home-assistant-api
Orchestrates access to the Home Assistant REST API for programmatic control of smart home devices. Routes requests to specialized resource files based on task type - authentication, state management, service calls, entity types, or advanced queries. Provides intelligent decision tables for selecting appropriate endpoints and managing integrations.
What this skill does
# Home Assistant REST API Orchestration Skill
This skill provides access to the Home Assistant REST API for building integrations, automating smart home devices, and managing Home Assistant instances programmatically.
## Quick Reference: When to Load Which Resource
| Task | Load Resource |
|------|---------------|
| Setting up authentication, understanding API basics, HTTP methods | `resources/core-concepts.md` |
| Querying entity states, updating states, monitoring changes | `resources/state-management.md` |
| Controlling lights, climate, locks, and other devices | `resources/service-reference.md` |
| Understanding light, switch, sensor, climate entity types | `resources/entity-types.md` |
| Server-side template queries, complex filters, aggregations | `resources/templates.md` |
| System configuration, component discovery, error logs | `resources/system-config.md` |
| Complete code examples, client libraries, patterns | `resources/examples.md` |
## Orchestration Protocol
### Phase 1: Task Analysis
Identify what the user needs to accomplish:
**Authentication & Setup?**
- Getting started with Home Assistant API
- Creating or managing tokens
- Configuring HTTP clients
→ Load `resources/core-concepts.md`
**Query or Monitor State?**
- "What is the temperature in the kitchen?"
- "Is the front door locked?"
- "Get all lights that are on"
- "Monitor entity changes"
→ Load `resources/state-management.md`
**Control a Device?**
- "Turn on the kitchen light"
- "Set thermostat to 22°C"
- "Lock the front door"
- "Play music on speaker"
→ Load `resources/service-reference.md` (then find entity type in `resources/entity-types.md`)
**Understand Entity Types?**
- "What attributes does a climate entity have?"
- "What services are available for locks?"
- "How do I control a media player?"
→ Load `resources/entity-types.md`
**Complex Query or Data Aggregation?**
- "Count all lights that are on"
- "Get devices with low battery"
- "Average temperature from all sensors"
- "Conditional logic based on time of day"
→ Load `resources/templates.md`
**System Management or Discovery?**
- "What components are loaded?"
- "What services are available?"
- "Check configuration validity"
- "View error logs"
→ Load `resources/system-config.md`
**Practical Working Example?**
- Code in Python, Node.js, Bash, curl
- Integration patterns
- Error handling
- Multi-entity operations
→ Load `resources/examples.md`
### Phase 2: Endpoint Selection
Use this decision tree to select the right API endpoint:
```
Do you need to...
│
├─ GET INFORMATION?
│ ├─ Get one entity's state? → GET /api/states/{entity_id}
│ ├─ Get all entity states? → GET /api/states (then filter)
│ ├─ Get configuration? → GET /api/config
│ ├─ List available services? → GET /api/services
│ ├─ Discover event types? → GET /api/events
│ ├─ Query historical data? → GET /api/history/period/{timestamp}
│ ├─ Get error log? → GET /api/error_log
│ ├─ Complex query/computation? → POST /api/template
│ └─ Check system status? → GET /api/
│
├─ CONTROL A DEVICE?
│ ├─ Light (on/off/brightness)? → POST /api/services/light/{service}
│ ├─ Switch? → POST /api/services/switch/{service}
│ ├─ Climate/thermostat? → POST /api/services/climate/{service}
│ ├─ Lock? → POST /api/services/lock/{service}
│ ├─ Cover/blinds? → POST /api/services/cover/{service}
│ ├─ Media player? → POST /api/services/media_player/{service}
│ ├─ Fan? → POST /api/services/fan/{service}
│ ├─ Camera? → POST /api/services/camera/{service}
│ └─ Any service? → POST /api/services/{domain}/{service}
│
├─ MODIFY STATE (NOT FOR DEVICE CONTROL)?
│ ├─ Create/update state? → POST /api/states/{entity_id}
│ ├─ Delete state? → DELETE /api/states/{entity_id}
│ └─ Fire custom event? → POST /api/events/{event_type}
│
└─ MANAGE SYSTEM?
├─ Validate config? → POST /api/config/core/check_config
├─ Reload config? → POST /api/services/homeassistant/reload_core_config
├─ Restart Home Assistant? → POST /api/services/homeassistant/restart
├─ Get components list? → GET /api/components
├─ Update entity metadata? → POST /api/services/homeassistant/update_entity
└─ Check error log? → GET /api/error_log
```
### Phase 3: Execution & Validation
**Before Calling API:**
1. Do you have correct entity_id? (domain.name format)
2. Are you using the right HTTP method? (GET vs POST vs DELETE)
3. Is your authentication token valid?
4. For service calls, do you have the right parameters?
**During Execution:**
- Handle error responses appropriately (401, 404, 500, etc.)
- Retry on network errors with exponential backoff
- Monitor performance for polling operations
**After Execution:**
- Verify response matches expectation
- Check for error codes in response
- Cache results if applicable
## Common Task Patterns
### Query Current State
```bash
# Get one light's state
GET /api/states/light.kitchen
# Get temperature reading
GET /api/states/sensor.temperature
# Get all lights
GET /api/states
# Then filter: .[] | select(.entity_id | startswith("light."))
```
Load: `resources/state-management.md` then `resources/core-concepts.md` for HTTP details
### Turn on/off Devices
```bash
# Turn on light with brightness
POST /api/services/light/turn_on
{"entity_id": "light.kitchen", "brightness": 200}
# Turn off all lights
POST /api/services/light/turn_off
{"entity_id": "all"}
# Toggle switch
POST /api/services/switch/toggle
{"entity_id": "switch.coffee_maker"}
```
Load: `resources/service-reference.md` + `resources/entity-types.md` for specific parameters
### Query Multiple Entities
**Option 1: Multiple API calls** (simple, high bandwidth)
```bash
GET /api/states/light.kitchen
GET /api/states/light.living_room
GET /api/states/light.bedroom
```
**Option 2: Get all and filter** (one call, parse locally)
```bash
GET /api/states
# Filter in client: select by entity_id prefix
```
**Option 3: Server-side template** (most efficient)
```bash
POST /api/template
{"template": "{{ states.light | selectattr('state', 'eq', 'on') | list | length }}"}
```
Load: `resources/templates.md` for advanced queries
### Batch Operations
```bash
# Bad: Multiple sequential API calls
POST /api/services/light/turn_on {"entity_id": "light.kitchen"}
POST /api/services/light/turn_on {"entity_id": "light.living_room"}
POST /api/services/light/turn_on {"entity_id": "light.bedroom"}
# Better: Array of entities in one call
POST /api/services/light/turn_on
{"entity_id": ["light.kitchen", "light.living_room", "light.bedroom"]}
# Best: Use Home Assistant script (for complex multi-step)
POST /api/services/script/turn_on
{"entity_id": "script.my_scene"}
```
Load: `resources/examples.md` for working code patterns
## Entity Type Quick Reference
### Read-Only (Sensors)
- `sensor.*` - Numeric/text readings
- `binary_sensor.*` - On/off detection
- `camera.*` - Camera snapshots
Load: `resources/entity-types.md` for attributes
### Controllable Entities
- `light.*` - Lights (on/off, brightness, color)
- `switch.*` - Switches (on/off)
- `climate.*` - Thermostats (temperature, mode)
- `cover.*` - Blinds, doors (open/close, position)
- `lock.*` - Locks (lock/unlock)
- `fan.*` - Fans (on/off, speed, oscillate)
- `media_player.*` - Media devices (play/pause, volume)
Load: `resources/entity-types.md` then `resources/service-reference.md`
### Meta Entities (Non-device)
- `automation.*` - Automations (trigger, turn on/off)
- `script.*` - Scripts (turn on/off)
- `scene.*` - Scenes (activate)
- `group.*` - Entity groups
- `person.*` - Location tracking
- `device_tracker.*` - Device tracking
- `input_*` - Input helpers
### Status Entities
- `person.*` - "home" or "not_Related in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.