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.
Claude Code is a good fit because the whole system can live as plain Markdown files in one folder on your machine: the context, the voice guide, the drafts and the reports. CLAUDE.md and skills load your rules for you, so you edit files instead of retyping prompts.
Save this for your next content planning session.
## Before You Run a Single Prompt: Load Your Business Context
Claude Code 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. In Claude Code that is a file edit: change `context/business-context.md` and every later session reads the new version.
### Where Claude Code keeps this context
Claude Code works on the folder you start it in, so the context becomes files on disk. It does not need to be a code project, an empty folder called `linkedin` is enough.
A layout that works for the whole series:
```
linkedin/
CLAUDE.md
context/ business-context.md, voice-guide.md, top-posts.md, sources.md
calls/ transcripts/ data/
research/ briefs/ drafts/ review/ published/
.claude/skills/ linkedin-calendar/ linkedin-draft/
```
- **The reference document.** Start `claude` in that folder, run the prompt above, and ask it to save the result as `context/business-context.md`. That file path is the answer to "tell me where it is stored".
- **CLAUDE.md for always-on rules.** Create `CLAUDE.md` at the folder root with a few lines: read `@context/business-context.md` and `@context/voice-guide.md` before any LinkedIn work, save drafts to `drafts/`, never publish or send anything. Claude Code loads CLAUDE.md at the start of every session, and `@path` imports pull in the files it names. Keep it under about 200 lines, longer reference material belongs in skills or `.claude/rules/` files.
- **AGENTS.md.** If the folder already has an `AGENTS.md`, Claude Code can load it in place of CLAUDE.md.
- **Auto memory.** Claude Code also keeps its own memory notes, which load before you type and come back after `/compact`. Run `/memory` to open and edit both CLAUDE.md and those notes, and `/context` to see what actually loaded.
- **User level for personal habits.** CLAUDE.md files add up across levels, so preferences you want in every folder (spelling, no emojis) go in your user-level CLAUDE.md, and the LinkedIn rules stay in this folder.
- **Output style.** For writing sessions you can switch to an output style with a writing-assistant role, so replies stop sounding like an engineer's.
## PART 1: RESEARCH + CONTENT PRODUCTION
Prompts 1-8. Each one reads files from the `linkedin` folder and writes its result back into it, so the next prompt can pick it up.
## 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 Claude Code:**
- Input: your best posts in `context/top-posts.md`, one post per heading with its reactions and comments, and transcripts as text files in `transcripts/`. Reference both with `@` paths so Claude reads them in full instead of skimming.
- Output: `context/voice-guide.md`. The "source references" become file names and headings you can open and check.
- Where the rules live: copy only the five or six rules that must hold for every post into CLAUDE.md. The full guide stays in its own file and is imported when a prompt needs it, so it does not sit in context in every session.
- Watch for: if Claude gets the same phrase wrong twice, write the fix into CLAUDE.md instead of correcting it a third time in chat.
**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 Claude Code:**
- Input: calls, support tickets and closed-lost notes exported into `calls/` as text or CSV, one conversation per file, with the date and the customer's role in the file name.
- Run: forty transcripts would flood your main session, so ask Claude to hand the reading to a subagent. It works in its own context window and returns only the grouped themes and counts.
- Output: `research/customer-questions.md`, with the file name of each quotation as its source reference.
- Check: the "number of distinct conversations" column is easy to verify, because you can search `calls/` for the quoted phrase yourself.
**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, the pages I link, and accessible public sources, 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 Claude Code:**
- Search: the built-in web search tool works when you sign in with a Claude subscription or an Anthropic Console account. It is not available on Amazon Bedrock, there, give the WebFetch tool specific URLs instead.
- X: there is no native X search, so keep the accounts and thread links you follow in `context/sources.md` and reference that file in the prompt. If a page will not load, paste the posts into `research/raw/`.
- Output: `research/topics-YYYY-MM-DD.md`, so each week's sweep is kept and prompt 6 can read the latest one.
- Watch for: a summary that cites a link Claude never opened. Ask it to mark which sources it read in full.
**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 Claude Code:**
- Input: `data/swipe.csv` with columns for author, date, format, reactions, comments and reposts.
- Run: ask Claude to write a short script that computes each author's median per format and to run it, rather than estimating in prose. "Show the calculation" then means a script you can rerun when the file grows.
- Output: `research/outliers.md` with the five patterns and an original idea for each.
- Your own baseline: check your own numbers against your usual results with the [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 Claude Code:**
- Plan first: press Shift+Tab to enter plan mode, so Claude reads the transcript and proposes the eight moments without writing any files.
- Then draft: once you agree on the three, leave plan mode and let it write `drafts/YYYY-MM-DD-slug.md`, one file per post.
- Placeholders: keep them in square brackets. Searching `drafts/` for `[` shows every result that still needs confirming.
- Timestamps: they come back only if the transcript has them, so export it from your recorder with timestamps on.
**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 Claude Code:**
- Input: the files from prompts 2-4: `@research/customer-questions.md`, the latest topics file and `@research/outliers.md`.
- Output: `calendar.md` as a Markdown table, and each week-one brief in its own file under `briefs/`.
- Reuse: save the prompt as a skill in `.claude/skills/linkedin-calendar/SKILL.md`. Next month it is one `/linkedin-calendar` command.
- Timing and formats: check the posting days against our [best time to post on LinkedIn](/tools/linkedin/best-time-to-post) data, and build any carousel slot 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 Claude Code 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 Claude Code:**
- Skill: this is the prompt worth turning into a `/linkedin-draft` skill that takes a brief file as its argument.
- Safety: because the skill writes files, set `disable-model-invocation: true` in its frontmatter so it runs only when you call it by name.
- Output: each draft in `drafts/`, with the two alternative openings and the missing-evidence list at the bottom of the same file.
- Openings: test extra hooks in the [LinkedIn hook generator](/tools/linkedin/hook-generator) before you settle on one of the two alternatives.
**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 points.
> Specificity and supporting evidence: 25 points.
> Strength and clarity of the argument: 20 points.
> Match with my voice: 20 points.
> Readability and formatting: 10 points.
> 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 Claude Code:**
- Reviewer: a subagent starts from a fresh context with your CLAUDE.md, not with the conversation that wrote the drafts, so it judges the text rather than the reasoning behind it.
- Output: `review/YYYY-MM-DD.md` with scores, the revision and open questions. The drafts stay untouched until you approve.
- Automatic checks: if you want a length check every time a draft file changes, a hook can run a small script after each edit. Hooks fire on every matching event without Claude deciding.
- Last look: preview the approved text in the [LinkedIn post preview](/tools/linkedin/post-preview) and confirm length in the [LinkedIn character counter](/tools/linkedin/character-counter).
**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
The review leaves you with an approved file in `review/`. You copy it into LinkedIn and publish it yourself, then add the live URL and the time to the top of `published/YYYY-MM-DD-slug.md`, which prompt 11 uses to compare posts at the same age. From here, every other MAXXING technique is the same: in the first, golden hour, get as many reactions on the post as you can.
**Start with people you can reach.** Ask colleagues, friends, relatives and clients, anyone you can actually contact, to react as soon as the post is live.
**No such people? Use a specialized service,** but only one with quality engagement and moderation.
**Proven groups.** The engagement groups in the table below come from our other articles, where we have already checked them.
**aaas.market** is our own service:
- real people,
- the highest level of moderation and the best-quality audience, since that is where we put the most attention,
- a closed community with entry through verification,
- every type of engagement supported.
**Give Claude Code the MCP connection.** An agent like Claude Code needs MCP to get engagement for a post. aaas.market has an MCP server at through it, Claude Code can request any type of engagement on a post you have published. Add it from your terminal, not inside a running session:
```
claude mcp add --scope user --transport http aaas.market
```
`--scope user` makes it available in every folder you open, not only the LinkedIn one. Then:
1. Run `claude mcp list` and check the status next to `aaas.market`.
2. The server uses OAuth, so the first status is `! Needs authentication`. Start `claude`, type `/mcp`, select `aaas.market` and choose `Authenticate`. Your browser opens the aaas.market sign-in, sign in and approve, and the status changes to connected.
3. The first time Claude calls an aaas.market tool, Claude Code asks for permission, approve it. The tool call is labeled with the server name.
Already added aaas.market as a connector at claude.ai? Connectors from claude.ai/customize/connectors load in the CLI automatically when you sign in with that same account. In the Claude Code desktop app you can add the server through its Connectors UI instead.
After the post is live, paste its URL into the session and name the engagement you want. Publishing, comments, invitations and messages stay with you. For a really strong result at the engagement step, this is how to do it. Start free on aaas.market.
## LinkedIn engagement groups you can join
These are free communities we checked on Oct 21, 2026, with the member counts each platform displayed that day. Admins run every group by their own rules, so read the pinned message before you share a link. Save the table as `context/groups.md` if you want it at hand inside your Claude Code sessions.
| 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 |
Rather not search through groups for each post? Start free on aaas.market.
## PART 2: PIPELINE + PERFORMANCE
Prompts 9-12. These touch sales data, so this is where file locations and `.gitignore` start to matter.
## 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 Claude Code:**
- CRM: if your CRM publishes a remote MCP server, add it like aaas.market above, with its own name and URL, and sign in through `/mcp`. Otherwise export the stalled deals to `data/stalled-deals.csv`.
- Privacy: deal notes are sensitive. If the folder is a git repository, list `data/` in `.gitignore` so they are never committed.
- Output: briefs in `briefs/` and the two drafts in `drafts/`, so prospect details never sit next to publishable copy.
- Watch for: an MCP server can write as well as read. Approve its tool calls one at a time the first few runs.
**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 Claude Code:**
- Input: Claude Code has no access to your LinkedIn comments or inbox by default, so paste them into `inbox/YYYY-MM-DD.md`, one conversation per heading.
- Output: `replies/YYYY-MM-DD.md`, with each draft under the message it answers.
- Sending: you copy and send each reply yourself.
- A second opinion: for quick public replies, compare with options from the [LinkedIn comment generator](/tools/linkedin/comment-generator).
**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 Claude Code:**
- Input: monthly exports in `data/YYYY-MM/`: LinkedIn analytics, website visits, inbound inquiries and CRM records.
- Run: have Claude write one script that joins them by post URL and date, so next month's report is the same script on new files.
- Output: `reports/YYYY-MM.md`. Signed in with a claude.ai account, Claude Code can also publish it as an Artifact, a private interactive page you can open or share.
- Reach check: for a quick sense of reach before the detail, try 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 Claude Code:**
- Input: what you actually posted, saved as `published/YYYY-MM-DD-slug.md` next to its draft. If the folder is a git repository, each edit is also in its history.
- Output: `changelog.md` with the proposed rules, an example for each, and when an exception applies.
- Apply: after you approve it, Claude can edit `context/voice-guide.md`, the `/linkedin-draft` skill and CLAUDE.md. A correction you keep making is a CLAUDE.md edit, not a one-off note in chat.
- Keep separate: performance hypotheses go to `experiments.md`, not into the rules, so one good post does not turn into a permanent rule.
**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. In Claude Code, that means the folder, CLAUDE.md and `context/` exist by Friday.
Week 2: Build your research backlog. Run prompts 2-4 using a manageable set of customer conversations, market sources, and supplied LinkedIn examples. Fill `calls/`, `context/sources.md` and `data/swipe.csv` before you start.
Week 3: Produce the first batch. Use prompts 5-7 to turn approved material into a calendar and a small number of drafts. By the end of the week, `/linkedin-calendar` and `/linkedin-draft` should exist as skills.
Week 4: Strengthen the review process. Run prompt 8, publish the posts you approve, run the first hour on each one, and record the changes you make. Let a subagent do the review and keep its files in `review/`.
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. If your CRM has an MCP server, add it now.
Then repeat monthly: Use prompts 11 and 12 to review results, improve the instructions, and choose the next experiment. The prompt 11 script simply runs again on the new month's `data/` folder.
For any recurring job, define its inputs, destination, frequency, and owner. Test a run before relying on the schedule, and have Claude Code report missing access or incomplete work.
**Automating it in Claude Code.** Signed in with a claude.ai account, you can set up cloud scheduled runs with `/schedule`, which execute on Anthropic's side without your laptop. Without that sign-in, `/loop` repeats a command on an interval inside an open session. For your own scheduler, `claude -p "..."` runs a single prompt non-interactively, so a weekly job can run prompt 3 and save the file to `research/`, GitHub Actions is another option if the folder lives in a repository. Each of these produces files for you to review. None of them publishes to LinkedIn. To get a nudge when each approved post is due, use our [free social media scheduler](/tools/free-tools/free-social-media-scheduler): we remind you, you publish, free for good.
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
Claude Code 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. A folder full of well-organized files does not change that, it only makes your judgment easier to apply every week.
Once a post from this folder is live, bring aaas.market into its first hour. Start free on aaas.market.
Otherwise, load your context and run prompt 1. Improving the inputs will make every step after it more useful.