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Homerail Shared

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xiaotianfotos
homerail-shared

Shared HomeRail rules for AI agents operating the local-source release candidate: service roles, environment variables, secrets, provider boundaries, Docker callback networking, update expectations, and safe command behavior. Use whenever installing, configuring, upgrading, troubleshooting, or running HomeRail through other HomeRail skills.

Overview

Publisherxiaotianfotos
Repositoryhomerail
Skill namehomerail-shared
Stars
995
Forks
224
Bundled files
1
LicenseMIT
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Homerail Shared 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/xiaotianfotos/homerail/tree/main/skills/homerail-shared
  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/xiaotianfotos/homerail.git /tmp/homerail
mkdir -p .claude/skills
cp -r /tmp/homerail/skills/homerail-shared .claude/skills/homerail-shared
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Homerail Shared 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 Homerail Shared 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 Homerail Shared 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.

HomeRail Shared

Core Rules

Use these rules with all HomeRail skills.

  • Treat this release as a local-source install. Do not assume npm packages or public GitHub URLs exist yet.

  • Prefer placeholders such as <local-homerail-repo> and /path/to/HomeRail in user-facing instructions.

  • Keep provider and integration secrets in the Manager encrypted secret store through CLI/UI configuration; never write API keys, tokens, or passwords into repo files, skill files, DAG templates, commits, or issue text.

  • Use one shared HOMERAIL_HOME for Manager and Node. Default is $HOME/.homerail.

  • Prefer hr config for local runtime settings and hr start to start Manager and Node together.

  • Manager and Node run as local services. Manager is not expected to run inside the Worker Docker image.

  • Node provisions Worker containers. Workers for one run should share ${HOMERAIL_HOME}/workspace/<run_id>.

  • Local service ports (use this table and the CLI before falling back to low-level process probes):

    PortRoleDefault URLNotes
    19191Manager (HTTP + WS, what DAGs and the CLI talk to)http://localhost:19191This is the only port a DAG run / CLI hits. Bind via HOMERAIL_MANAGER_HOST / manager.host / hr start --host; public access via HOMERAIL_MANAGER_PUBLIC_URL / hr start --public-url.
    19192Agent UI (HTTPS, browser)https://localhost:19192Browser-only. Do NOT send DAG/CLI traffic here. Config via HOMERAIL_UI_HOST / HOMERAIL_UI_PORT / ui.host / ui.port.
    19193Agent UI HTTP fallbackhttp://localhost:19193Fallback when HTTPS port is taken. Config via HOMERAIL_UI_HTTP_PORT / ui.httpPort.

    The Manager port is 19191, not 19192. 19192 is the Agent UI. Mixing these up is the most common startup mistake.

  • For hr start --ui --public without explicit public URLs, Agent UI should bind and advertise the detected machine IP with HTTPS, and the browser should use same-origin Manager API/WS proxying for the HTTPS UI. Use HOMERAIL_PUBLIC_HOST only when automatic machine IP detection picks the wrong interface.

  • For reverse-proxied public access, prefer explicit HTTPS/WSS public URLs through HOMERAIL_MANAGER_PUBLIC_URL / HOMERAIL_UI_PUBLIC_URL or --public-url / --ui-public-url.

  • Do not hardcode Docker bridge addresses. Use Docker host.docker.internal / host-gateway support or explicit operator-provided callback settings.

Provider Boundaries

For Coding Plan / Agent Plan accounts:

  • Never use direct-llm or Chat Completions as the worker runtime path.
  • Kimi plans should use the Kimi Code CLI harness (kimi_code / kimi-code).
  • Other provider plans should use the Claude Code compatible harness (claude-sdk) with an Anthropic-compatible endpoint.
  • If a plan does not expose an Anthropic-compatible endpoint, fail explicitly instead of falling back to the Chat Completions URL.

For MiMo:

  • DAG worker LLM access uses the MiMo token-plan Anthropic-compatible endpoint.
  • MiMo ASR/API-billing endpoints are a separate voice path and must not be mixed into DAG worker model configuration.
  • If a user provides a provider key, configure it with hr model configure --api-key-stdin or the interactive CLI/UI flow so Manager stores it encrypted. Plaintext env files are legacy import fallbacks only.

Local Source Layout

Expected source checkout:

text
<local-homerail-repo>/
  homerail_manager/
  homerail_node/
  homerail_worker/
  homerail_protocol/
  homerail_cli/
  agent-ui/
  assets/
  skills/

Use homerail_cli for the TypeScript CLI. Do not refer to the old homerail_cli_ts name.

Anti-Patterns (Don'ts)

These cause wasted investigation rounds during normal operation. The CLI already exposes everything below.

  • Do not start service diagnosis with raw process probes or source spelunking. Run hr doctor (readiness) and hr runtime status (live state) first; they report manager/node/worker/model state. Use ps, lsof, direct curl, logs, or source reading only after the CLI output is insufficient or the user explicitly asks for low-level debugging.
  • Do not start a fixed host Worker process. DAG Workers are provisioned on demand by a Node into Docker containers; there is no standing worker to launch. Starting a bare homerail_worker node process manually will not register correctly and bypasses the isolated-workspace contract every DAG depends on.
  • Do not hand-craft WebSocket URLs or reverse-engineer the worker connection protocol. hr start handles Manager↔Node↔Worker wiring. If a Worker cannot reach the Manager, set the Manager worker-WS callback host (see homerail-install-ops troubleshooting), do not hardcode Docker bridge IPs.
  • Do not rebuild packages before every DAG run. install:all / build / typecheck / test are only needed after changing source or on first install. A ready runtime runs DAGs directly via hr run.
  • Do not read dag_runs/dag_handoffs tables via sqlite3 to get run output. Use hr dag handoffs <run_id> (with --content-limit 0 for untruncated content). The CLI is the supported read path.
  • Do not treat an idle or running snapshot as success. Terminal status (completed/failed/cancelled) plus non-empty handoffs is the only success signal.

Validation Ladder

When validating a local install or change, prefer this order:

bash
npm run install:all
npm run build
npm run typecheck
npm test
npm run ci

For end-to-end readiness, also run hr config, hr start, hr doctor, and one public smoke DAG through homerail-install-ops.

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 Homerail Shared AI skill do?

Shared HomeRail rules for AI agents operating the local-source release candidate: service roles, environment variables, secrets, provider boundaries, Docker callback networking, update expectations, and safe command behavior. Use whenever installing, configuring, upgrading, troubleshooting, or running HomeRail through other HomeRail skills.

Why use Homerail Shared on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/xiaotianfotos/homerail/tree/main/skills/homerail-shared. 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 Homerail Shared?

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 Homerail Shared?

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

Is the Homerail Shared AI skill free?

Yes. It is published on GitHub by xiaotianfotos 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.

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