job seekers, employers, or both, but each planned article needs one clear reader and job to be done.
3. Connect content to business goals such as qualified job-seeker traffic, newsletter growth, employer demand, job-posting revenue, topical authority, or retention. It does not bolt a commercial call to action onto an unrelated keyword.
4. Incorporate the approved keyword clusters and content gaps without duplicating them. It balances discovery, evaluation, and action-stage content according to the board’s current needs rather than filling a generic funnel quota.
5. Decide the role of each item: create a new article, refresh an existing one, merge overlap, build a supporting cluster, improve internal linking, or defer it. Existing Cavuno coverage and cannibalisation risk stay visible in every decision.
6. Prioritise business value, audience value, evidence, information gain, effort, and dependencies separately. This prevents a high-volume but weak-fit topic from crowding out a smaller workflow that is genuinely useful and commercially relevant.
7. Produce a practical publishing plan with clear sequencing, ownership decisions, internal-link paths, and measures. It will verify that every priority traces back to an audience problem, business goal, or research finding. When an article is ready to produce, continue with create and publish a job board blog post with AI.

The strategy remains a decision document until you ask the agent to create or update content. Cavuno MCP lets the same conversation move from planning into verified Cavuno writes without making planning itself an automatic publishing action.