If you give AI a topic and ask it to write a LinkedIn post that leaves out the work that makes the post worth reading: understanding your buyers, finding an interesting angle, pulling together evidence, and knowing what you can credibly say.
These 12 prompts turn that work into a repeatable system, from researching your market to building a content calendar and identifying which posts contribute to real business conversations.
Here's what you'll build:
- A voice guide based on how you actually communicate.
- A research process that finds useful ideas before you need them.
- A draft and review workflow with specific quality standards.
- A reporting loop that helps you decide what to write next.
Cursor is a code editor, but its agent reads and writes any file in the folder you open, so a writing folder with your posts, transcripts and rules becomes a content desk that remembers your standards in files rather than in chat.
Save this for your next content planning session.
## Before You Run a Single Prompt: Load Your Business Context
Cursor needs more than your job title and a request to "sound human." Give it enough context to understand who you sell to, what you believe, and which claims you can support.
Paste this first:
> Use the following as the working context for my LinkedIn content. Save it in a reference document I can reuse, and tell me where it is stored.
> Business: [Company name, website, product or service, and typical sales cycle].
> Audience: [Buyer roles, company sizes, industries, and locations]. Their main problems are [problems], their common objections are [objections], and the events that make them look for help are [triggers].
> Positioning: We help [audience] achieve [outcome] through [approach]. Our strongest differences are [differences]. We are not a good fit for [disqualifiers].
> Point of view: I believe [three opinions about my industry]. I disagree with [common advice] because [reason].
> Evidence: Use these approved case studies, results, examples, and personal experiences: [sources]. Do not invent customers, revenue figures, stories, or quotations.
> Voice: Here are [10-20 posts] and [transcripts or writing samples] that sound like me. Avoid [phrases, tones, and formatting habits].
> Goals: My primary LinkedIn objective is [qualified conversations / deal support / recruiting / awareness]. My offer is [offer], and the next step I want interested readers to take is [action].
> Working rules: Use connected sources only where access is available and authorized. If a source is inaccessible, ask for an export or pasted material. Keep LinkedIn publishing, comments, connection requests, and messages with me. Draft into [review destination], distinguish evidence from inference, and flag anything that needs my input.
Keep that context current as your offer, audience, and opinions change.
### Where Cursor keeps this context
Cursor removed its automatic Memories in version 2.1, and a new chat does not read your earlier chats. What carries over is what you write into files, so give the context a file Cursor loads on its own.
Open one folder for all LinkedIn work, for example `linkedin/` with `context/`, `drafts/`, `published/`, `research/` and `reports/` inside. Then ask the agent to store the answers above as a Project Rule:
```
.cursor/rules/business-context.mdc
---
description: Business context for all LinkedIn content
alwaysApply: true
---
(the business, audience, positioning, evidence and goals from the prompt)
```
`alwaysApply: true` puts it into every Agent chat in this folder. The extension matters: a plain `.md` file inside `.cursor/rules` is ignored. Other rules can apply when a matching file is in context (`globs`), when the agent judges the description relevant, or only when you @-mention them, business context is the one that should always be on. Keep each rule short, well under the 500 lines Cursor recommends, and split the voice guide into its own rule later.
Two other places are worth knowing. `AGENTS.md` at the folder root is plain Markdown with no frontmatter, and other agents read it too, so use it if you switch tools. User Rules, under Customize -> Rules, follow you into every project, they suit personal habits such as spelling or tone, not business facts. On Teams and Enterprise plans, an admin can also set Team Rules from the dashboard, which is how a marketing team keeps one set of approved claims and phrases to avoid for everyone. Commit the folder to git: Cursor's cloud agents and Automations start from the repository, not from your laptop.
Longer procedures, such as the editorial rubric in prompt 8, fit better as skills in `.cursor/skills/{name}/SKILL.md`. A rule is loaded whole into every matching chat, a skill loads only when the request matches its description, which keeps everyday chats light.
By the end of this article the folder looks roughly like this:
```
linkedin/
AGENTS.md shared instructions, if you use other agents too
.cursor/
rules/
business-context.mdc alwaysApply: true
voice-guide.mdc globs: drafts/**
skills/editorial-review/SKILL.md
mcp.json project-only MCP servers
context/ samples, calls, exports
research/ buyer questions, X notes, outliers
calendar/ one brief per post
drafts/ reviews/ published/ inbox/ reports/
```
## PART 1: RESEARCH + CONTENT PRODUCTION
Prompts 1-8
## 1. Build Your Voice Guide
A useful voice guide should explain the choices behind your writing, including how you introduce an argument, use examples, and move between ideas.
"Conversational and authoritative" won't get you very far.
> Analyze my supplied posts and transcripts to build a practical LinkedIn voice guide.
> Identify:
> How I open posts, explain ideas, and close an argument.
> My usual vocabulary, sentence lengths, paragraph lengths, and use of lists.
> How I express disagreement, uncertainty, humor, and confidence.
> Phrases I use naturally, with verbatim examples and source references.
> Writing habits that appear in weaker examples but should not become rules.
> Create a list of phrases and patterns to avoid, including unnecessary repetition, manufactured drama, unsupported certainty, and excessive one-line paragraphs.
> Finish with three before-and-after examples showing how you would edit generic copy into my voice without changing its meaning. Keep the guide specific enough that another writer could apply it.
**In Cursor:** put your 10-20 best posts and a few transcripts in `context/samples/` and reference 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 instead of crowding every chat. When you rework the before-and-after examples yourself, edit inside the file: Tab predicts your next edit from the changes you just made, which speeds up rewriting. Rules reach the Agent only, not Tab, so Tab will not enforce the guide for you, the review in prompt 8 does that job.
**Why this matters:** A good voice guide makes feedback reusable. Instead of correcting the same phrasing every week, you can improve the instructions that shape every draft.
## 2. Find the Buyer Questions Worth Answering
Your sales calls and customer conversations can reveal questions that never show up in a keyword tool. The useful ones usually involve a decision someone is struggling to make.
> Review the supplied sales calls, support conversations, customer interviews, and closed-lost notes from [period].
> Group recurring questions into:
> Problems buyers are trying to understand.
> Concerns that delay a decision.
> Alternatives they are comparing.
> Expectations that lead to disappointment.
> Questions they ask right before they decide.
> For each theme, include an anonymized quotation, source reference, number of distinct conversations where it appears, buyer role, and stage in their decision.
> Suggest two LinkedIn angles per theme: one that helps the reader diagnose the problem and one that helps them evaluate a solution. Rank the top 10 by recurrence, commercial relevance, and the strength of our available evidence.
> Separate explicit buyer statements from your interpretation.
**In Cursor:** export the calls and support threads as text into `context/calls/`, and drag a CSV of closed-lost notes straight into the chat. Run the first pass in Ask mode, which reads and answers without touching files, to check that the themes make sense. Then switch to Agent and have it write `research/buyer-questions.md`, with the file path of every quotation next to it so you can open the source in one click. Ask mode is also a quick way to get oriented in a messy export folder: ask which files are transcripts, which are notes and which are duplicates before the analysis starts. Later, `@Codebase` runs a semantic search across the whole folder, which helps when a question you are tracking also turns up in notes filed somewhere else.
**Why this matters:** This gives you a content backlog tied to conversations your business is already having, with language you can recognize and evidence you can check.
## 3. Find Topics Moving From X Into Your Market
A discussion gaining attention on X can become a useful LinkedIn post, provided you explain why it matters to your buyers and contribute something beyond a rewrite.
> Using @Web search, accessible public sources, and any X search tool connected over MCP, review the last [seven days] of discussion around [topics] and these accounts: [accounts].
> Find up to 10 developments or arguments relevant to [buyer].
> For each, return:
> The original source, author, publication date, and link.
> A neutral summary of what happened or what is being argued.
> The strongest competing interpretations.
> The practical implication for our audience.
> A possible angle supported by our experience or evidence.
> Label engagement figures as a snapshot, avoid treating popularity as proof, and check factual claims against original sources where possible.
> Recommend three ideas worth developing, explaining what I would need to contribute before each becomes a credible LinkedIn post.
**In Cursor:** there is no built-in X access. The agent searches the open web (`@Web` in chat), which reaches articles, newsletters and public pages, but not posts behind a login. For the accounts you follow closely, paste their recent posts into `research/x-notes.md` and mention that file alongside `@Web`. End the prompt with "list every URL you opened", so you can check the snapshot before you trust it. If you watch the same accounts every week, keep the list in `research/sources.md` and mention it instead of retyping it. This is also a sensible first Automation: on a weekly schedule, a cloud agent can run the prompt against that sources file and leave the result on a branch, ready for you to read on Monday morning.
**Why this matters:** Research gives you a starting point, your judgment gives readers a reason to follow you. A useful adaptation connects the discussion to a decision they actually face.
## 4. Analyze Posts That Outperform Their Authors' Usual Results
Comparing every post with a celebrity account's biggest hit will distort your research. Compare a post with that author's typical results first, then examine what might explain the difference.
> Analyze this supplied LinkedIn swipe file and the available performance data: [file].
> Where enough comparable data exists, compare each post's engagement with the author's median for a similar format and period. Show the calculation and flag small samples, missing metrics, and unusually recent posts.
> For the strongest outliers, identify:
> The topic and intended reader.
> The opening, argument, supporting evidence, and closing.
> The format and approximate length.
> Whether the discussion suggests genuine buyer relevance.
> Possible confounding factors, including a launch, news event, or unusually large distribution.
> Produce five reusable structural patterns, then propose an original idea for each using my own expertise. Do not copy distinctive wording or present an observed pattern as an algorithm rule.
**In Cursor:** keep the swipe file as a CSV in `research/` and ask the agent to calculate the medians with a short script it runs in the terminal, saving the script next to the output. That turns "show the calculation" into something you can rerun next month. To check one of your own posts against your usual numbers, the [LinkedIn engagement rate calculator](/tools/linkedin/engagement-rate-calculator) gives you the baseline without a spreadsheet. When 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 this matters:** You get a more useful set of hypotheses to test, while avoiding the assumption that copying a successful post's layout will reproduce its results.
## 5. Turn Recorded Expertise Into Original Posts
A transcript can contain a strong post buried inside a story, an offhand explanation, or a disagreement that deserves more attention than it received in the original conversation.
> Review this interview, podcast, webinar, or meeting transcript: [source].
> Find eight moments that could support an original LinkedIn post. Prioritize decisions, mistakes, surprising observations, practical explanations, and opinions backed by experience.
> For each moment, provide:
> The exact quotation and timestamp or source location.
> The main idea and who would benefit from it.
> Any context needed to avoid misrepresenting the speaker.
> Supporting facts already available.
> One follow-up question that would make the idea stronger.
> Select the three strongest moments and draft a post from each using my voice guide. Preserve the speaker's level of certainty, remove confidential details, and leave placeholders wherever a result or example needs confirmation.
**In Cursor:** save the transcript as a text file and mention it with `@`. Webinar slides can come along as images: the agent reads images as well as text, so a chart the speaker talked over can be quoted correctly. Split the prompt in two: first the table of eight moments in `research/moments.md`, then, after you have crossed out the weak ones, the three drafts in `drafts/`. Writing into `drafts/` is what brings the scoped voice-guide rule into play, mention `@voice-guide.mdc` as well if you want to be certain it is applied. Cursor takes a checkpoint before significant changes, so if the drafts overwrite something you liked, restoring it brings the files back, it does not rewind the conversation.
**Why this matters:** You can produce content from expertise that already exists without asking someone to manufacture a fresh opinion every time the calendar needs filling.
## 6. Build a Four-Week Content Calendar
A calendar should balance what buyers need to understand with what you have enough evidence to say. Filling every slot with a different version of your offer makes the feed repetitive.
> Build a four-week LinkedIn calendar at [three posts per week] using the approved ideas from our research.
> Include a mix of:
> Practical explanations that help buyers solve a problem.
> Opinions supported by experience or evidence.
> Customer examples we have permission to share.
> Answers to objections and decision questions.
> Occasional offer posts with a relevant next step.
> For each post, specify the reader, single objective, central argument, source material, suggested format, and intended reader action.
> Check for repeated topics, overlapping arguments, unsupported claims, and too many promotional posts. Write full briefs for week one and shorter outlines for weeks two through four.
> Leave room for one timely replacement each week rather than assuming the calendar must remain fixed.
**In Cursor:** start this one in Plan mode. Plan writes out what it intends to do and waits for your approval, which is exactly the moment to fix the mix of post types before twelve briefs land in `calendar/`. Set the slots with the [best time to post on LinkedIn](/tools/linkedin/best-time-to-post) guide, and for every brief marked as a carousel, lay out the slides in the [LinkedIn carousel maker](/tools/linkedin/carousel-maker). Ask for one file per brief: swapping in the weekly timely replacement then means editing one file, not regenerating the month. If you work 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 this matters:** A useful calendar reduces weekly planning work while leaving room for better ideas, new evidence, and changes in the market.
## 7. Draft Posts With Something to Say
The research should determine the argument before Cursor starts polishing sentences. Otherwise, you can end up with clean writing that takes several paragraphs to make an obvious point.
> Write three LinkedIn drafts from these approved briefs: [briefs].
> Use my business context and voice guide. For each draft:
> Make one clear argument for one intended reader.
> Open with a specific observation, tension, result, or question supported by the brief.
> Explain the reasoning with a concrete example.
> Use bullets where they make parallel points easier to scan.
> Vary sentence and paragraph length, allowing occasional one-line emphasis.
> End with a useful implication or natural next step.
> Aim for [180-300 words] unless the idea warrants a different length. Avoid repeated conclusions, invented personal stories, exaggerated promises, and formulaic contrasts.
> Supply two alternative openings and explain briefly how they change the emphasis. Flag missing evidence separately 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 it stays clean. If you want genuinely different takes on the same brief, `/best-of-n` runs the request in parallel isolated worktrees and lets you compare the results side by side. Put the openings you like through the [LinkedIn hook generator](/tools/linkedin/hook-generator) for a few more variations before you choose.
**Why this matters:** Clear constraints give you drafts that are easier to judge. Your review can focus on the quality of the argument instead of repairing unnecessary filler.
## 8. Create a Separate Editorial Review
Give the reviewer a defined job: find weaknesses in the draft and explain how to fix them. A reassuring "This is strong" is not an editorial process.
> Review these LinkedIn drafts against the brief, source material, business context, and voice guide.
> Score each out of 100 using:
> Buyer relevance: 25.
> Specificity and supporting evidence: 25.
> Strength and clarity of the argument: 20.
> Match with my voice: 20.
> Readability and formatting: 10.
> Show the evidence behind each score. Check every factual claim, identify generic passages, and highlight repetitions, abrupt one-line fragments, and paragraphs that would work better as lists.
> Return a publishable revision, a short explanation of meaningful changes, and any unresolved questions. Treat invented evidence, exposed confidential information, or a misrepresented source as a blocking issue regardless of score.
> Keep everything in the review document for human approval.
**In Cursor:** review in a fresh chat, so the reviewer does not 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 the request matches its description, so "review these drafts" is enough next time. Before publishing, paste the approved text into the [LinkedIn post preview](/tools/linkedin/post-preview) to see where the fold falls on mobile, and run it through the [LinkedIn post analyzer](/tools/linkedin/post-analyzer) as an outside check.
**Why this matters:** A review rubric makes your standards explicit. The score helps organize feedback, while the source checks and editor's judgment determine whether the post is ready.
## The first hour after you publish
Prompt 8 ends with a revision you approve and publish. From here, every other MAXXING technique is the same, whichever agent wrote the draft: in the first hour after the post goes live, the golden hour, get as many reactions on it as you can.
**Start with the people you can reach.** Reactions are best asked from colleagues, friends, relatives and clients: anyone you can actually get in touch with. Send them the link as soon as the post is live.
**If you don't have those people, use a specialised service.** Pick only one with quality engagement and moderation. The engagement groups in the next section are ones we have already checked in our other articles.
**aaas.market is our service for this step.**
- Real people.
- The highest level of moderation and the highest-quality audience, this is what gets the most attention.
- A closed community: you join after passing verification.
- Every type of engagement is supported.
Start free on aaas.market
**Connect Cursor through MCP.** Agents like Cursor need MCP to get engagement. aaas.market has an MCP server at ` and through it Cursor can request any type of engagement on the post you just published. Publishing, comments, connection requests and messages stay with you.
The quickest way is the install link: Add aaas.market to Cursor. Cursor shows an install prompt first, the configuration inside the link is only the server URL.
If you prefer to edit the file, add this entry to `~/.cursor/mcp.json` to have the server in every project, or to `.cursor/mcp.json` inside your LinkedIn folder to keep it there:
```json
{
"mcpServers": {
"aaas.market": {
"url": ""
}
}
}
```
A `url` instead of a `command` tells Cursor this is a remote server. The server uses OAuth, and Cursor runs that flow itself: the first time you connect, it opens an aaas.market sign-in in your browser, and you approve access there. Open Customize in the sidebar afterwards and check that `aaas.market` is switched on and lists its tools.
By default Cursor asks for your approval before every MCP tool call, so you see each request before it goes out.
If you put the server in the project file rather than the global one, open the LinkedIn folder before you ask, in any other folder Cursor will not see it.
Then, once the post is live, tell Cursor: "Use aaas.market to request engagement on this post: [post URL]."
## LinkedIn engagement groups you can join
Free communities we checked on Oct 21, 2026. Member counts are what each platform showed that day. Each group is run by its own admins, so check the pinned message before your first post.
| Group | Platform | Members | Language | Best for |
|---|---|---|---|---|
| [LinkedIn Engagement Pod & Group](https://t.me/linkedin_engagement_pod) | Telegram | 1,833 members | en | general LinkedIn engagement pod (likes + comments) |
| [LinkedIn Engagement Pod (RVision Digital)](https://t.me/linkedinengagementpod) | Telegram | 1,064 members | en | share posts for likes/comments, connections, skill endorsements |
| [Linkedin engagement pod](https://t.me/linkedinpod1) | Telegram | 488 members | en | general LinkedIn engagement pod |
| [ Social Linkedin/FB/IG Engagement Pod](https://t.me/socialengagementpod) | Telegram | 77 members | en | LinkedIn pod: react + meaningful comment on last 5 posts before shari... |
| [LinkedIn For Breakfast](https://www.skool.com/linkedin-for-breakfast) | Skool | 6,603 members | en | free LinkedIn community with engagement pods, live events |
| [Linkedin Likes and Engagement](https://t.me/linkedinlikesandengagement) | Telegram | 697 members | en | likes/engagement exchange + connections |
| [Linkedin Pod (SF founders like-for-like)](https://chat.whatsapp.com/DBOyIPrDR5IK27yzW606sF) | WhatsApp | n/a (WhatsApp hides count) | en | founders like-for-like on LinkedIn |
| [r/linkedintips](https://www.reddit.com/r/linkedintips/) | Reddit | 38,314 members | en | LinkedIn tips for promoting your brand |
| [LinkedIn Club](https://t.me/linkedinclub) | Telegram | 552 members | en | LinkedIn networking/connections community |
| [r/LinkedInGrowth](https://www.reddit.com/r/LinkedInGrowth/) | Reddit | - | en | LinkedIn growth and connections |
Want real engagement on your own posts without searching through groups? Start free on aaas.market.
## PART 2: PIPELINE + PERFORMANCE
Prompts 9-12
## 9. Build Content Around Stalled Deals
Some of your most useful content may answer a concern that several prospects have raised privately. The goal is to explain the issue well enough that future buyers arrive better informed.
> Review the supplied CRM records and call notes for qualified deals that stalled during [period].
> Identify recurring objections and distinguish:
> Explicit objections recorded in conversations.
> Possible explanations that remain unconfirmed.
> Operational delays that content is unlikely to solve.
> For each repeated objection, record the buyer role, deal stage, available evidence, and the question a useful LinkedIn post should answer.
> Recommend five content briefs. Each should explain the concern fairly, show where our approach is appropriate, acknowledge where it is not, and identify the proof required.
> Draft the two strongest posts without naming prospects or revealing deal details. Separately, suggest which approved existing resources a salesperson could share in a relevant conversation.
**In Cursor:** if your CRM offers an MCP server, add it to `.cursor/mcp.json` inside this folder only, so the connection does not follow you into unrelated projects, otherwise export the stalled deals to a CSV. Because Cursor shows each MCP call and its arguments before running it, you can see exactly which records it asks for. Once you trust the CRM's read tools, Cursor's Run Modes let allowlisted MCP tools run straight away while everything else still goes through the usual review, keep any tool that writes to the CRM off that list. Keep raw deal exports out of the commits you share, and let only the anonymized briefs reach `drafts/`.
**Why this matters:** This connects marketing with the questions salespeople repeatedly need to answer. It also helps you avoid writing persuasive content about problems your buyers do not consider important.
## 10. Prepare Relevant Conversation Briefs
When someone responds to a post, the best next step depends on what they said and whether a business conversation would be welcome. Engagement alone does not show real business interest.
> Review these LinkedIn conversations I have supplied: [comments, inbound messages, or manually recorded interactions].
> Group them into:
> Questions that deserve a useful public answer.
> Conversations where sharing a resource would help.
> Explicit interest in our offer.
> General discussion with no commercial next step.
> For each relevant conversation, summarize the person's stated need, available business context, and what remains unknown.
> Draft a response that addresses their actual message before introducing anything else. Where appropriate, suggest one follow-up question or a resource we already have.
> Do not infer business interest from a like, invent familiarity, or force a meeting request into a conversation that does not support one. Keep all replies as drafts for me to review and send.
**In Cursor:** copy the comments and messages into a dated file in `inbox/` and mention that file 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. Ask it to link each draft to the line it answers, so you can review a week of replies in one pass. If a thread needs more context while the agent is still working, you can queue a follow-up message, it is picked up at the agent's next step instead of interrupting it.
**Why this matters:** Preparation can make your responses more thoughtful without turning every interaction into a sales sequence. Sometimes the right outcome is simply a useful answer.
## 11. Report on Business Results With Clear Attribution
Impressions help you understand distribution, but they cannot tell you whether the right people became interested or whether a post contributed to a deal.
> Combine my supplied LinkedIn analytics, tracked website visits, inbound inquiry records, and relevant CRM data for [period].
> Report on:
> Reach and engagement by post, topic, and format.
> Qualified inbound conversations.
> Resource sign-ups and demo requests where tracking exists.
> Opportunities with documented LinkedIn touchpoints.
> Content production time and review effort.
> Separate directly tracked conversions, self-reported discovery, and possible influence. Do not attribute an entire deal to LinkedIn because one contact engaged with a post.
> Highlight posts that attracted relevant buyers, posts that reached a broad but less relevant audience, and posts with insufficient data to judge.
> Finish with three findings, three limitations, and one recommended experiment. If possible, assess posts at consistent ages so newly published content is not unfairly compared with older posts.
**In Cursor:** drop the analytics exports into `reports/data/` and ask for the result as a Canvas, the live dashboard Cursor opens next to the chat. A Canvas cannot pull data from the internet, so every number on it comes from the files you supplied, which keeps the attribution traceable. The Canvas itself 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 against. If your team lives in Slack, a cloud agent started from a Slack message can produce the same report from the committed exports, without anyone opening the editor. For a quick reach check on a single post, use the [LinkedIn impressions calculator](/tools/linkedin/impressions-calculator).
**Why this matters:** Better reporting gives you a reason to repeat or change an approach. It also makes uncertainty visible instead of turning incomplete tracking into a confident revenue claim.
## 12. Turn Feedback Into a Better System
Every edited draft contains information about your standards. Capture the recurring lessons so you spend less time correcting familiar problems.
> Compare the original drafts, my edits, final published versions, and available performance data from [period].
> Identify:
> Voice corrections I have made repeatedly.
> Claims that needed more evidence.
> Ideas that became clearer after a change in framing.
> Formatting changes that improved readability.
> Topics that generated useful buyer conversations.
> Propose updates to the voice guide, research briefs, and editorial checklist. For every proposed rule, show an example, explain its purpose, and note when an exception may be appropriate.
> Keep stylistic preferences separate from performance hypotheses. Do not create a permanent rule from one successful or unsuccessful post.
> Return a short change log for my approval and suggest one controlled experiment for the 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 just into the chat, because Cursor will not remember them otherwise. Approve each edit in the diff view before it lands, and ask for the change log as its own file so you can see in three months 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 every time the agent changes a draft, for example a check that the length stays inside your range.
**Why this matters:** This is how the workflow becomes easier to manage over time. Your corrections become usable guidance, while performance ideas remain testable rather than hardening into superstition.
## How to Use All 12 Prompts
Build the workflow in stages so you can inspect the output before adding another recurring job.
Week 1: Establish the foundation. Load your business context, run prompt 1, and check the voice guide against writing you would actually publish.
Week 2: Build your research backlog. Run prompts 2-4 using a manageable set of customer conversations, market sources, and supplied LinkedIn examples.
Week 3: Produce the first batch. Use prompts 5-7 to turn approved material into a calendar and a small number of drafts.
Week 4: Strengthen the review process. Run prompt 8, publish the posts you approve, work the first hour on each one, and record the changes you make.
Weeks 5-6: Connect content with conversations. Add prompts 9 and 10 where you have enough sales context and inbound activity to make them useful.
Then repeat monthly: Use prompts 11 and 12 to review results, improve the instructions, and choose the next experiment.
For any recurring job, define its inputs, destination, frequency, and owner. Test a run before relying on the schedule, and have Cursor report missing access or incomplete work.
In Cursor, recurring work has a home of its own. 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 configured. With memory turned on, an Automation keeps a small notepad between runs, so the monthly report from prompt 11 can note what changed since last time. 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 goes for MCP: a server reachable only from your machine works in the desktop app but not in the cloud, while a public URL like aaas.market's can work in both.
Cloud agents cover the one-off jobs: you can start one from the editor, the web, Slack, Linear or a GitHub comment, it works in 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 MCP configuration, which helps if you want a script on a server rather than a window on your desktop.
For the posting rhythm itself, the [free social media scheduler](/tools/free-tools/free-social-media-scheduler) reminds you by email or Telegram when a post from the calendar is due, and you publish it yourself.
You do not need 12 separate agents on day one.
Start with the job that consumes the most time and produces an output you can reliably evaluate.
## The Real Talk
Cursor can help you organize research, retrieve useful evidence, prepare drafts, and learn from your edits. Its real advantage is that every file you approve becomes context for the next draft. The quality still depends on the experience and judgment you bring into the system.
That means having a real opinion, being willing to explain it, and noticing when a polished draft says something you would never defend in a conversation.
If you want the first engagement on the posts this workflow produces, start free on aaas.market.
Otherwise, load your context and run prompt 1. Improving the inputs will make every step after it more useful.