Content Package

Organization

runxhq

content-package

Turn governed source evidence into a citation-bound reader-facing draft, channel package, and provider-neutral publication handoff.

Overview

Publisher	runxhq
Repository	`runx`
Skill name	`content-package`
Stars	105
Forks	109
Bundled files	4
License	Apache-2.0
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.
4 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 Content Package 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/runxhq/runx/tree/main/skills/content-package
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/runxhq/runx.git /tmp/runx
mkdir -p .claude/skills
cp -r /tmp/runx/skills/content-package .claude/skills/content-package

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

Use it in TypingMind

Enable Content Package 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 Content Package 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 Content Package 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.

Content Package

This is the standard preparation lane for researched content. It keeps evidence, writing, packaging, and external delivery distinct so an operator can inspect one concrete artifact and no local success is mistaken for publication.

Use it for articles, updates, newsletters, explainers, and other channel-bound content whose substantive claims need traceable support. The result should do something for the reader—clarify a decision, explain a change, establish trust, or enable a concrete next step—not merely prove that research happened.

Composes

ghostwrite#draft
ghostwrite#handoff
ghostwrite#package
research#research

How the chain works

The caller provides the objective, audience, channel, and governed source packets. Usually those packets come from web-fetch or a provider reader.
research admits the sources, verifies citations, and decides whether the evidence supports a useful deliverable.
ghostwrite drafts only from the admitted claims and digests, applying any supplied voice or operator context without treating it as factual evidence.
The validated draft is packaged deterministically for the declared channel.
A provider-neutral handoff records the target and approval requirement. The lane stops before external delivery.

Local research, drafting, and packaging require no approval. A later provider skill owns the consequential gate, idempotency, send or publish request, acknowledgement, and readback.

Inputs and result

objective states what the content should change for its reader.
source_packets contain bounded governed evidence.
audience, channel, and domain shape the artifact.
operator_context and target_entities narrow interpretation but are not source evidence.
publication_target and boundary_kind (internal or external) prepare the next lane; they do not authorize it. An external delivery needs the delivering lane's own approval.

The result preserves the citation-validated research packet, the evidence-bound draft, a deterministic channel package with not_sent state, and a provider-neutral handoff. needs_more_evidence, needs_review, and not_worth_publishing are valid terminal states when the topic is unsupported, stale, duplicative, or unhelpful.

Stop conditions

Stop before drafting when the source packet is missing or invalid.
Do not turn operator context, campaign intent, or a target URL into evidence.
Withhold any draft whose material claim cannot be tied to an admitted digest.
Do not add delivery or publication evidence to a local package.
Route the exact accepted artifact into the relevant provider lane instead of rebuilding it there.

Example

An operator wants a blog post explaining a new governance boundary. The lane can validate official docs and receipt evidence, draft for engineering readers, package the accepted body for the blog, and prepare a CMS handoff. It cannot say the post is live until an approved CMS operation and provider readback prove it.

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 Content Package AI skill do?

Turn governed source evidence into a citation-bound reader-facing draft, channel package, and provider-neutral publication handoff.

Why use Content Package on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/runxhq/runx/tree/main/skills/content-package. 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 Content Package?

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 Content Package?

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

Is the Content Package 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Content Package

Composes

How the chain works

Inputs and result

Stop conditions

Example

Bundled files

Frequently asked questions

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