linkedin-onboard
Skill9 starsChanged 4 months agoMITView on GitHub
Build the user's linkedin-brief.md in one pass so the whole engine runs in their voice and their market. Use first, before running any other skill.
In this repository28 other files it gives its agents.
- linkedin-booking-closerSkill
- linkedin-carousel-writerSkill
- linkedin-cold-reviverSkill
- linkedin-comment-engineSkill
- linkedin-comment-warmerSkill
- linkedin-connection-noteSkill
- linkedin-content-calendarSkill
- linkedin-engage-planSkill
- linkedin-engager-analyticsSkill
- linkedin-first-dmSkill
- linkedin-followup-adapterSkill
- linkedin-hook-labSkill
- linkedin-humanizerSkill
- linkedin-icp-definerSkill
- linkedin-objection-handlerSkill
- linkedin-outreachSkill
- linkedin-post-autopsySkill
- linkedin-post-writerSkill
- linkedin-profile-auditorSkill
- linkedin-reply-triagerSkill
- linkedin-repurposerSkill
- linkedin-sell-by-chatSkill
- linkedin-signal-stackSkill
- linkedin-story-minerSkill
- linkedin-strategistSkill
- linkedin-voice-profilerSkill
- linkedin-warm-list-builderSkill
- linkedin-warm-trackerSkill
The file
We split this skill into the rules it gives an agent
3 rules. About 0 are common, 3 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈3
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 16
Run /linkedin-icp-definer on the business and buyer details.
- Distinctive: line 17
Run /linkedin-voice-profiler on the pasted posts and DMs.
- Distinctive: line 29
No em-dashes. Draft first: show the assembled brief, the user confirms, then it is saved.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
LinkedIn Onboard
The setup pass. This skill assembles the one file every other skill reads: linkedin-brief.md. Run it once, in the user's own words and evidence, and the whole engine knows the voice, the market, and the offer.
Inputs
- The user's business, who they sell to, their offer, and a paste of their past posts and DMs (the more real text, the better): $ARGUMENTS
- Nothing loads automatically yet. This skill is what creates the brief that loads from now on.
Do this
- Run /linkedin-icp-definer on the business and buyer details. Capture the ICP it returns: who, role, company shape, the trigger that makes them a fit.
- Run /linkedin-voice-profiler on the pasted posts and DMs. Capture the voice profile: cadence, sentence length, words they reach for, words they never use.
- Capture the offer in the user's language: one sentence (what it is), the outcome it produces, the proof behind it, and the risk reversal (the guarantee or the safety net).
- Capture the top three to five buyer pains in the buyer's words, pulled from the paste where possible, not paraphrased into jargon.
- Assemble all of it into one file and write it to linkedin-brief.md in the project root, with clear sections: ICP, Voice profile, Offer, Pains.
Output
The assembled linkedin-brief.md (ICP, voice profile, offer, pains), shown in full for the user to confirm. End with a one-line note: the SessionStart hook will load this file into every session from now on, so no other skill will ask for this context again.
Rules
- Build only from the user's real evidence and real words. If a section is thin because the paste was thin, say so and ask for more text rather than inventing it.
- The offer positions the user as a Revenue Partner who uses AI to do the heavy lifting. Never "AI consultant" or "AI agency".
- This is the file every other skill reads. Get it right before running anything else.
- No em-dashes. Draft first: show the assembled brief, the user confirms, then it is saved.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

