linkedin-engager-analytics
Skill9 starsChanged 4 months agoMITView on GitHub
Analyse everyone who liked or commented on a post, score them against your ICP, and return the match rate plus the top 10 profiles with a real-signal outreach note for each. Use after a post lands to find the warm prospects hiding in your engagement.
7 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
- sylvan-io/linkedin-skillstheir own version, first seen Oct 11, 2026
- subu53/linkedin_exponentialtheir own version, first seen Oct 8, 2026
Other versions7 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 7
- sergebulaev/linkedin-skills★ 5.4ksame 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-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-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
4 rules. 0 are common, 4 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 7 edited versions we read keep these.
4
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 9
Every post that lands buries warm prospects in its likes and comments.
- Distinctive: line 28
Every outreach note must cite a real, specific signal.
- Distinctive: line 29
No invented titles, companies, or signals.
- Distinctive: line 30
No em-dashes. No guaranteed-result promises.
“Common” is measured: kept by at least 6 in 10 of the 7 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 Engager Analytics
Every post that lands buries warm prospects in its likes and comments. This skill pulls them out, scores them, and hands you the ones worth a conversation.
Inputs
- A post URL (pulled live if the LinkedIn CLI/MCP layer is connected) OR a pasted list of engagers with name, title, company: $ARGUMENTS
- The brief (ICP, persona, pains, offer) loads automatically.
Do this
- Get the engagers. Live via the connected LinkedIn CLI/MCP layer if it is wired in. If it is not, work off the pasted list and say so in one line.
- Score each engager against the brief's ICP, 0 to 100, on title, seniority, company size, industry, and any buying signal. Show the score logic in one phrase per person.
- Compute the ICP match rate: the share of total engagers who clear the fit bar.
- Rank everyone and return the top 10. For each, write a one-line outreach note that references their REAL signal: the exact comment they left, their role, or a recent post of theirs. No generic openers.
- Flag the 3 hottest profiles to action first.
Output
Lead with the ICP match rate headline (e.g. "31% of 64 engagers fit the ICP"). Then a top-10 table: name, title, company, score, outreach note. Then a short "Action these 3 first" list, ready to hand to /linkedin-outreach. Run any draft note through /linkedin-humanizer before it goes out.
Rules
- Pulling live engagers needs the connected CLI/MCP layer. Without it, state plainly that you are scoring the pasted list only.
- Draft notes only. Nothing sends from this skill. Sending happens through the opt-in CLI/MCP layer, on explicit approval, capped at 20 actions a day for new accounts.
- Every outreach note must cite a real, specific signal. If a person's signal is just a like with no comment, say so and rank accordingly.
- No invented titles, companies, or signals. If a field is missing, mark it unknown rather than guessing.
- No em-dashes. No guaranteed-result promises.
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.