agentleFS
styfinitystyfinity/linkedin-engine/skills/linkedin-engage-plan/SKILL.md

linkedin-engage-plan

Skill9 starsChanged 4 months agoMITView on GitHub

Design a multi-touch warming sequence for one target so they recognise you before you ever message them. Use when you have a specific person worth warming up before any pitch.

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
  1. Distinctive:

    Read the starting warmth. Skip steps they're already past.

    line 16
  2. Distinctive:

    Stop the moment they reply.

    line 30
  3. Distinctive:

    No em-dashes. No guaranteed-result or fast-growth promises.

    line 33

≈ “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.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

LinkedIn Engage Plan

Warming is recognition before the message. This skill lays out a conditional, stop-on-reply arc that earns familiarity first, so the eventual DM lands on a name they already know, never a cold pitch.

Inputs

  • The target (name, role, company) and how warm they already are (never seen you, follow each other, you've commented before, a mutual connection): $ARGUMENTS
  • The brief (persona, pains, offer, voice) loads automatically.

Do this

  1. Read the starting warmth. Skip steps they're already past. A target who follows you starts further along than a stranger.
  2. Lay out a day-by-day arc, each step conditional on the prior one landing and on no reply yet:
    • Day 1: react to a recent, relevant post of theirs. Specific, not the latest by default.
    • Day 2: leave a real value-add comment on their content. Hand the draft to /linkedin-comment-warmer.
    • Day 3: send a connection note that references the prior touch. Hand to /linkedin-connection-note.
    • Day 4+ (only after the connection is accepted): a first DM. Hand to /linkedin-first-dm only once the connection is accepted.
  3. Set the stop conditions plainly: the moment they reply or engage back, stop the arc and move to a real conversation.
  4. Note the gates: each step waits on the prior one. No connection note before a comment has landed; no DM before the connection is accepted.

Output

A day-by-day arc table: day, action, the skill to run for that step, and the stop/advance condition. End with the single next touch to make today and what has to be true before the step after it.

Rules

  • Warm before you message. Recognition first, pitch never in the arc itself.
  • Stop the moment they reply. Every step is conditional on no reply yet.
  • This skill drafts and plans only. Nothing here sends automatically.
  • Any sending happens through the connected CLI/MCP layer wired to your own LinkedIn session, drafts shown first, explicit approval required, hard-capped at 20 actions per day for new accounts.
  • No em-dashes. No guaranteed-result or fast-growth promises.

Download now (ZIP)

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
What did you use it for? e.g. Reviewing pull requests in a Rails app

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.