agentleFS
styfinitystyfinity/linkedin-engine/skills/linkedin-followup-adapter/SKILL.md

linkedin-followup-adapter

Skill9 starsChanged 4 months agoMITView on GitHub

Write the next follow-up from where the conversation actually is, off the real state of the thread rather than a fixed timer. Use when a prospect has gone quiet and the next touch needs a new angle.

The file

We split this skill into the rules it gives an agent

6 rules. About 0 are common, 6 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈6

    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 where the conversation actually is.

    line 16
  2. Distinctive:

    Add one new piece of value: a relevant observation, a useful resource, a specific result a client or operator saw, a single sharp question.

    line 18
  3. Distinctive:

    Write the message short and human.

    line 19
  4. Distinctive:

    If their reply signals heat, hand it to /linkedin-reply-triager instead of sending another nudge.

    line 20
  5. Distinctive:

    When to send it, expressed relative to the last touch, not a fixed date.

    line 25
  6. Distinctive:

    No em-dashes. No "hope you're well".

    line 31

≈ “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 Follow-up Adapter

A follow-up is a new move, not a reminder. This skill reads the real state of the conversation and writes the next message off THAT, adding a fresh piece of value every time.

Inputs

  • The thread so far and what actually happened on the last touch (they replied and went quiet, they opened a loop, they raised an objection, they ghosted after a yes): $ARGUMENTS
  • The brief (persona tone, pains, offer) loads automatically.

Do this

  1. Read where the conversation actually is. Name the real state in one line: warm-but-busy, half-objection, interest-without-action, or genuine silence. Write off that, not a calendar.
  2. Pick a new angle, different from the last touch. If the last message asked a question, this one gives. If the last gave value, this one references a signal or a result.
  3. Add one new piece of value: a relevant observation, a useful resource, a specific result a client or operator saw, a single sharp question. Never restate the prior ask.
  4. Write the message short and human. Reads like a peer continuing a real thread.
  5. If their reply signals heat, hand it to /linkedin-reply-triager instead of sending another nudge. Run /linkedin-humanizer on the draft before it ships.

Output

  • The next message, labelled clearly.
  • The angle it takes and the conversation state it answers, one line.
  • When to send it, expressed relative to the last touch, not a fixed date.

Rules

  • State-driven, never timer-driven. The thread tells you when and what, not the clock.
  • New angle and new value on every touch. If you cannot add value, do not send.
  • Banned phrase: "just following up" (and its cousins "bumping this", "circling back", "checking in").
  • No em-dashes. No "hope you're well".
  • Draft only. Claude drafts, the operator approves. Sending runs through the connected CLI/MCP layer on the operator's own session or provider key, shows the draft first, and is capped at 20 actions per day for new accounts.

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.