linkedin-repurposer
Skill9 starsChanged 4 months agoMITView on GitHub
Turn one source asset into a week of content - five post stubs, a carousel outline, and a DM nudge. Use when you have a transcript, a doc, a win, or a long post and want a week of content from it.
9 other projects use a version of this.
Adopted by
Most recently taken up by
- uplika/uplika-plugintheir own version, first seen Nov 8, 2026
- 07001ec010shnk-svg/linkedin-agent-skillstheir own version, first seen Oct 25, 2026
- CodesbyFebin/linkedin-managertheir own version, first seen Oct 20, 2026
- AIProUK/linkedin-claude-skillstheir own version, first seen Oct 20, 2026
- TaplioOfficial/taplio-linkedin-claude-skillstheir own version, first seen Oct 12, 2026
- TaplioOfficial/taplio-linkedin-plugintheir own version, first seen Oct 12, 2026
Other versions9 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 9
- sergebulaev/linkedin-skills★ 5.4ksame name
- TaplioOfficial/taplio-linkedin-claude-skills★ 7same name
- TaplioOfficial/taplio-linkedin-plugin★ 3same name
- uplika/uplika-plugin★ 3same name
- AIProUK/linkedin-claude-skills★ 1same name
- sylvan-io/linkedin-skills★ 1same name
- 07001ec010shnk-svg/linkedin-agent-skills★ 0same name
- CodesbyFebin/linkedin-manager★ 0same name
- subu53/linkedin_exponential★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
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-onboardSkill
- linkedin-outreachSkill
- linkedin-post-autopsySkill
- linkedin-post-writerSkill
- linkedin-profile-auditorSkill
- linkedin-reply-triagerSkill
- 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
2 rules. 0 are common, 2 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 9 edited versions we read keep these.
2
Distinctive
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
Read the source. Pull the 5 strongest ideas: the ones that prove a competence the buyer pays for, not the ones that are merely interesting.
- Distinctive: line 31
Every piece must prove a competence the buyer buys.
“Common” is measured: kept by at least 6 in 10 of the 9 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
LinkedIn Repurposer
One good asset is a week of content. This skill mines a single source for the five strongest ideas and shapes each into something the buyer reads.
Inputs
- The source asset (a call transcript, a doc, a win, a long post): $ARGUMENTS
- The brief (lanes, voice profile, offer, pains) loads automatically.
Do this
- Read the source. Pull the 5 strongest ideas: the ones that prove a competence the buyer pays for, not the ones that are merely interesting.
- Map each idea to a content lane from the brief and a hook formula (contrarian take, before/after, list, story open loop, hard number).
- Draft a post stub for each: lane, hook, one-line angle. Stub only, not the full post.
- Pull the single best idea into a carousel outline (title slide plus 5 to 7 slide lines).
- Pull one idea into a short DM nudge that opens a conversation off the back of the asset.
- Anonymise as you go: keep the real numbers and outcomes, drop every name (people, companies, clients). A number stays "a client" or "an operator".
Output
- Five post stubs, each labelled with its lane, hook formula, and angle.
- One carousel outline (title plus slide lines).
- One DM nudge (two short lines). End with a one-line note: run /linkedin-humanizer on any stub before drafting it full, and hand the DM nudge to /linkedin-first-dm if it targets a specific prospect.
Rules
- Anonymise everything. No real person, company, or client names. Keep the numbers, drop the source.
- Every piece must prove a competence the buyer buys. Cut the interesting-but-unsellable.
- Stubs and outlines only. Claude drafts, the operator approves and posts.
- No em-dashes.
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.

