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.
Muse suits this kind of system because it is an agent with its own cloud computer: it remembers what you tell it, keeps files and Artifacts between conversations, and keeps working after you close the app.
Save this for your next content planning session.
## Before You Run a Single Prompt: Load Your Business Context
Muse 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.
**In Muse:** send this as the first message of a fresh chat in the Muse app or at muse.ai, and attach your sample posts and transcripts as files rather than pasting them inline. For [review destination], name a document Artifact, for example "LinkedIn review doc". Muse can create documents, trackers, and dashboards as Artifacts and keep them on its VM, so every later prompt can write into the same file.
Keep that context current as your offer, audience, and opinions change.
### Where Muse keeps this context
Muse keeps context in three places, and it is worth using all three:
- **Memory.** Muse remembers details you mention and uses them later. You can open its memory files, correct them, and tell it to forget anything stale. Check them after the first week: a wrong or overshared memory will quietly shape every draft.
- **A file on its VM.** Files, Artifacts, and memory all live on your own Muse Secure VM. Ask Muse to save the context as a document named "LinkedIn context" and to read it before every content request.
- **A skill.** A skill is a set of saved instructions Muse loads when a request matches. Ask it to save your working rules as a skill called "LinkedIn content": read the context file first, draft only, never publish, flag missing evidence. That way the rules apply in new chats without pasting them again.
To set up the skill, send one message after the context is saved:
> Save my LinkedIn working rules as a skill called "LinkedIn content". Before any LinkedIn request, read the "LinkedIn context" and "Voice guide" documents. Draft only. Never publish, comment, connect, or message on my behalf. Mark every claim that has no source in my files.
Here is what goes where once the series is running:
| What | Where in Muse |
|---|---|
| Business context, offer, evidence list | "LinkedIn context" document on the VM |
| Tone, phrases to avoid, before-and-after edits | "Voice guide" document, read by the skill |
| Durable personal facts (role, audience, opinions) | Memory, reviewed monthly |
| Working rules and the stop rule | "LinkedIn content" skill |
| Drafts and review scores | "LinkedIn review doc" Artifact |
| The four-week plan | A goal in the Goals view |
Use a side chat per job (research, drafting, review, reporting) so one long conversation does not blur the context. If you work in Muse Code, the terminal agent, the equivalent is an `AGENTS.md` file at the project root (`muse init` creates one) plus Markdown notes under `.agents/memory/` with a `MEMORY.md` index.
## 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 Muse:** Muse has no LinkedIn connector, so it cannot read your profile. Export or copy your posts into a file and upload it, if your transcripts sit in Google Drive, turn on the Google Workspace connector and name the folder. Real examples steer the model better than adjectives, so include your best posts, not only your latest. Add a short ban list of your own: the openers, sign-offs, and filler phrases you never use. Muse, like any model, drifts back to them unless they are named. Ask Muse to save the result as a "Voice guide" document and add it to the LinkedIn content skill.
**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 Muse:** if customer threads live in Gmail, connect Gmail with read-only access, Muse lets you choose read or send per connector, and this prompt only needs read. Upload call transcripts and closed-lost notes as files. Ask for the output as a tracker Artifact with one row per theme, so prompt 6 can pull from it later. Rerun it monthly on new conversations and ask Muse to update the counts in the same tracker rather than start a new one.
**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 your browser and accessible public sources on X and the web, 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 Muse:** there is no X connector in Muse's built-in list, so this runs through its browser sub-agent on public pages. You can watch it in the browser view and take over if a page needs a login. Browsing is slower than a direct API call, so keep the account list short. Threads is connected through Accounts Center, which makes it a useful second source. Ask Muse to put every link it opened next to each summary, so you can check a source in one click before you build a post on it. If you want this every week, add "Run this every Monday morning and update the same research document."
**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 Muse:** upload the swipe file as a spreadsheet. Muse can write and run code on its VM, so ask it to compute the medians in code and return a spreadsheet Artifact with the formulas visible, not just a summary. To check your own posts against the same baseline, run their numbers through our [LinkedIn engagement rate calculator](/tools/linkedin/engagement-rate-calculator).
**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 Muse:** upload the transcript file, or give Muse a link to a public recording page. Long recordings can take a while, Muse keeps going in the background and messages you when the moments are ready. Ask for timestamps in mm:ss so you can jump to each moment and hear the tone yourself. Ask it to keep the three drafts in the LinkedIn review doc, not in the chat, so they reach prompt 8 intact.
**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 Muse:** give this to Muse as a goal, not a one-off request: "Keep this calendar as my LinkedIn goal for the next four weeks." The Goals view then shows the plan and where it stands. With Google Calendar connected, Muse can block writing time on the days before each slot, keep calendar changes on approval so nothing moves without you. To keep the review load small, add: "Write the first brief for week one, then stop and wait for my approval before the next." Pick the slots with our [best time to post on LinkedIn](/tools/linkedin/best-time-to-post) guide, and when a brief calls for a carousel, build it in the [LinkedIn carousel maker](/tools/linkedin/carousel-maker).
**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 Muse 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 Muse:** Muse is built to keep going, so end the message with a stop: "Do not post anything and do not open LinkedIn. Leave the drafts in the review doc and wait for me." Rewrite the first line in your own words before anything else, to test more openings than the two alternatives, try our [LinkedIn hook generator](/tools/linkedin/hook-generator).
**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 Muse:** run the review in a side chat, not in the drafting conversation, so the reviewer judges the drafts against the files instead of defending its own choices. Name the context file, the voice guide, and the review doc it should check against, and ask for the scores as a table at the top of the review doc so you can compare drafts at a glance. Before you publish, paste the approved version into our [LinkedIn post analyzer](/tools/linkedin/post-analyzer) for a last check on length and structure.
**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 you publish it on LinkedIn yourself. From here, every other MAXXING technique works the same way: in the first hour after the post goes live, the golden hour, get as many reactions on it as you can.
**Ask people you can reach first.** Colleagues, friends, family, clients: anyone you can actually get hold of. Send each of them the link as soon as the post is live and ask them to react, and to comment if they have something to add.
**No such people yet? Use a specialized service, but only one with quality engagement and moderation.** The engagement groups in the next section are the ones we have checked and already list in our other articles.
**aaas.market is our service for this step.** Real people. The strictest moderation and the highest-quality audience, because that is where we put the most attention. A closed community you join only after verification. It supports every type of engagement.
**Muse needs MCP for this.** To get engagement, an agent needs MCP. aaas.market runs an MCP server at ` and through it Muse can request any type of engagement on a post you have already published. Publishing, comments, connection requests, and messages stay with you.
Muse has no "add MCP server" form. You set the connection up in a chat, and Muse builds it as a custom connector on its Secure VM:
1. Open a new chat in the Muse app or at muse.ai and send: "Create a custom connector for aaas.market. It is a remote MCP server over streamable HTTP at Connect to it, list its tools, and save the integration as a reusable skill. If it needs a sign-in or a key, use the secure credential flow, never this chat. Do not request anything until I send you a published post URL."
2. If Muse asks how the server runs, answer "a URL over HTTP." If it sends a sign-in link, approve it in your browser. If it asks for a key, enter it only in the secure credential prompt, which saves it to the Secure Credentials Store instead of your chat history.
3. Review the connector confirmation, click Continue, and say yes when Muse offers to save it as a skill.
4. Keep approvals on for this connector in Settings, and approve each request in the Sentinel dialog when it appears.
Meta does not review custom connectors, so the connection is between you and aaas.market. Once it is saved, the routine after each post is one message: "My post is live at [URL]. Use the aaas.market connector to request [reactions, comments, or reposts] on it, show me the request, and wait for my approval."
If you use Muse Code instead, add the server to the `mcp_servers` block of `~/.config/muse/settings.json`:
```json
{
"mcp_servers": {
"aaas.market": {
"transport": "streamable_http",
"url": "",
"mode": "optional"
}
}
}
```
Run `muse mcp login aaas.market` if the server asks you to sign in, then `/mcp` in a session to see its tools.
Start free on aaas.market
## 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 Muse:** no CRM is on the built-in connector list. The simplest route is a CSV export of the stalled deals, uploaded to the chat. If your CRM has a public API or a hosted MCP server, Muse can build a custom connector to it the same way as above, set that connector to read-only, since this prompt never needs to change a record. Every call it makes still passes the Sentinel approval dialog, which is a good moment to confirm Muse is reading only the period you named.
**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 Muse:** because Muse cannot read LinkedIn directly, paste the comments and messages into the chat or upload them as a file. Keep the replies in the chat and send them from LinkedIn yourself. If some of these conversations moved to email, keep Gmail on read access, Muse asks before it sends anything, but a draft you copy is safer than a send you approve in a hurry. For a quick first pass on a single public reply, our [LinkedIn comment generator](/tools/linkedin/comment-generator) gives you options to edit.
**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 Muse:** download the LinkedIn analytics export and your website report, upload both, and ask for a dashboard Artifact you can reopen next month instead of a one-time summary. Fixing the post age is easier when Muse does it on a timer: "Seven days after each post, ask me for the LinkedIn export and add that post to the dashboard." That way every post is measured at the same age. To sanity-check reach on any single post, use our [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 Muse:** this is where memory and the skill get updated, so tell Muse to apply nothing until you approve the change log. After you approve, ask it to update the voice guide file, the LinkedIn content skill, and its memory, then open the memory files and check what it actually changed. One message does it:
> Apply the approved items from the change log to the "Voice guide" document, the "LinkedIn content" skill, and your memory. List every change, file by file, and leave anything I did not approve untouched.
**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 Muse report missing access or incomplete work.
Muse can own the recurring part. Put the timing in plain language: "Every Monday at 8am, run prompt 3 and update the research document," or "On the first of each month, run prompt 11 with the files I upload." Muse works on its VM after you close the app and messages you when it needs a file or an approval. A rhythm that works for a weekly series:
- **Monday:** prompt 3 runs on its own and updates the research document.
- **Tuesday:** you approve ideas, Muse writes briefs (prompt 6) and drafts (prompt 7) into the review doc.
- **Wednesday:** prompt 8 runs in a separate side chat, you publish what passes and work the first hour.
- **Friday:** Muse lists open questions from prompts 9 and 10 and the files it still needs from you.
Check the Activity log after the first few runs to see what it actually did. For posting dates, let a scheduler hold the times rather than the agent: our [free social media scheduler](/tools/free-tools/free-social-media-scheduler) reminds you when each post is due, and you publish it yourself. In Muse Code, the same jobs can run from a script with `muse exec --prompt-file`.
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
Muse can help you organize research, retrieve useful evidence, prepare drafts, and learn from your edits. 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.