Homerail Install Ops logo

Homerail Install Ops

Community
xiaotianfotos
homerail-install-ops

Install, configure, start, verify, update, and troubleshoot a local-source HomeRail deployment for AI-agent use. Use when a user wants to deploy HomeRail from a local checkout or local Git address, install HomeRail skills, set up Manager/Node/Worker services, configure provider credentials, run doctor/smoke, or repair a broken local install before npm publication exists.

Overview

Publisherxiaotianfotos
Repositoryhomerail
Skill namehomerail-install-ops
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 Install Ops 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-install-ops
  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-install-ops .claude/skills/homerail-install-ops
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Homerail Install Ops 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 Install Ops 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 Install Ops 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 Install Ops

Before acting, apply homerail-shared rules. This pre-publication flow uses a local source checkout or local Git clone, not npm publication.

Local Skill Install

Install HomeRail skills by linking the checkout's skills/homerail-* directories into the target agent skill directory. Prefer symlinks over copies so skill updates follow git pull.

If the source tree is already present, use skills/README.md as the source of truth for Codex and Claude Code install targets. On macOS/Linux:

bash
repo=/path/to/HomeRail

for root in "${CODEX_HOME:-$HOME/.codex}/skills" "$HOME/.claude/skills"; do
  mkdir -p "$root"
  for skill in "$repo"/skills/homerail-*; do
    [ -d "$skill" ] || continue
    name="$(basename "$skill")"
    dst="$root/$name"
    if [ -L "$dst" ]; then
      rm "$dst"
    elif [ -e "$dst" ]; then
      echo "Refusing to replace existing non-symlink skill: $dst" >&2
      continue
    fi
    ln -s "$skill" "$dst"
  done
done

If the user gives a local Git address, clone it first, then install links from the checkout:

bash
git clone <local-homerail-git-url> HomeRail
cd HomeRail

On Windows, use PowerShell directory symlinks when Developer Mode or Administrator permissions allow them. If symlinks are blocked, use a junction as the local fallback; repeat the junction for every skills/homerail-* directory that should be installed:

powershell
cmd /c mklink /J "%USERPROFILE%\.claude\skills\homerail-install-ops" "C:\path\to\HomeRail\skills\homerail-install-ops"

After installing or updating skills, ask the user to restart the AI agent session if their agent host only loads skills on startup.

After restart, route user requests to the root skills this way:

  • Use homerail-install-ops for first install, update, service startup, provider configuration, and smoke verification. Codex invocation: $homerail-install-ops; Claude Code invocation: /homerail-install-ops.
  • Use homerail-dag-ops for the full DAG lifecycle: pattern selection, workflow authoring, execution, background supervision, decisions and output inspection. Its references cover advanced modes; no separate supervision skill is needed.
  • Use homerail-cli for command syntax, flags, and evidence commands.
  • Use homerail-shared as background rules for all HomeRail operations.

Deploy From Local Source

Run from the source root:

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

Prepare a local data root:

bash
export HOMERAIL_HOME="${HOMERAIL_HOME:-$HOME/.homerail}"

Configure local runtime settings and credentials through the CLI:

bash
node homerail_cli/dist/cli.js config

Start Manager and Node together:

bash
node homerail_cli/dist/cli.js start
node homerail_cli/dist/cli.js start --rebuild-worker-image
node homerail_cli/dist/cli.js start --ui
node homerail_cli/dist/cli.js doctor

hr start uses the shared HOMERAIL_HOME, starts Manager if needed, applies stored model config, builds homerail-worker:latest when missing, and starts a Node with Docker capability when no configured Node is connected. Use --rebuild-worker-image after Worker runtime changes so provisioned DAG workers run the current checkout. Use --ui or node homerail_cli/dist/cli.js ui start to start the decoupled Agent UI. HTTPS is the default endpoint, https://localhost:19192, and HTTP is kept as a fallback on http://localhost:19193. If provider credentials are not configured yet, hr start should still bring up Manager and Node; hr doctor reports the missing model or Manager Agent runtime configuration and the agent should run hr config / hr model configure.

Use node homerail_cli/dist/cli.js runtime status|logs|stop for service inspection and cleanup. Use node homerail_cli/dist/cli.js ui status|logs|stop for UI-only inspection and cleanup.

hr doctor is the first environment probe an agent needs for normal operation. Do not supplement it with ps, lsof, curl /health, or source reading unless the CLI output is insufficient for a concrete failure. It reports, per line: manager reachability + URL, runtime state (connected nodes/workers, active runs, node capabilities), node count, any active LLM model setting, and the Manager Agent runtime/harness readiness. A HomeRail is ready. trailer is the readiness gate; any FAIL/WARN line names the exact next command to run (e.g. hr model configure).

connected_workers: 0 is expected on an idle Manager. DAG Workers are provisioned on demand by a Node into Docker containers when a run dispatches; there is no standing worker to inspect or start by hand. connected_nodes >= 1 is what matters for DAGs to run.

The local harness deployment diagnosis DAG runs through the same Worker provisioning path as normal DAGs. Do not start a fixed host Worker, mount the Docker socket into a Worker, or use Docker-in-Docker for this test. The diagnosis run uses its isolated workspace, clones the latest source, builds it, configures the cloned CLI to talk to the outer Manager, and verifies runtime status / doctor. Deployment diagnosis uses advisory scorecard enforcement; inspect findings, but use terminal status and structured handoff evidence as the deployment result.

Configure Provider

For MiMo token-plan Claude Agent SDK compatibility:

bash
node homerail_cli/dist/cli.js config
node homerail_cli/dist/cli.js config apply
node homerail_cli/dist/cli.js doctor

Do not put the key into repo files. The config flow stores secrets under Manager's encrypted secret store. ${HOMERAIL_HOME}/secrets/env is a legacy plaintext import path only. Do not use MiMo ASR/API-billing endpoints for DAG workers.

Smoke Test

Run the public smoke DAG:

bash
node homerail_cli/dist/cli.js smoke dag \
  --template assets/orchestrations/public-dev-5node.yaml.template

Then verify the configured Manager Agent harness can call Manager tools and start a real run:

bash
node homerail_cli/dist/cli.js smoke manager-agent

This is the live Manager Agent gate. It uses the saved Manager Agent config, calls /api/manager/chat, requires a real run_id, and waits for the deterministic two-node DAG to reach completed. Use --setting-id, --provider, or --model when validating a specific Kimi Code or Claude SDK-compatible runtime.

When it completes, inspect:

bash
node homerail_cli/dist/cli.js dag quick <run_id> --events 30
node homerail_cli/dist/cli.js dag chats <run_id> --tools 10
node homerail_cli/dist/cli.js dag handoffs <run_id>
node homerail_cli/dist/cli.js scorecard <run_id>
node homerail_cli/dist/cli.js eval-run <run_id>

Do not treat an idle-looking process as success until terminal status, handoffs, scorecard, and eval-run are checked.

Maintenance

  • Keep installed skills as symlinks to the checkout. After skill edits, verify the target agent skill directory still points at skills/homerail-*; do not refresh by copying stale directories.
  • Re-run npm run build after code changes.
  • Run node homerail_cli/dist/cli.js start; add --rebuild-worker-image after Worker runtime changes.
  • Keep Manager and Node on the same HOMERAIL_HOME.
  • If Worker containers cannot connect back to Manager, set an explicit callback host through Manager worker WebSocket environment settings rather than hardcoding Docker bridge IPs.

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 Install Ops AI skill do?

Install, configure, start, verify, update, and troubleshoot a local-source HomeRail deployment for AI-agent use. Use when a user wants to deploy HomeRail from a local checkout or local Git address, install HomeRail skills, set up Manager/Node/Worker services, configure provider credentials, run doctor/smoke, or repair a broken local install before npm publication exists.

Why use Homerail Install Ops on TypingMind?

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

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

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 Install Ops?

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

Is the Homerail Install Ops 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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