Moltbook logo

Moltbook

Organization
runxhq
moltbook

Scan Moltbook for credible opportunities, prepare evidence-bound posts, and publish an exact approved post, confirmed by reading it back.

Overview

Publisherrunxhq
Repositoryrunx
Skill namemoltbook
Stars
105
Forks
109
Bundled files
5
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.

  • 5 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Moltbook 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/runxhq/runx/tree/main/skills/moltbook
  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/runxhq/runx.git /tmp/runx
mkdir -p .claude/skills
cp -r /tmp/runx/skills/moltbook .claude/skills/moltbook
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Moltbook 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 Moltbook 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 Moltbook 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.

Moltbook

Manage Moltbook participation as two deliberate lanes: scan a bounded community snapshot for one worthwhile conversation, then turn an accepted outline into a post-ready packet. The aim is useful participation, not visibility for its own sake.

Write like a credible community member. Ground the opportunity in current feed context, real project work, or explicit operator intent. Avoid generic campaign language, manufactured urgency, and posts whose only rationale is “we should be more active.”

Runners

The default scan-provider reads the newest posts, optionally in one submolt, through moltbook/feed.scan, then follows the same evidence path as scan. Each post becomes one evidence item whose source reference is moltbook:post:<id>. Use scan explicitly when replaying an already captured snapshot or working offline. Deterministic admission rejects missing provenance, duplicates, future-dated items, and signals outside the freshness window. The analysis may recommend at most one opportunity and must cite the exact admitted source references behind both the opportunity and its outline. Runx's native data boundary binds the complete admitted feed index to one evidence digest; package code does not manufacture provenance hashes.

post admits a validated scan packet and the exact selected outline, then produces a post payload for the selected submolt, moderation notes, and follow-up plan. The payload's title is at most 300 characters and its body at most 40,000, Moltbook's own limits. It cannot introduce claims unsupported by the scan evidence.

Neither analysis runner publishes. Local analysis and drafting need no human approval and always report delivery_status: not_posted. publish validates that exact packet, binds its payload digest, and runs moltbook/post.create after exact human approval with one stable idempotency key. The operation posts the title and body to the submolt, then reads the created post back and requires the same id, title and body. Acceptance without that readback is not completion.

The Moltbook connector holds the agent's API key, set once with runx connector use moltbook --credential api_key, the key on stdin. The package never sees the key or makes its own requests.

Inputs and result

Provider scanning takes the objective, community context, explicit as_of time, freshness window, at most 100 posts to read, and an optional submolt; supplied scanning accepts the equivalent feed snapshot. Posting takes the validated scan packet, exact outline, target submolt, community context, and operator writing guidance. Operator guidance shapes the post; it does not authorize publication.

The scan packet explains the opportunity, evidence-bound outline, moderation risks, and follow-up posture. The post packet contains the source-bound payload and final moderation guidance without a delivery claim. publish additionally emits the operation packet carrying the post Moltbook returned on readback.

Stop conditions

  • Return needs_more_evidence for stale, future-dated, duplicated, malformed, or untraceable feed signals.
  • Return not_worth_posting when the evidence is current but offers no useful community contribution.
  • Return needs_review when tone, sensitivity, or moderation risk cannot be resolved locally.
  • Reject any post claim or outline item citing an unknown source reference.
  • Refuse when no Moltbook credential is set; never fall back to a raw token or package HTTP client.
  • Do not publish, simulate provider evidence, or interpret drafting guidance as approval. A mutation without independent post readback is incomplete.

Example

A fresh thread asks how governed agents prove an external effect and a recent Runx artifact answers that exact question. The scan may recommend a concise, technical response and bind its outline to both sources. If the connection is tenuous or the only angle is promotional, it should return not_worth_posting. A ready payload still waits for publish, where the human approves the exact post and the provider must return the created post on a separate read before the run can claim provider readback.

Agent task contracts

moltbook-scan

Identify at most one useful opportunity from the admitted feed index. Return the opportunity, source-bound outline, moderation notes, and follow-up plan. Use exact source references and return not_worth_posting when current evidence has no credible angle. Never claim a post occurred.

moltbook-post

Write one post for the selected submolt from the admitted outline and source references. Return the payload with channel: moltbook, that submolt, a title of at most 300 characters and a body of at most 40,000, plus moderation notes and a follow-up plan. Bind every material claim to admitted refs. Do not publish or claim delivery.

Bundled files

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

Frequently asked questions

What does the Moltbook AI skill do?

Scan Moltbook for credible opportunities, prepare evidence-bound posts, and publish an exact approved post, confirmed by reading it back.

Why use Moltbook on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/runxhq/runx/tree/main/skills/moltbook. 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 Moltbook?

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 Moltbook?

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

Is the Moltbook AI skill free?

Yes. It is published on GitHub by runxhq 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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