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Setup Customize

Community
dcb
setup-customize

Run after setup-infrastructure to map rooms, entities, and preferences to the dashboard and automation templates. Conversational and resumable. Trigger phrases: "customize my home", "set up rooms", "configure automations", "map my entities", "set up my dashboard", "finish setup", "resume setup".

Overview

Publisherdcb
Repositoryhomeassistant-claude-kit
Skill namesetup-customize
Stars
125
Forks
22
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 dcb on GitHub. Read the source before you install it.

Installation

Install the Setup Customize 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/dcb/homeassistant-claude-kit/tree/main/.claude/skills/setup-customize
  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/dcb/homeassistant-claude-kit.git /tmp/homeassistant-claude-kit
mkdir -p .claude/skills
cp -r /tmp/homeassistant-claude-kit/.claude/skills/setup-customize .claude/skills/setup-customize
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Setup Customize 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 Setup Customize 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 Setup Customize 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.

Setup Customize

This skill maps your Home Assistant instance to the dashboard and automation templates through a guided interview. It is resumable — if the conversation ends mid-way, re-invoke this skill and it will pick up from the last checkpoint.

See references/question-patterns.md for detailed question wording and example answers for each domain.

Step 0: Check Prerequisites

Verify setup-state.json exists and infrastructure is complete:

python
import json, sys, os
if not os.path.exists('setup-state.json'):
    print('NOT_READY'); sys.exit(0)
with open('setup-state.json') as f:
    state = json.load(f)
schema = state.get('schema_version', 0)
if schema > 1:
    print('SCHEMA_WARNING')
phase = state.get('session', {}).get('current_phase', '')
infra = state.get('infrastructure', {}).get('steps_completed', [])
if 'infrastructure_complete' in phase or 'pull' in infra:
    answers = state.get('answers', {})
    if answers.get('rooms') or phase.startswith('customize:'):
        print('RESUME')
        print(f'PHASE:{phase}')
        print(f'ROOMS_DONE:{",".join(answers.get("rooms", {}).keys())}')
    else:
        print('FRESH')
else:
    print('NOT_READY')

Run via python3 -c "..." and check the output:

  • NOT_READY: Tell user to run setup-infrastructure first.
  • SCHEMA_WARNING: State file from newer version — proceed with caution.
  • RESUME: Load checkpoint. Tell user: "Welcome back! You were at [phase]. Rooms done: [list]. Continuing."
  • FRESH: Begin from Phase 1.

Checkpoint Writing Pattern

After EVERY user answer, update setup-state.json with granular progress:

python
import json
def save_checkpoint(phase, answers_update=None, files_written=None):
    with open('setup-state.json') as f:
        state = json.load(f)
    state['session']['current_phase'] = phase
    if answers_update:
        state.setdefault('answers', {}).update(answers_update)
    if files_written:
        state.setdefault('files_written', []).extend(files_written)
    with open('setup-state.json', 'w') as f:
        json.dump(state, f, indent=2)

Example calls:

  • save_checkpoint('customize:room_mapping', {'rooms': {'living_room': {'light': '...', 'motion': '...'}}})
  • save_checkpoint('customize:domain_selection', {'domains_selected': ['lighting', 'climate']})
  • save_checkpoint('customize:notifications', {'notify_targets': {'primary': 'notify.mobile_app_x'}})
  • save_checkpoint('customize:files', files_written=['config/automations/lighting.yaml'])

Step 1: Discover Entity + Area + Floor Registries

Primary method: Use registry data. Entity-to-room assignment should come from the device/entity registries (via area_id) whenever possible. This is the authoritative source.

Fallback: Name inference + user confirmation. If the registries have sparse area assignments (common in setups where the user hasn't organized areas in HA), you may infer room assignments from entity ID naming patterns (e.g., bedroom_motion → bedroom). However, when using name inference, you MUST:

  1. Clearly mark inferred assignments as "inferred (not in registry)"
  2. Ask the user to confirm ALL inferred assignments before proceeding
  3. Never present inferred data as verified fact

1a. Query Floor + Area Registries

Get the authoritative room and floor structure:

bash
source .env && ssh "$SSH_USER@$HA_HOST" "source /etc/profile.d/claude-ha.sh; source ${HA_REMOTE_PATH:=/config/}.env; ha-ws raw config/floor_registry/list" 2>/dev/null
source .env && ssh "$SSH_USER@$HA_HOST" "source /etc/profile.d/claude-ha.sh; source ${HA_REMOTE_PATH}.env; ha-ws raw config/area_registry/list" 2>/dev/null

This gives you:

  • All floors with IDs and names
  • All areas with floor_id assignments
  • Do NOT ask the user about floors if this data is available.

1b. Query Device + Entity Registries

Get the authoritative entity-to-area mappings:

bash
source .env && ssh "$SSH_USER@$HA_HOST" "source /etc/profile.d/claude-ha.sh; source ${HA_REMOTE_PATH}.env; ha-ws raw config/device_registry/list" 2>/dev/null
source .env && ssh "$SSH_USER@$HA_HOST" "source /etc/profile.d/claude-ha.sh; source ${HA_REMOTE_PATH}.env; ha-ws raw config/entity_registry/list" 2>/dev/null

Entity-to-area resolution chain:

  1. Check entity_registry → if the entity has a direct area_id, use it
  2. Otherwise, find the entity's device_id → look up that device in device_registry → use the device's area_id
  3. If neither has an area_id, the entity is unassigned — note it but do NOT guess

1c. Query Entities by Domain

For each relevant domain, query the live entity list:

bash
source .env
for domain in light binary_sensor sensor climate media_player camera cover vacuum remote switch; do
  ssh "$SSH_USER@$HA_HOST" "source /etc/profile.d/claude-ha.sh; source ${HA_REMOTE_PATH}.env; ha-ws entity list $domain" 2>/dev/null
done

1d. Build Verified Room-Entity Map

Cross-reference the entity list with the device/entity registry area assignments to build a verified mapping. For each room, list:

  • Lights (prefer zone/group entities over individual bulbs)
  • Motion sensors (binary_sensor.* with device_class: motion or occupancy)
  • Temperature sensors
  • Climate entities (TRVs, AC units)
  • Media players
  • Cameras

Before presenting any mapping to the user: mark each assignment's source:

  • Registry: directly from device/entity registry area_id — present as fact
  • Inferred: from entity ID naming pattern — present with a ? mark, ask user to confirm
  • Unassigned: no area in registry and no clear naming pattern — ask the user

1e. Fallback: Local .storage Files

If SSH/ha-ws is unavailable, parse the local .storage/ files (pulled by make pull):

bash
source venv/bin/activate && python tools/entity_explorer.py --full 2>/dev/null | head -100

Or use the REST API as a last resort:

bash
source .env && set -a && source .env && set +a && python3 -c "
import urllib.request, json, os
url = os.environ['HA_URL'] + '/api/states'
req = urllib.request.Request(url, headers={'Authorization': 'Bearer ' + os.environ['HA_TOKEN']})
with urllib.request.urlopen(req) as r:
    states = json.load(r)
domains = {}
for s in states:
    d = s['entity_id'].split('.')[0]
    domains[d] = domains.get(d, 0) + 1
for d, c in sorted(domains.items()):
    print(f'{d}: {c} entities')
"

Summarize what was found: "Found X floors, Y areas, Z lights, W climate entities, ..."

Step 2: Room Mapping (Phase 1)

See references/question-patterns.md → Phase 1 for question wording.

Goal: Build a RoomConfig[] array for dashboard/src/lib/areas.ts.

  1. Present the verified room-entity map from Step 1d to the user. This should already include floor assignments (from the floor registry), entity assignments (from device/entity registries), and all detected sensors/lights/climate/media per room.
  2. Ask the user to confirm, correct, or skip each room. Common corrections:
    • Merging rooms (e.g., kitchen + storage → one zone)
    • Renaming rooms for the dashboard
    • Skipping rooms they don't want on the dashboard
  3. Only ask about floors if the floor registry returned no data. If floors are assigned in HA, use those values directly.
  4. For each confirmed room, verify entity assignments match what the user expects. If any entity was listed as "unassigned" in Step 1d, ask the user to assign it.
  5. Save progress to setup-state.json after each room confirmation.

Generate areas.ts once all rooms are confirmed:

typescript
// dashboard/src/lib/areas.ts — generated by setup-customize
export interface RoomConfig {
  id: string;
  name: string;
  floor: number;
  icon: string;
  light?: string;           // primary light entity
  motionSensor?: string;
  temperatureSensor?: string;
  mediaPlayer?: string;
  climate?: string;
}

export const ROOMS: RoomConfig[] = [
  // REPLACE: Add your rooms here (generated from interview)
  // { id: "living_room", name: "Living Room", floor: 0, icon: "sofa", light: "light.living_room" },
];

// Maps HA person entity → display name
export const USER_ROOM_MAP: Record<string, string> = {};

Step 3: Entity Specialization (Phase 2)

For each room, ask domain-specific questions:

Lights:

  • Is the main light a Hue zone/group or individual bulbs?
  • Any motion-triggered lights in this room? (entity ID)
  • Luminance sensor? (for light-level gating)

Climate:

  • Thermostat/TRV or AC unit?
  • TRV entity ID (for zone control)

Media:

  • TV / media player entity?
  • Remote entity? (for IR/HDMI control)

Save answers to setup-state.json as you go.

Step 4: Domain Selection (Phase 3)

Present automation domains as a checklist. Ask the user which apply to their setup:

Which automation domains do you want to set up?
□ Motion lights (auto on/off with motion sensors)
□ Activity modes (night mode, movie mode, work mode)
□ Climate scheduling (morning/night temperature changes)
□ Away mode (setback when nobody home)
□ Appliance tracking (washer/dishwasher state machine)
□ Health monitoring (integration watchdogs, battery alerts)
□ EV/Solar charging (if you have solar + EV)
□ AC solar heating (if you have solar + AC units)
□ None — I'll write my own automations

For each selected domain, note which automation template to use from docs/templates/config/automations/.

Step 5: Behavioral Interview (Phase 4)

Ask about preferences that drive automation behavior. See references/question-patterns.md → Phase 4 for full question bank.

Key questions:

  • What time do you typically wake up on weekdays? Weekends?
  • What time is bedtime on weekdays? Weekends?
  • Who lives in the home? (for presence tracking — no custody/schedule details needed)
  • Do you work from home? (drives work_mode auto-trigger)
  • What's your preferred daytime temperature? Night temperature?
  • Battery alert threshold? (default: 10%)
  • Any devices that should NOT be automated? (creates exceptions list)

Save all answers to setup-state.json.

Step 6: Notification Discovery (Phase 5)

Discover available notification targets:

bash
set -a && source .env && set +a && python3 -c "
import urllib.request, json, os
url = os.environ['HA_URL'] + '/api/services'
req = urllib.request.Request(url, headers={'Authorization': 'Bearer ' + os.environ['HA_TOKEN']})
with urllib.request.urlopen(req) as r:
    services = json.load(r)
notify = [s for s in services if s.get('domain') == 'notify']
for n in notify:
    for svc in n.get('services', {}).keys():
        print(f'notify.{svc}')
" 2>/dev/null

Alternatively, use SSH + ha-api (more reliable):

bash
source .env && ssh "$SSH_USER@$HA_HOST" "source /etc/profile.d/claude-ha.sh; source ${HA_REMOTE_PATH:=/config/}.env; ha-api search notify"

Ask the user which targets to use for:

  • Primary notifications (most alerts)
  • Critical alerts (security, health)

Step 7: Helpers Merge

Read existing configuration.yaml and check if it already has input_* helpers:

bash
grep -l "input_boolean:\|input_select:\|input_number:" config/configuration.yaml 2>/dev/null && echo "has_helpers" || echo "no_helpers"

If existing helpers found: Show them and ask:

"Your configuration.yaml already has input helpers. I can: (A) Keep them where they are and add only missing ones from the templates (B) Consolidate all helpers into config/helpers.yaml and use !include helpers.yaml

Which do you prefer?"

Never silently move or overwrite existing helpers.

Step 8: Generate Configuration Files

Based on all interview answers, generate:

dashboard/src/lib/entities.ts

typescript
// dashboard/src/lib/entities.ts — generated by setup-customize
// Edit this file to update entity mappings. Re-run setup-customize to regenerate.

// ── Modes ──────────────────────────────────────────────────────────────────
export const NIGHT_MODE = "input_boolean.night_mode";
export const MOVIE_MODE = "input_boolean.movie_mode";
export const WORK_MODE = "input_boolean.work_mode";
export const AWAY_MODE = "input_boolean.away_mode";
export const CLIMATE_MODE = "input_select.your_climate_mode"; // # REPLACE: or remove

// ── People ─────────────────────────────────────────────────────────────────
// Add your person entity IDs here
export const PERSONS: string[] = [];

// ── Add your entities below ────────────────────────────────────────────────
// (Generated from interview answers — each room's entities added here)

Automation YAML files

For each selected domain, copy the template and substitute placeholder IDs:

  • your_room_motion_sensor → actual entity ID from interview
  • your_notify_target → chosen notification target
  • your_morning_work_day (input_datetime) → keep as-is (user sets value in HA UI)

Copy template files to config/automations/:

bash
# Example for lighting:
cp docs/templates/config/automations/lighting.yaml config/automations/lighting.yaml
# Then perform substitutions based on interview answers

docs/system-overview.md and docs/house-rules.md

Populate the template sections with interview answers. Leave blank sections with the original guidance comments for sections not covered.

Step 9: Build + Deploy

  1. Install dashboard dependencies (if not already installed):

    bash
    cd dashboard && npm install && cd ..
  2. Verify TypeScript compiles cleanly before deploying:

    bash
    cd dashboard && npx tsc -b --noEmit && cd ..

    If this fails, fix the errors before proceeding. Common issues:

    • Entity constants with empty string "" as EntityId need actual entity IDs or removal
    • Missing @types/* packages → run npm install first
  3. Run backup:

    bash
    make backup
  4. Push configuration:

    bash
    make push
  5. Deploy dashboard:

    bash
    make deploy-dashboard
  6. Verify by asking the user to open HA and confirm the dashboard panel appears. Provide the URL: http://[HA_URL]/custom-dashboard

Optional: Make the dashboard the default view

After the dashboard deploys successfully, ask the user:

"Would you like to make this dashboard your default view when opening Home Assistant? This requires installing the Custom Sidebar plugin via HACS. If you don't have HACS, you can skip this — your dashboard is still accessible from the sidebar."

If the user wants this:

  1. Verify HACS is installed:

    bash
    source .env && ssh "$SSH_USER@$HA_HOST" "source /etc/profile.d/claude-ha.sh; source ${HA_REMOTE_PATH:=/config/}.env; ha-api state sensor.hacs" 2>/dev/null

    If HACS is not installed, tell the user to install it first from hacs.xyz and skip this step.

  2. Tell the user to install Custom Sidebar via HACS:

    • Open HA → HACS → Frontend → search "Custom Sidebar" → Install
    • This cannot be done via CLI — HACS frontend installations require the UI
  3. Once confirmed installed, add the plugin to configuration.yaml under frontend:

    yaml
    frontend:
      extra_module_url:
        - /hacsfiles/custom-sidebar/custom-sidebar-plugin.js

    Merge with existing frontend: block — do not duplicate the key.

  4. Create config/custom-sidebar-config.yaml:

    yaml
    default_path: /custom-dashboard
  5. Push the config and tell the user to restart HA (this change requires a restart, not just a reload):

    bash
    make push

Step 10: Save Completion Checkpoint

bash
python3 -c "
import json, datetime
with open('setup-state.json') as f:
    state = json.load(f)
state['session']['current_step'] = 'customize_complete'
state['session']['steps_completed'].append('customize')
state['session']['completed_at'] = datetime.datetime.now().isoformat()
with open('setup-state.json', 'w') as f:
    json.dump(state, f, indent=2)
print('Setup complete. setup-state.json updated.')
"

Completion Message

"Setup complete! Your dashboard is deployed.

What's configured:

  • [N] rooms mapped
  • [N] automation domains active
  • Dashboard entities wired

Next steps:

  • Open HA companion app and navigate to your custom dashboard panel
  • Review docs/house-rules.md and fill in any sections that are still blank
  • If something's wrong, just tell me what to fix — all entity IDs are now in dashboard/src/lib/entities.ts and config/automations/*.yaml"

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 Setup Customize AI skill do?

Run after setup-infrastructure to map rooms, entities, and preferences to the dashboard and automation templates. Conversational and resumable. Trigger phrases: "customize my home", "set up rooms", "configure automations", "map my entities", "set up my dashboard", "finish setup", "resume setup".

Why use Setup Customize on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/dcb/homeassistant-claude-kit/tree/main/.claude/skills/setup-customize. 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 Setup Customize?

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 Setup Customize?

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

Is the Setup Customize AI skill free?

Yes. It is published on GitHub by dcb 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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