Rlm Heartbeat

Organization

7df-lab

rlm-heartbeat

Manage agent-owned RLM heartbeats from the Python REPL. Use when the user asks the agent to start, create, schedule, or manage a heartbeat, unless they explicitly request the user's /heartbeat.

Overview

Publisher	7df-lab
Repository	`devo`
Skill name	`rlm-heartbeat`
Stars	341
Forks	139
Bundled files	2
License	MIT
Links	Skill source Repository Homepage

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.
2 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by 7df-lab on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/7df-lab/devo/tree/main/crates/skills/assets/bundled/rlm-heartbeat
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.

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/7df-lab/devo.git /tmp/devo
mkdir -p .claude/skills
cp -r /tmp/devo/crates/skills/assets/bundled/rlm-heartbeat .claude/skills/rlm-heartbeat

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Rlm Heartbeat 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 Rlm Heartbeat 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 Rlm Heartbeat 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.

RLM Heartbeat

RLM heartbeats are internal recurring prompts for the current agent session. They are separate from the user's visible /heartbeat: this skill cannot read, replace, pause, resume, or clear that user-level heartbeat.

Call directly from the Python REPL:

python
await rlm_heartbeat.create("check test progress", interval="5m", label="tests")
await rlm_heartbeat.create("watch build", delivery_mode="follow_up")
await rlm_heartbeat.list()
await rlm_heartbeat.update("job-id", status="pause")
await rlm_heartbeat.delete("job-id")

API

await rlm_heartbeat.list(include_inactive=False) — list this session's internal RLM heartbeats. By default this includes active and paused entries.
await rlm_heartbeat.create(instruction, interval=None, label=None, delivery_mode=None) — create a recurring heartbeat for this session. The default interval is every 5 minutes. Multiple RLM heartbeats may run at once; use labels to distinguish them. delivery_mode is "steer" (default) or "follow_up".
await rlm_heartbeat.update(id, instruction=None, interval=None, label=None, status=None, delivery_mode=None) — update one RLM heartbeat by id. status may be "pause" or "resume"; delivery_mode may be "steer" or "follow_up".
await rlm_heartbeat.delete(id) — cancel one RLM heartbeat by id.

Delivery mode

Each heartbeat has a delivery mode controlling how the scheduled prompt reaches the session when it is busy:

steer (default): interrupt the current turn so the heartbeat runs promptly.
follow_up: wait for the current turn to finish before running the heartbeat.

Rules

Use this when the user asks you to start, create, schedule, or manage your own heartbeat without explicitly referring to /heartbeat.
Use this only for agent-internal recurring checks and long-running task coordination.
Do not use this skill to satisfy a user's request to configure /heartbeat; that is a separate user-level surface.
Keep heartbeat instructions specific and actionable so each recurring turn knows exactly what to inspect or continue.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

pyproject.toml1 KB
src/rlm_heartbeat/__init__.py4 KB

Frequently asked questions

What does the Rlm Heartbeat AI skill do?

Manage agent-owned RLM heartbeats from the Python REPL. Use when the user asks the agent to start, create, schedule, or manage a heartbeat, unless they explicitly request the user's /heartbeat.

Why use Rlm Heartbeat on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/7df-lab/devo/tree/main/crates/skills/assets/bundled/rlm-heartbeat. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Rlm Heartbeat?

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 Rlm Heartbeat?

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

Is the Rlm Heartbeat AI skill free?

Yes. It is published on GitHub by 7df-lab 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.

View all

Diffs

openclaw

OrganizationPopular

Use the diffs tool to produce real, shareable diffs (viewer URL, file artifact, or both) instead of manual edit summaries.

Feishu Doc

Feishu document read/write workflows. Activate when the user mentions Feishu docs, cloud docs, or docx links.

Feishu Drive

Feishu cloud-storage and comment workflows. Activate when the user mentions cloud space, folders, Drive files, or document comments.

Feishu Wiki

Feishu knowledge-base navigation workflows. Activate when the user mentions a knowledge base, wiki, or wiki link.

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

RLM Heartbeat

API

Delivery mode

Rules

Bundled files

Frequently asked questions

Related AI Skills

Set up your own AI workspace now