# LinkedIn Maxxing with Cursor: 12 Prompts That Turn a Folder into a Content Desk

> LinkedIn maxxing with Cursor: 12 prompts for research, drafts, review and reporting, plus where Cursor keeps your context, voice guide and rules.

Source: https://linkedinlikes.com/blog/linkedin-maxxing-with-cursor · Updated: 2026-11-12

## Key takeaways

- Cursor dropped automatic Memories in version 2.1, so anything it should remember has to live in files such as Project Rules.
- A rule saved as .mdc with alwaysApply: true loads into every Agent chat in the folder; a plain .md file inside .cursor/rules is ignored.
- Prompts 1 to 8 cover research and writing, prompts 9 to 12 connect posts to sales conversations and reporting.
- Automations, added in March 2026, run agents on a schedule or an event in a cloud sandbox that starts from your repository.
- Whatever agent writes the draft, the first hour after publishing still decides how far the post travels.

LinkedIn maxxing with Cursor means using Cursor's agent on a folder of your posts, transcripts and rules, so it can research topics, draft posts in your voice, review them against a rubric and report on results. The 12 prompts below do that work in order, from your first voice guide to a monthly review.

Handing an AI a topic and asking for a LinkedIn post skips the work that makes a post worth reading: knowing your prospects, finding an angle, collecting evidence and knowing which claims you can stand behind. These prompts turn that work into a routine you can repeat, from market research to a content calendar to tracking which posts led to real business conversations.

By the end you will have:

- a voice guide built from the way you already write and speak;
- a research routine that finds ideas before the calendar needs them;
- a drafting and review flow with written quality standards;
- a reporting loop that tells you what to write next.

Cursor is built for code, but its agent can open and edit any file in the folder you give it. A folder of posts, transcripts and rules becomes a content desk that keeps your standards in files, so they survive after a chat ends. Bookmark this page for your next planning session. If you work in another agent, the same series covers [LinkedIn maxxing with Claude Code](https://linkedinlikes.com/blog/linkedin-maxxing-with-claude-code).

## Before your first prompt: give Cursor your business context

A job title and a request to "sound human" are not enough. Cursor needs to know who you sell to, what you believe and which claims you can prove.

Paste this first:

> Treat everything below as the standing context for my LinkedIn writing. Store it in a reference file I can reuse and tell me its path. 
 Company: [name, website, what we sell, how long a typical sales cycle runs]. 
 Readers: [job roles, company sizes, industries, regions]. They struggle with [problems], push back with [objections], and go looking for help when [trigger events] happen. 
 Positioning: we get [audience] to [outcome] by [method]. What sets us apart: [differences]. We are the wrong choice for [disqualifiers]. 
 Opinions: my three views on the industry are [views]. I disagree with [popular advice], for this reason: [reason]. 
 Proof: use only these approved case studies, results, examples and personal stories: [sources]. Never make up clients, revenue numbers, anecdotes or quotes. 
 Voice: these [10-20 posts] and [transcripts or writing samples] sound like me. Stay away from [phrases, tones and formatting habits]. 
 Goals: what I want most from LinkedIn is [qualified conversations / deal support / recruiting / awareness]. My offer is [offer], and readers who are interested should next [action]. 
 How to work: use connected sources only where access exists and is authorized; when a source won't open, ask me for an export or pasted text. LinkedIn publishing, comments, connection requests and messages stay with me. Save drafts to [review location], keep evidence apart from inference, and mark anything that needs my input.

Update that context whenever your offer, your audience or your opinions change.

### Where does Cursor keep this context?

Cursor removed its automatic Memories in version 2.1, and a new chat doesn't see earlier chats. Only what you write into files carries over, so put the context in a file Cursor loads by itself.

Make one folder for all LinkedIn work, such as `linkedin/`, with `context/`, `drafts/`, `published/`, `research/` and `reports/` inside it. Then ask the agent to save the answers above as a Project Rule:

```
.cursor/rules/business-context.mdc
---
description: Business context for every LinkedIn post
alwaysApply: true
---
(company, readers, positioning, proof and goals from the context prompt)
```

With `alwaysApply: true`, the rule joins every Agent chat opened in this folder. The file extension matters: a plain `.md` file inside `.cursor/rules` is ignored. Other rules can attach when a matching file is in context (`globs`), when the agent decides the description fits, or only when you @-mention them; business context is the one that should always be on. Keep every rule short, far below the 500 lines Cursor recommends, and give the voice guide its own rule later.

Two more places are worth knowing about. `AGENTS.md` in the folder root is plain Markdown without frontmatter, and other agents read it as well, which helps if you change tools. User Rules, found under Customize -> Rules, follow you into every project, so they suit personal habits like spelling or tone; keep business facts out of them. On Teams and Enterprise plans an admin can also set Team Rules from the dashboard, which lets a marketing team share one list of approved claims and phrases to avoid. Put the folder under git, because Cursor's cloud agents and Automations start from the repository and never see your laptop.

Longer procedures, like the editorial rubric in prompt 8, work better as skills in `.cursor/skills/{name}/SKILL.md`. A rule is loaded in full into every matching chat, while a skill loads only when a request matches its description, which keeps ordinary chats lighter.

```
linkedin/
 AGENTS.md instructions shared with other agents
 .cursor/
 rules/
 business-context.mdc alwaysApply: true
 voice-guide.mdc globs: drafts/**
 skills/editorial-review/SKILL.md
 mcp.json servers for this folder only
 context/ writing samples, call transcripts, exports
 research/ prospect questions, X notes, outlier posts
 calendar/ one brief per post
 drafts/
 reviews/
 published/
 inbox/
 reports/
```

## Part 1: research and writing (prompts 1 to 8)

## 1. How do you build a LinkedIn voice guide in Cursor?

A voice guide earns its place when it explains the decisions behind your writing: how you set up an argument, where you bring in examples and how you move from one idea to the next. A label like "conversational and authoritative" gives a model almost nothing to work with.

> Study the posts and transcripts I gave you and turn them into a working LinkedIn voice guide. Cover: 

how I start a post, explain an idea and wrap up an argument;
the words I reach for, my usual sentence and paragraph length, and how often I use lists;
how I disagree, admit doubt, joke and sound sure of myself;
phrases that come naturally to me, quoted word for word with the source of each;
habits from my weaker samples that should not turn into rules.

 Add a do-not-use list of phrases and patterns, including needless repetition, staged drama, certainty I can't back up and too many one-line paragraphs. End with three before-and-after edits that move generic copy into my voice without changing what it says. Make the guide concrete enough that another writer could follow it.

**In Cursor:** put your 10 to 20 best posts and a few transcripts into `context/samples/` and pull in the whole folder with `@context/samples/` in an Agent chat. Ask for the guide as `.cursor/rules/voice-guide.mdc` with `globs: drafts/**`, so it attaches whenever a draft file is in context and stays out of other chats. When you rework the before-and-after examples by hand, do it inside the file: Tab predicts your next edit from the ones you just made, which speeds up rewriting. Rules reach only the Agent and never Tab, so Tab won't apply the guide for you; the review in prompt 8 handles that.

**Why it helps:** a written guide turns feedback into something you reuse. You fix the instruction once instead of correcting the same phrasing every week.

## 2. How do you find the questions prospects actually ask?

Sales calls and customer conversations surface questions that no keyword tool shows. The useful ones tend to sit around a decision someone finds hard to make.

> Go through the sales calls, support threads, customer interviews and closed-lost notes from [period]. Sort the questions that keep coming up into five groups: 

problems prospects are trying to make sense of;
worries that slow down a decision;
alternatives they weigh us against;
expectations that end in disappointment;
questions they ask just before they decide.

 For each theme, give one anonymized quote, where it came from, how many separate conversations raise it, the person's role and how far along their decision they are. Propose two LinkedIn angles per theme: one that helps a reader diagnose the problem and one that helps them judge a solution. Rank the ten strongest by how often they recur, how much they matter commercially and how good our evidence is. Keep what people said apart from your reading of it.

**In Cursor:** export calls and support threads as text into `context/calls/`, and drag a CSV of closed-lost notes straight into the chat. Do the first pass in Ask mode, which reads and answers without changing files, to see whether the themes hold up. Then switch to Agent and have it write `research/prospect-questions.md`, with the file path beside every quote so each source opens in one click. Ask mode is also a fast way to get oriented in a messy export folder: ask which files are transcripts, which are notes and which are duplicates before the analysis begins. Later on, `@Codebase` runs a semantic search over the whole folder, which helps when a question you track also shows up in notes stored elsewhere.

**Why it helps:** you get a backlog of post ideas tied to conversations your company is already having, in wording you already know and with evidence you can check.

## 3. How do you bring topics from X into your LinkedIn feed?

A debate picking up speed on X can turn into a good LinkedIn post if you explain why it matters to your prospects and add something of your own beyond a summary.

> Look at the past [seven days] of discussion about [topics] and from these accounts: [accounts], using @Web, public sources you can reach and any X search tool connected through MCP. Pick up to 10 developments or debates that matter to [reader]. For each one, return: 

the original source, its author, the date and the link;
a neutral summary of what happened or what people are arguing;
the strongest competing readings;
what it means day to day for our audience;
an angle we could back with our own experience or evidence.

 Treat like and comment counts as a snapshot, don't take popularity as proof, and check factual claims against the original source where you can. Suggest three ideas worth developing and say what I would need to add before each could become a credible LinkedIn post.

**In Cursor:** X access isn't built in. The agent searches the open web (`@Web` in chat), which reaches articles, newsletters and public pages, though nothing behind a login. For accounts you follow closely, paste their recent posts into `research/x-notes.md` and mention that file next to `@Web`. Close the prompt with "list every URL you opened" so you can check the snapshot before relying on it. If you watch the same accounts each week, keep them in `research/sources.md` and mention the file instead of retyping the list. This also makes a sensible first Automation: on a weekly schedule, a cloud agent runs the prompt against that sources file and leaves the result on a branch, ready to read on Monday morning.

**Why it helps:** research gives you somewhere to start, and your judgment gives people a reason to follow you. A good adaptation ties the debate to a decision your readers really face.

## 4. How do you study posts that beat their author's usual results?

Measuring every post against a celebrity account's biggest hit skews your research. Compare a post with what that author normally gets first, and only then look for what might explain the gap.

> Here is my LinkedIn swipe file with the performance data I have: [file]. Where there is enough comparable data, set each post's reactions and comments against the author's median for a similar format and period. Show the math and flag small samples, missing numbers and very recent posts. For the clearest outliers, describe: 

the topic and the reader it was written for;
the opening, the argument, the supporting evidence and the ending;
the format and rough length;
whether the comments suggest real interest from prospects;
other factors that could explain the result, such as a launch, a news event or unusually wide distribution.

 Turn the findings into five reusable structures, then suggest one original idea for each, drawn from what I know. Don't copy distinctive wording, and don't present a pattern you observed as an algorithm rule.

**In Cursor:** keep the swipe file as a CSV in `research/` and ask the agent to work out the medians with a short script it runs in the terminal, saving the script beside the output. That way "show the math" becomes something you can rerun next month. To measure one of your own posts against your normal numbers, the [LinkedIn engagement rate calculator](https://linkedinlikes.com/linkedin-engagement-rate-calculator) gives you a baseline without a spreadsheet. Once the outlier table gets long, ask for it as a Canvas: a sortable view next to the chat, built only from the CSV you supplied.

**Why it helps:** you end up with hypotheses worth testing, and you avoid assuming that copying a popular post's layout will copy its results.

## 5. How do you turn recorded talks into original posts?

A transcript often hides a strong post inside a story, a throwaway explanation or a disagreement that got less attention in the room than it deserved.

> Go through this transcript from an interview, podcast, webinar or meeting: [source]. Find eight moments that could carry an original LinkedIn post, favouring decisions, mistakes, surprising observations, practical explanations and opinions grounded in experience. For each moment, list: 

the exact quote with its timestamp or location in the source;
the main idea and who would gain from it;
any context needed so the speaker isn't misrepresented;
facts we already have that support it;
one follow-up question that would make the idea stronger.

 Choose the three strongest and draft a post for each using my voice guide. Keep the speaker's level of certainty, strip anything confidential, and leave placeholders wherever a result or example still needs confirming.

**In Cursor:** save the transcript as a text file and mention it with `@`. Webinar slides can come along as images, since the agent reads pictures as well as text, so a chart the speaker talked over gets quoted correctly. Run the prompt in two halves: first the table of eight moments in `research/moments.md`, then, once you have struck out the weak ones, the three drafts in `drafts/`. Writing into `drafts/` is what switches on the scoped voice-guide rule; mention `@voice-guide.mdc` too if you want to be sure it applies. Cursor saves a checkpoint before large edits, so if the drafts overwrite something you liked, restoring the checkpoint brings the files back; the conversation itself stays where it is.

**Why it helps:** you publish from knowledge that already exists, without asking anyone to invent a fresh opinion each time the calendar has a gap.

## 6. How do you plan a four-week LinkedIn content calendar?

A good calendar weighs what prospects need to understand against what you have enough evidence to say. If every slot is another version of your offer, the feed starts to repeat itself.

> Using the approved ideas from our research, plan four weeks of LinkedIn posts at [three posts a week]. Mix: 

practical explanations that help prospects solve a problem;
opinions backed by experience or evidence;
customer stories we have permission to share;
answers to objections and decision questions;
an occasional offer post with a relevant next step.

 For every post, name the reader, its one goal, the central argument, the source material, a suggested format and what the reader should do next. Check the plan for repeated topics, overlapping arguments, unsupported claims and too many promotional posts. Write full briefs for week one and shorter outlines for weeks two to four. Keep one slot each week open for a timely swap instead of treating the plan as fixed.

**In Cursor:** start this one in Plan mode. Plan writes down what it intends to do and waits for your approval, which is the right moment to fix the mix of post types before twelve briefs appear in `calendar/`. Pick the slots with the [best time to post on LinkedIn](https://linkedinlikes.com/best-time-to-post-on-linkedin) tool, and lay out the slides for every brief marked as a carousel in the [LinkedIn carousel maker](https://linkedinlikes.com/linkedin-carousel-maker). Ask for one file per brief, so the weekly timely swap means editing one file instead of regenerating the month. In the Agents Window the four weeks can even run as parallel agents, each in its own worktree, and you merge the briefs you accept.

**Why it helps:** a calendar cuts weekly planning time and still leaves room for better ideas, new evidence and changes in the market.

## 7. How do you get drafts that make an actual argument?

Research should settle the argument before Cursor starts polishing sentences. Skip that step and you get tidy writing that takes several paragraphs to say something obvious.

> Draft three LinkedIn posts from these approved briefs: [briefs], following my business context and voice guide. In each draft: 

argue one clear claim for one reader;
open with a specific observation, tension, result or question that the brief supports;
walk through the reasoning with a concrete example;
use bullets where they make parallel items easier to scan;
vary sentence and paragraph length, with an occasional one-line paragraph for weight;
close with what the idea means for the reader or a natural next step.

 Aim for [180-300 words] unless the idea needs another length. Avoid repeated endings, made-up personal stories, inflated promises and formula contrasts. Give two alternative openings with a line on how each shifts the focus, and list missing evidence apart from the publishable copy.

**In Cursor:** one file per draft, named by date and topic, with the alternative openings and the missing-evidence list under a divider at the bottom so the copy above stays clean. When you want truly different takes on the same brief, `/best-of-n` runs the request in parallel isolated worktrees and shows the results side by side. Run the openings you like through the [LinkedIn hook generator](https://linkedinlikes.com/linkedin-hook-generator) for a few more variations before choosing.

**Why it helps:** tight constraints produce drafts that are easier to judge, so your review goes into the argument instead of cutting filler.

## 8. How do you set up a separate editorial review?

Give the reviewer one clear job: find the weak spots in the draft and say how to fix them. A comforting "this is strong" is no editorial process.

> Check these LinkedIn drafts against the brief, the source material, my business context and my voice guide. Score each out of 100: 

relevance to prospects: 25;
specific detail and supporting evidence: 25;
strength and clarity of the argument: 20;
match with my voice: 20;
readability and formatting: 10.

 Show what each score is based on. Verify every factual claim, mark generic passages, and call out repetition, abrupt one-line fragments and paragraphs that would read better as lists. Return a publishable revision, a short note on the changes that matter and any open questions. Treat made-up evidence, exposed confidential details or a misquoted source as blocking, whatever the score. Leave everything in the review file for a human to approve.

**In Cursor:** run the review in a fresh chat so the reviewer doesn't inherit the drafting conversation, and score in Ask mode, where it can only read. Switch to Agent only when you want the revision written into `reviews/`. Save the rubric as a skill, `.cursor/skills/editorial-review/SKILL.md`: the agent loads a skill when a request matches its description, so next time "review these drafts" is enough. Before publishing, paste the approved text into the [LinkedIn post preview](https://linkedinlikes.com/linkedin-post-preview) to see where the "see more" fold lands on mobile, and run it through the [LinkedIn post analyzer](https://linkedinlikes.com/linkedin-post-analyzer) as an outside check.

**Why it helps:** a rubric writes your standards down. The score sorts the feedback, while the source checks and an editor's judgment decide whether the post is ready.

## What should you do in the first hour after publishing?

Prompt 8 ends with a revision you approve and post. From there the rest of the maxxing method is the same no matter which agent wrote the draft: collect as many reactions as you can in the first hour after the post goes live.

**Begin with people you can reach.** Colleagues, friends, relatives and clients are the best people to ask, since you can actually contact them. Send them the link as soon as the post is up.

**If you don't have those people, use a moderated community.** The free groups in the table below are ones we checked for other articles on this site.

**LinkedInLikes is our own option for this step.** It is a free community: you paste the link to your post, pick likes, comments, reposts or saves, and set the points per action and how many actions you want. Members act by hand from their own LinkedIn accounts and write comments in their own words. You get 100 starter points after confirming your email, and you add more by doing other members' tasks, up to 600 points a day. If someone confirms an action they didn't do, you can report it within 72 hours and the points are taken back.

Cursor doesn't connect to LinkedInLikes; once the post is live, you add its link on the site yourself. Start on the [comments page](https://linkedinlikes.com/linkedin/comment) or [create a free account](https://linkedinlikes.com/signup).

## Which free LinkedIn groups can you join for early reactions?

We checked these free communities on Oct 21, 2026, and the member counts are what each platform showed that day. All of them post in English. Each group has its own admins, so read the pinned message before you share your first post.

| Community | Where | Members on Oct 21, 2026 | What it is for |
| --- | --- | --- | --- |
| [LinkedIn Engagement Pod & Group](https://t.me/linkedin_engagement_pod) | Telegram | 1,833 | an all-purpose pod for likes and comments |
| [LinkedIn Engagement Pod (RVision Digital)](https://t.me/linkedinengagementpod) | Telegram | 1,064 | sharing posts for likes and comments, plus connections and skill endorsements |
| [Linkedin engagement pod](https://t.me/linkedinpod1) | Telegram | 488 | an all-purpose LinkedIn pod |
| [Social Linkedin/FB/IG Engagement Pod](https://t.me/socialengagementpod) | Telegram | 77 | react to the last 5 posts and leave a real comment on each before sharing your own |
| [LinkedIn For Breakfast](https://www.skool.com/linkedin-for-breakfast) | Skool | 6,603 | a free LinkedIn community with pods and live events |
| [Linkedin Likes and Engagement](https://t.me/linkedinlikesandengagement) | Telegram | 697 | like swaps and new connections |
| [Linkedin Pod (SF founders like-for-like)](https://chat.whatsapp.com/DBOyIPrDR5IK27yzW606sF) | WhatsApp | not shown (WhatsApp hides the count) | founders trading likes on LinkedIn |
| [r/linkedintips](https://www.reddit.com/r/linkedintips/) | Reddit | 38,314 | tips on getting a brand seen on LinkedIn |
| [LinkedIn Club](https://t.me/linkedinclub) | Telegram | 552 | networking and new connections |
| [r/LinkedInGrowth](https://www.reddit.com/r/LinkedInGrowth/) | Reddit | not shown | growing a LinkedIn profile and network |

If you would rather not hunt through groups, put the post on [LinkedInLikes](https://linkedinlikes.com/linkedin/like), where members pick it up from a shared task list.

## Part 2: pipeline and results (prompts 9 to 12)

## 9. How do you write posts around deals that went quiet?

Some of your most useful posts can answer a worry several prospects raised in private. The aim is to explain the issue well enough that the next prospects arrive better informed.

> Look through the CRM records and call notes for qualified deals that stopped moving during [period]. Find the objections that repeat and sort them into: 

objections someone actually voiced in a conversation;
possible explanations nobody has confirmed;
operational delays that a post is unlikely to fix.

 For each repeated objection, note the person's role, the deal stage, the evidence available and the question a helpful LinkedIn post should answer. Suggest five content briefs. Each should state the concern fairly, show where our approach fits, admit where it doesn't and name the proof needed. Draft the two strongest posts without naming prospects or revealing deal details. Separately, list approved resources a salesperson could share in a relevant conversation.

**In Cursor:** if your CRM has an MCP server, add it to `.cursor/mcp.json` inside this folder only, so the connection doesn't follow you into unrelated projects; if it doesn't, export the deals to a CSV. Cursor shows every MCP call and its arguments before it runs, so you can see exactly which records it requests. Once you trust the CRM's read tools, Cursor's Run Modes let allowlisted MCP tools run straight away while everything else still waits for your review; keep any tool that writes to the CRM off that list. Leave raw deal exports out of the commits you share, and let only anonymized briefs reach `drafts/`.

**Why it helps:** marketing starts answering the questions salespeople hear again and again, and you stop writing persuasive posts about problems your prospects don't see as important.

## 10. How do you prepare replies to LinkedIn conversations?

When someone responds to a post, the right next step depends on what they wrote and whether a business conversation would be welcome. A like or a comment on its own doesn't show business interest.

> Here are LinkedIn conversations I collected: [comments, inbound messages or interactions I logged by hand]. Group them as: 

questions that deserve a helpful public answer;
conversations where sharing a resource would help;
clear interest in our offer;
general discussion with no commercial next step.

 For each relevant conversation, sum up what the person said they need, the business context we have and what we still don't know. Draft a reply that answers their actual message before bringing up anything else. Where it fits, suggest one follow-up question or a resource we already have. Don't read business interest into a like, don't pretend we know each other, and don't push a meeting request into a conversation that doesn't call for one. Leave every reply as a draft for me to review and send.

**In Cursor:** copy the comments and messages into a dated file in `inbox/` and mention it in the chat. Cursor has no LinkedIn connection of its own, which suits this prompt: replies stay as drafts in a file next to each message, and you send them yourself. Ask the agent to link every draft to the line it answers, so you can review a week of replies in one sitting. If a thread needs more context while the agent is still working, queue a follow-up message; it gets picked up at the agent's next step without interrupting it.

**Why it helps:** preparing ahead makes your replies more thoughtful without turning every exchange into a sales sequence. Sometimes the right result is just a helpful answer.

## 11. How do you report LinkedIn results with honest attribution?

Impressions tell you how widely a post spread. They can't tell you whether the right people got interested or whether a post helped close a deal.

> Pull together my LinkedIn analytics, tracked website visits, inbound inquiry records and relevant CRM data for [period]. Report on: 

reach, reactions and comments by post, topic and format;
qualified inbound conversations;
resource sign-ups and demo requests where tracking exists;
opportunities with a documented LinkedIn contact;
time spent writing and reviewing.

 Keep directly tracked conversions, self-reported discovery and possible influence apart. Don't attribute a whole deal to LinkedIn because one contact reacted to a post. Mark posts that drew relevant prospects, posts that reached a broad but less relevant audience and posts without enough data to judge. End with three findings, three limitations and one experiment to run next. Where you can, compare posts at the same age so new posts aren't judged against older ones.

**In Cursor:** drop the analytics exports into `reports/data/` and ask for the result as a Canvas, the live dashboard Cursor opens beside the chat. A Canvas can't fetch data from the internet, so every number on it comes from files you supplied, which keeps the attribution traceable. The Canvas is saved as a `.canvas.tsx` file, so it sits in the folder next to its data and can be committed with the month's report. Save the written findings to a dated Markdown file as well, so next month's run has something to compare with. If your team works in Slack, a cloud agent started from a Slack message can build the same report from the committed exports, and nobody has to open the editor. For a quick reach check on one post, use the [LinkedIn impressions calculator](https://linkedinlikes.com/linkedin-impressions-calculator).

**Why it helps:** better reporting gives you a reason to repeat or change an approach, and it keeps uncertainty visible instead of letting patchy tracking become a confident revenue claim.

## 12. How do you turn your edits into a better system?

Each draft you edit records something about your standards. Write down the lessons that keep coming back and you spend less time fixing the same problems.

> Set the original drafts, my edits, the published versions and the performance data from [period] side by side. Find: 

voice corrections I keep making;
claims that needed more evidence;
ideas that got clearer once the framing changed;
formatting changes that made posts easier to read;
topics that started helpful conversations with prospects.

 Suggest updates to the voice guide, the research briefs and the editorial checklist. For each new rule, show an example, say what it is for and when an exception makes sense. Keep style preferences apart from performance guesses. Never turn a single hit or miss into a permanent rule. Give me a short change log to approve and suggest one controlled experiment for next month.

**In Cursor:** this is where committing `drafts/` and `published/` pays off. Mention `@Git` so the agent can read the commit history, or ask it to diff each draft against its published version. Accepted changes go into `voice-guide.mdc` and the review skill, never only into the chat, because Cursor won't remember them anywhere else. Approve each edit in the diff view before it lands, and ask for the change log as its own file, so in three months you can still see why a rule exists. Some lessons are better enforced than remembered: a hook in `.cursor/hooks.json` on the `afterFileEdit` event can run a small script whenever the agent changes a draft, such as a check that the length stays inside your range.

**Why it helps:** the workflow gets easier to run over time. Your corrections become written guidance, and performance ideas stay testable instead of hardening into superstition.

## How should you roll out all 12 prompts?

Add the workflow in stages, so you can inspect each output before another recurring job joins it.

- **Week 1, groundwork:** load your business context, run prompt 1 and check the voice guide against writing you would really publish.
- **Week 2, research backlog:** run prompts 2 to 4 on a manageable set of customer conversations, market sources and LinkedIn examples you supply.
- **Week 3, first batch:** use prompts 5 to 7 to turn approved material into a calendar and a handful of drafts.
- **Week 4, review:** run prompt 8, publish the posts you approve, work the first hour on each, and note the changes you make.
- **Weeks 5 and 6, conversations:** add prompts 9 and 10 once you have enough sales context and inbound activity for them to be useful.
- **Every month after that:** use prompts 11 and 12 to review results, improve the instructions and choose the next experiment.

For every recurring job, write down its inputs, where the output goes, how often it runs and who owns it. Test one run before you rely on the schedule, and have Cursor report missing access or unfinished work.

Cursor gives recurring work its own home. Automations, added in March 2026, are always-on agents that start on a schedule or on an event such as a Slack message or a GitHub event, run in a cloud sandbox and use the MCP servers and models you set up. With memory switched on, an Automation keeps a small notepad between runs, so the monthly report from prompt 11 can note what changed since the last one. Cloud runs start from your repository, so commit the analytics exports they need; a file that exists only on your laptop is invisible to them. The same applies to connected servers: one reachable only from your machine works in the desktop app and fails in the cloud, while one with a public URL can work in both.

Cloud agents handle one-off jobs. You can start one from the editor, the web, Slack, Linear or a GitHub comment; it works on an isolated machine on a branch of your repository and pushes the result back for you to review. The Cursor CLI runs the same agent in a terminal with the same rules and server setup, which helps if you want a script on a server in place of a window on your desktop.

You don't need 12 separate agents on day one. Start with the job that takes the most time and produces something you can judge reliably.

## What can Cursor do for your LinkedIn, and what can't it?

Cursor can organize research, find the right evidence, prepare drafts and learn from your edits. Its real edge is that every file you approve becomes context for the next draft. The quality of the posts still comes from the experience and judgment you put into the system.

That means holding a real opinion, being ready to explain it, and noticing when a polished draft says something you would never defend face to face.

When a post from this workflow goes live, add it to [LinkedInLikes](https://linkedinlikes.com/linkedin/comment) for its first reactions. For the full setup from an empty folder, read [how to build an AI content system for LinkedIn](https://linkedinlikes.com/blog/how-to-build-an-ai-content-system-for-linkedin). Otherwise, load your context and run prompt 1: better inputs make every later step more useful.

## Frequently asked questions

### What does LinkedIn maxxing with Cursor mean?

Using Cursor's agent on a folder of your posts, transcripts and rules so it researches topics, drafts posts in your voice, reviews them against a rubric and reports on results, with your standards stored in files.

### Does Cursor remember my LinkedIn voice between chats?

Only through files. Cursor removed automatic Memories in version 2.1, so save the context as a Project Rule in `.cursor/rules/` with the `.mdc` extension and `alwaysApply: true`.

### Can Cursor post to LinkedIn for me?

Cursor has no LinkedIn connection of its own. It writes drafts and reply suggestions into files, and you publish and send them yourself.

### Can Cursor read posts on X?

X access isn't built in. `@Web` reaches public pages, articles and newsletters, so paste posts from accounts you follow into a notes file. If you have an X search tool connected to Cursor, the agent can use it too.

### What is the difference between a Cursor rule and a skill?

A rule is loaded in full into every chat it matches. A skill in `.cursor/skills/{name}/SKILL.md` loads only when a request matches its description, which suits long procedures like an editorial rubric.

### How do I get early reactions on posts written with Cursor?

Send the link to colleagues, friends and clients as soon as the post is live, and add it to [LinkedInLikes](https://linkedinlikes.com/linkedin/like), where members react and comment by hand from their own accounts.