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laude-institute
chat

Reply to humans who are chatting with me

Overview

Publisherlaude-institute
Repositoryheadlong
Skill namechat
Stars
1.2K
Forks
127
Bundled files
Instructions only
LicenseApache-2.0
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 laude-institute on GitHub. Read the source before you install it.

Installation

Install the Chat 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/laude-institute/headlong/tree/main/skills/chat
  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/laude-institute/headlong.git /tmp/headlong
mkdir -p .claude/skills
cp -r /tmp/headlong/skills/chat .claude/skills/chat
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Chat 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 Chat 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 Chat 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.

chat — Talking to humans

I can talk to humans and other AIs. I send and receive messages by way of my trajectory. Each message is a step in my trajectory.

There is a CLI tool called chat that is used for me and others to send messages. Others can use chat send <message> to append chat messages to my trajectory. To send a message to others, I can write steps directly to my trajectory or I can use chat reply <to_name> <message>.

Trajectory step types

A step in my trajectory with "type":"message" is a message to or from me. I know who it is from and to by looking at the step's to and from fields.

A message in my trajectory to me, i.e. my name, is someone talking to me. A message in my trajectory from me, i.e. my name, is something I already said.

Replying to humans

To send a reply, I can use chat reply <to_name>:

chat reply <to_name> <message>

This creates a message step with from set to my name and to set to the recipient.

IMPORTANT: if I use chat send it sends a message to myself, so I must NEVER use chat send to reply to somebody else. I always use chat reply.

Reviewing conversation history

chat history [N]                          # show last N messages (default 20)
chat history --with <name> [--since 7d]   # my whole conversation with one person
chat history --with <name> -n 50 --json   # same, as JSON with timestamps
chat pending                              # requests the responder deferred to me that I have not delivered yet
chat sent [--since 24h] [-n N] [--json]  # what I sent, newest first, with what the bridge reported back

--with groups a person across every name a bridge has used for them (a Slack user's DM and every channel thread, a phone chat name), so it is the way to check what someone and I said before, even days ago. It reads a small index next to my trajectory, so it is fast; chat person-key <name> shows the stable key behind a routing name.

Knowing what I already sent

chat sent is my sent folder. Each line is one outbound message with its delivery state: delivered, failed (reason) when the bridge could not post it, pending when a Slack or Telegram bridge has not confirmed it yet, or unconfirmed for a transport that never reports back (the phone chat). My wake prompt shows the last day of it. A failed line means the person never saw the message; fix the address and send again. chat send and a proactive chat reply refuse an exact repeat of something I sent to the same destination in the last 24 hours; --force overrides that when the repeat is deliberate.

When to reply

I should reply when I see a message that seems directed at me or asks me a question. I keep my replies natural and conversational. I can also start a conversation if I have a reason to talk to the person, such as asking for help or sharing something relevant to them.

Message formatting

Plain text is the only format guaranteed to render correctly in every chat client, so I default to it:

  • First sentence carries the whole point. Treat it as a subject line.
  • No markdown: no backticks around identifiers, no **bold**, no *italics*, no [text](url). Use plain names, put single quotes around phrases if I need emphasis.
  • Long context (paths, patch names, diffs, log excerpts) goes after the lede, never in it.
  • If I've sent more than 2 messages in a burst without a reply, the next update belongs in my running note (mem edit), not another chat message. I re-raise in chat when the person re-engages.

Shellm.app (macOS menu-bar client)

If the person I'm talking to reads chat through Shellm.app (macos/Shellm/main.swift), two rendering facts apply on top of the rules above:

  1. Notifications truncate the message to 200 chars. Anything past ~180 chars is invisible until they open the popover, so keep the lede under that.
  2. The message body renders as plain SwiftUI Text with no markdown parsing. Backticks, asterisks, and square-bracket links render literally.

Frequently asked questions

What does the Chat AI skill do?

Reply to humans who are chatting with me

Why use Chat on TypingMind?

Because you install it once and use it with any model. Chat 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 Chat in TypingMind?

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

Which AI models can use Chat?

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 Chat?

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

Is the Chat AI skill free?

Yes. It is published on GitHub by laude-institute under the Apache-2.0 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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