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Heartbeat

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MagicCube
heartbeat

Proactive intelligence pulse — scans user's memory files (USER.md, SOUL.md) to identify current interests, investments, projects, and concerns, then performs targeted web searches to find noteworthy updates. Returns a concise briefing if something deserves attention, or `[SKIPPED]` if nothing warrants interruption. Use this skill when the system needs to check if there's anything worth proactively notifying the user about — like a scheduled heartbeat check, a "what's new" sweep, or when the user asks "anything I should know?", "what did I miss?", or "any updates?". Also triggers on requests like "check my interests", "scan for news", "morning briefing", or "pulse check".

Overview

PublisherMagicCube
Repositoryagentara
Skill nameheartbeat
Stars
516
Forks
58
Bundled files
Instructions only
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 MagicCube on GitHub. Read the source before you install it.

Installation

Install the Heartbeat 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/MagicCube/agentara/tree/main/user-home/.claude/skills/heartbeat
  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/MagicCube/agentara.git /tmp/agentara
mkdir -p .claude/skills
cp -r /tmp/agentara/user-home/.claude/skills/heartbeat .claude/skills/heartbeat
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 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 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 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.

Heartbeat — Proactive Intelligence Pulse

You are performing a heartbeat check: scanning the user's world for things that matter to them right now, and deciding whether any of it is worth their attention.

Philosophy

The user's time and attention are sacred. A heartbeat that cries wolf is worse than no heartbeat at all. Only surface information that is actionable, time-sensitive, or significantly changes the user's understanding of something they care about. When in doubt, return [SKIPPED].

Step 1: Read Memory

Read the user's memory files to build a profile of current interests and concerns:

Extract a prioritized list of watchlist topics. Typical categories:

CategoryExamplesAlert Threshold
InvestmentsStock positions, crypto holdingsPrice moves >5% in a day, major company news (earnings, lawsuits, leadership changes), analyst upgrades/downgrades
Current projectActive codebase, architecture decisionsMajor version releases of key dependencies, breaking changes, security vulnerabilities
Career/IndustryEmployer news, role-relevant trendsCompany announcements, industry shifts directly affecting the user's role
Tech stackFrameworks, tools, languages used dailyNew major releases, deprecation notices, critical CVEs
Upcoming eventsTravel plans, deadlinesWeather alerts, schedule changes, booking reminders within 7 days
Personal interestsPhotography gear, anime, content creationNotable releases or events only (not routine content)

Step 2: Read News

Fetch the following sources directly by URL — no search needed, just fetch:

SourceURLNotes
Hacker Newshttps://hnrss.org/newestRSS feed
Product Hunthttps://www.producthunt.com/feedRSS feed — high weight: today's top launches often contain directly actionable tools or competitors
GitHub Trendinghttps://github.com/trending?since=dailyDaily trending — high weight: surfacing repos that match the user's stack or interests is high-value signal
Google News (CN)https://news.google.com/rss?hl=zh-CN&gl=CN&ceid=CN:zh-HansRSS feed

Weighting guidance: Product Hunt and GitHub Trending are real-time ranked signals — their top items reflect what's genuinely popular today. Prioritize items from these two sources over generic news articles when relevance is comparable. A trending repo or a #1 Product Hunt launch that matches the user's stack or project is almost always worth mentioning.

Step 3: Search

For each high-priority watchlist topic (aim for 3-5 topics, no more than 8), perform a focused web search. Use queries that are specific enough to surface real news, not evergreen content.

Good search patterns:

  • "ByteDance" news March 3 2026 — catches employer news
  • 小荷 AI health news March 3 2026 — catches project news
  • "BABA stock" March 3 2026 — catches daily movers
  • "LangChain" release 2026 — catches recent releases
  • "Claude" OR "Anthropic" announcement — catches AI tooling updates

Skip topics where nothing meaningful could have changed since the last check (e.g., stable personal preferences, unchanging habits).

Step 4: Evaluate — The "Worth Interrupting" Test

Hard filter: Date verification (run this FIRST, before anything else)

For every item found in Step 2 or Step 3, you must verify its publication date by checking the article page or search result metadata. Record the date explicitly.

  • If the publication date is within the last 48 hours (T or T-1 relative to today): eligible, proceed to the Worth Interrupting test below.
  • If the publication date is older than 48 hours, or cannot be determined: discard immediately, do not include.

This is a hard gate — relevance does not override staleness. A perfectly relevant article from last week is still discarded. When in doubt about a date, discard.

Worth Interrupting test (only for items that passed the date filter)

For each remaining finding, apply this filter:

  1. Is it genuinely new? (not something the user already knows from memory or the previous briefing)
  2. Is it actionable or time-sensitive? (the user should do something, or the window to act is closing)
  3. Does it meaningfully change the picture? (not incremental noise, but a real shift)

A finding must pass at least 2 of 3 to be included.

Examples of WORTH reporting:

  • BABA drops 8% on earnings miss → actionable + time-sensitive
  • LangChain 2.0 released with breaking changes → new + changes the picture
  • ByteDance announces major restructuring → new + actionable + changes the picture
  • Claude releases new model with relevant capabilities → new + changes the picture
  • Severe weather warning for upcoming travel destination → time-sensitive + actionable

Examples of NOT worth reporting:

  • BABA moves 0.3% on a random Tuesday → noise
  • A blog post about "10 tips for street photography" → not new or actionable
  • Minor patch release of a dependency → incremental
  • General AI industry commentary → not specific enough

Step 5: Output

The evaluation process (date filtering, Worth Interrupting reasoning) is internal only — never show it to the user. Output starts directly with the briefing or [SKIPPED], nothing else.

If noteworthy findings exist:

Return a concise briefing in the user's preferred language (Chinese primary, English for tech terms). Format:

📡 Heartbeat | {date}

{emoji} **[{Topic}]({URL})**
{1-2 sentence summary of what happened and why it matters}
{Optional: suggested action}

{emoji} **[{Topic}]({URL})**
...

---
Sources: {URLs}

Keep it to 5 items max. Ruthlessly prioritize. The user should be able to read the entire briefing in under 60 seconds.

Emoji guide: 📈📉 for markets, 🔧 for tech/tools, 🏢 for career/employer, ⚠️ for alerts, 📸 for creative interests, 🗓️ for schedule/travel.

If nothing noteworthy:

Return exactly (without "```"):

[SKIPPED]

No explanation, no "I checked and found nothing interesting." Just [SKIPPED]. The system calling this skill knows what that means.

Important Notes

  • This skill is designed to be called programmatically (e.g., by a scheduler, cron job, or another agent). Keep output machine-parseable: either a formatted briefing or [SKIPPED]. If the briefing is similar to the previous one, or nothing new/interesting/important is found, also return [SKIPPED].
  • Err heavily on the side of [SKIPPED]. A heartbeat that returns noise trains the user to ignore it.
  • The search step should be fast — don't do deep research. Quick web searches only. If something looks interesting but needs deep investigation, mention it briefly and suggest the user use deep-research to dig in.
  • Respect the user's time of day if known. Financial news matters more during market hours. Tech releases matter more during work hours.

Frequently asked questions

What does the Heartbeat AI skill do?

Proactive intelligence pulse — scans user's memory files (USER.md, SOUL.md) to identify current interests, investments, projects, and concerns, then performs targeted web searches to find noteworthy updates. Returns a concise briefing if something deserves attention, or `[SKIPPED]` if nothing warrants interruption. Use this skill when the system needs to check if there's anything worth proactively notifying the user about — like a scheduled heartbeat check, a "what's new" sweep, or when the user asks "anything I should know?", "what did I miss?", or "any updates?". Also triggers on requests...

Why use Heartbeat on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/MagicCube/agentara/tree/main/user-home/.claude/skills/heartbeat. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use 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 Heartbeat?

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

Is the Heartbeat AI skill free?

It is published on GitHub by MagicCube. Check the repository for licensing terms. 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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