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.
In this repository28 other files it gives its agents.
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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
- Distinctive: line 16
Read where the conversation actually is.
- Distinctive: line 18
Add one new piece of value: a relevant observation, a useful resource, a specific result a client or operator saw, a single sharp question.
- Distinctive: line 19
Write the message short and human.
- Distinctive: line 20
If their reply signals heat, hand it to /linkedin-reply-triager instead of sending another nudge.
- Distinctive: line 25
When to send it, expressed relative to the last touch, not a fixed date.
- Distinctive: line 31
No em-dashes. No "hope you're well".
≈ “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 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
- 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.
- 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.
- 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.
- Write the message short and human. Reads like a peer continuing a real thread.
- 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.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
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- 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.

