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Li Reply

CommunityPopular
Jakeschincariol
li-reply

Handle the replies under the user's own LinkedIn posts - draft answers to every comment, sorted by which ones are worth answering. Use when the user pastes the comments on their post, says "reply to these", "handle my comments", "someone said X on my post", or is dealing with a critic or a lead in the comments.

Overview

PublisherJakeschincariol
Repositorylinkedin-agent-skill
Skill nameli-reply
Stars
1.3K
Forks
192
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Jakeschincariol on GitHub. Read the source before you install it.

Installation

Install the Li Reply AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Jakeschincariol/linkedin-agent-skill/tree/main/skills/li-reply
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Jakeschincariol/linkedin-agent-skill.git /tmp/linkedin-agent-skill
mkdir -p .claude/skills
cp -r /tmp/linkedin-agent-skill/skills/li-reply .claude/skills/li-reply
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Li Reply in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Li Reply on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Li Reply is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

li-reply

The reply thread under your own post is where reach is actually decided. Every reply is a second engagement event on the post, and the first hour of replies does most of the work. But the value is not equal across comments, so this skill sorts before it writes.

Input

The user pastes the comments, ideally with names and roles. Screenshots are fine. Do not scrape the thread with a browser tool.

Triage first

Sort every comment into one of five buckets and say the count out loud:

bucketwhat it iswhat it gets
LEADsomeone describing the problem you solvea real answer + a soft open door
SUBSTANCEadds data, disagrees, extendsthe longest reply on the thread
PEERa name worth being seen next toa reply that gives them something
SUPPORT"great post", 🔥, a taga like, and a 3-8 word reply at most
NOISEpitch, spam, bad faithnothing, or one line and out

Then write in that order, and stop writing when the value stops.

How to reply

  • Answer the actual question. If someone asks how, tell them how, in the reply. Do not send them to a DM to hear the answer they could have had.
  • Use their name once, at the start, and never with an exclamation mark.
  • Match their length. A two-line comment does not get a six-line reply.
  • To a critic: concede the true part first, in their words, then hold the line on the part you believe. Never delete, never get defensive, never reply twice on the same thread.
  • To a lead: answer fully in public. The open door is one sentence at the end, and it is an offer of help, not a pitch. Public value is what makes the next person DM you.
  • To a pitch in your comments: ignore it. Replying gives it reach.

Output

One block, grouped by bucket, each reply copy-ready and already humanized:

REPLIES  ·  17 comments  ·  1 LEAD, 3 SUBSTANCE, 4 PEER, 8 SUPPORT, 1 NOISE

LEAD
@Sarah Chen - "we have the same problem with proposals"
> The part that fixed it for us was pulling the pricing table out of the doc
> entirely and sending it as its own page. Cut a whole review round. Happy to
> send you the template if it is useful.

SUBSTANCE
@Marcus Webb - disagrees on cadence
> Fair, and 4x a week only worked because I had 8 months of posts behind it.
> From a standing start I would do what you said.

SUPPORT  (like these, reply to the first three)
@... "This is great" -> Thanks Dan.
...

NOISE  (1)
Skipped: an agency pitch. Replying gives it reach.

Then the gate: nothing is posted until the user says yes. They paste the replies themselves.

Frequently asked questions

What does the Li Reply AI skill do?

Handle the replies under the user's own LinkedIn posts - draft answers to every comment, sorted by which ones are worth answering. Use when the user pastes the comments on their post, says "reply to these", "handle my comments", "someone said X on my post", or is dealing with a critic or a lead in the comments.

Why use Li Reply on TypingMind?

Because you install it once and use it with any model. Li Reply is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Li Reply in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Jakeschincariol/linkedin-agent-skill/tree/main/skills/li-reply. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Li Reply?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Li Reply?

As many as you like. As long as a model supports skills, you can use Li Reply with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Li Reply AI skill free?

Yes. It is published on GitHub by Jakeschincariol under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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