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Messaging The Human

CommunityPopular
mvschwarz
messaging-the-human

Use when project policy calls for a human decision or update, a human delivery is pending or failed, or a reply must resume the right work.

Overview

Publishermvschwarz
Repositoryopenrig
Skill namemessaging-the-human
Stars
5K
Forks
366
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 mvschwarz on GitHub. Read the source before you install it.

Installation

Install the Messaging The Human 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/mvschwarz/openrig/tree/main/packages/daemon/assets/plugins/openrig-core/skills/messaging-the-human
  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/mvschwarz/openrig.git /tmp/openrig
mkdir -p .claude/skills
cp -r /tmp/openrig/packages/daemon/assets/plugins/openrig-core/skills/messaging-the-human .claude/skills/messaging-the-human
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Messaging The Human 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 Messaging The Human 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 Messaging The Human 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.

Messaging the Human

Project World supplies when and why to contact a human. This skill supplies transport-neutral mechanics; installing it does not create an approval gate or choose a connector. If policy leaves a material decision ambiguous, identify the missing authority. Do not convert a solvable technical failure into a human gate.

Discover, check, send, inspect

Discover the registered participants and inspect the chosen human:

bash
rig gateway human list --json
rig gateway human show <entityId> --json

Use the returned address (<entityId>@external), not a username, remembered seat, connector handle, or guessed kernel address. Where several humans exist, use the decision ownership in Project World. An absent or ambiguous registration needs a named registration correction, not a fallback address.

Check readiness: configured, enabled, active, ready, reason, and next action. indeterminate is not ready. Follow the reported next inspection; do not enable or reconfigure a connector merely to make the check pass.

Author for the person reading on a phone: a short subject in --summary, then one complete brief in --body-file. State why it matters, your recommendation and material tradeoff, the bounded action if approved, and the choice requested. For an update, state the user-visible outcome and “No action needed.” Aim for roughly 100–150 words; this is guidance, not a semantic validator. Keep technical continuation, exact candidate/revision and evidence on the owning agent row and in the durable artifact behind --evidence-ref. A local path is not a phone link and Markdown evidence files are not automatically attached.

For example, a synthetic brief could say:

The repaired status view is ready. I recommend updating this instance; live status will briefly pause. Sessions will be preserved. Approve this instance update, or hold? Supporting test detail follows in this thread.

This example grants no authority. Choose --human-intent decision for a request or --human-intent update for a quiet FYI. Omission retains legacy decision behavior; words such as “FYI” and tags do not change intent.

The sole outbound human-message primitive is:

bash
rig queue create --destination <entityId>@external \
  --human-intent decision --summary "<short subject>" --body-file <brief-file> \
  --evidence-ref <durable-evidence> --verify --json

An optional --human-detail-file <path> supplies one coherent supplemental reply in the same thread. Announce its purpose in the brief; the product also marks that a detail reply follows. The primary must already contain the complete scope, options and action. Do not split an agent dump blindly or move the essential choice into overflow. Rendering checks every part and its accessibility fallback before posting; an oversized request is refused with a field-specific correction, never silently clipped. Shorten the brief or related detail as directed. Inspect the failed row, then deliberately cancel/replace the authored request if its content needs correction; a timeout alone is never a reason to replace it.

A follow-up update about earlier work ("the change you approved is merged") can post into that item's thread with --reply-to <earlier-qitem-id>. It is accepted only with --human-intent update. Name the item whose thread the human saw, such as the parked row, and create the update on the same host as that item. Send the update from the seat that owns that thread: the seat that parked the row, or the author of the earlier item. A human reply in a thread reaches its owning seat, so an update from any other seat posts as a new message. It also posts as a new message, rather than being refused, if the earlier thread is missing or closed, or while the earlier item still waits on the human (a pending human decision, or a row parked on the human), since a reply in that thread would answer the decision. In every such case the --verify result says threaded: false with the reason.

A decision with a few clear choices can carry --human-questions-file <path>: a JSON array of 1–4 questions, each {"id", "question", "options": [{"id", "label", "recommended"?}]} with 2–4 options (labels up to 75 characters, at most one recommended). Slack shows each question as a row of buttons. Each click records that answer on the item, and the decision resolves once every question has one. You then receive one reply row listing the answers, and the item's humanAnswers holds the option ids. The human may instead type a reply in the thread; that resolves the decision as usual, so read the reply rather than assuming an option was picked. Keep the brief complete: the questions add buttons, they do not replace the explanation.

If an existing agent-owned row must wait for a decision, block it on the new live qitem ID (rig queue block <work-id> --on <human-qitem-id> ...), not on the human address. Completion of the human qitem resumes its dependants. Blocking on the human as well would issue another notification for the same decision.

The row persists before bounded delivery verification. Read its qitem ID and verification result; posted proves connector posting, not human readership. transport-failed, never-posted, or a pending/indeterminate result leaves the row intact. Inspect that same row and its next action; never create a second row or blindly resend because verification timed out.

bash
rig queue transitions <qitem-id>

For update, confirmed complete delivery may close the delivery obligation. It creates no approval obligation and cannot be used as a decision blocker. Delivered updates remain queryable for Feed; a failure or ambiguous send stays separate. A root message alone does not prove supplemental delivery. Retries reconcile stable part identities and send only missing parts. An FYI reply is not a human decision.

For a decision, a correlated reply binds to that exact human and qitem and records the resolution that resumes the owner. Check the recorded result before claiming the decision arrived; a delivery receipt alone is not acceptance.

Existing blockers and other channels

An existing agent-owned row may be blocked on <entityId>@host. That is an internal custody label resolved through the human registry to the same external participant; it is not a second delivery address. Keep the owner and continuation on that row. Inspect its existing delivery receipt before considering another request, so a legacy blocker does not produce a duplicate message. Never derive @host from the current rig name.

rig send reaches an agent's terminal only. It is not a human transport or a durable human obligation. Agent-to-agent work uses the queue handoff path. Connector-specific configuration and handles belong to registry/readiness tools, not to project-independent message instructions.

Frequently asked questions

What does the Messaging The Human AI skill do?

Use when project policy calls for a human decision or update, a human delivery is pending or failed, or a reply must resume the right work.

Why use Messaging The Human on TypingMind?

Because you install it once and use it with any model. Messaging The Human 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 Messaging The Human in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mvschwarz/openrig/tree/main/packages/daemon/assets/plugins/openrig-core/skills/messaging-the-human. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Messaging The Human?

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 Messaging The Human?

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

Is the Messaging The Human AI skill free?

Yes. It is published on GitHub by mvschwarz 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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