Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Grid-Aware Energy Load Shifter
Grid-aware energy load shifter for Home Assistant. Reads real-time electricity prices (TOU, time-of-use, dynamic pricing), solar production forecasts, batter...
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Grid-aware energy load shifter for Home Assistant. Reads real-time electricity prices (TOU, time-of-use, dynamic pricing), solar production forecasts, batter...
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- README.md, SKILL.md, scripts/ha_bridge.py, references/energy_entities.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.3
Provenance
- Publisher
- mrbese
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Grid-Aware Energy Load Shifter
Shift heavy residential loads to the cheapest electricity hours using Home Assistant energy data.
Quick Start
# Find all energy-related entities in HA python3 {baseDir}/scripts/ha_bridge.py discover # Get a full energy dashboard snapshot (prices, solar, consumption, batteries) python3 {baseDir}/scripts/ha_bridge.py energy-summary # Turn on the EV charger python3 {baseDir}/scripts/ha_bridge.py call-service switch/turn_on --entity-id switch.ev_charger
Connection
Two paths to reach Home Assistant: MCP (preferred): If the HA MCP server is configured, use mcporter call homeassistant.<tool> directly. REST API: Use python3 {baseDir}/scripts/ha_bridge.py. Requires HA_URL and HA_TOKEN environment variables.
Security
Required credentials: VariableDescriptionHA_URLHome Assistant base URL (e.g. http://homeassistant.local:8123)HA_TOKENHome Assistant Long-Lived Access Token Least-privilege recommendations: Create a dedicated Home Assistant user account for this skill (e.g. openclaw-energy) Generate a Long-Lived Access Token from that account only Limit the account's entity access to energy-related entities if your HA setup supports entity-level permissions Test with read-only commands first (discover, energy-summary) before enabling device control Domain allowlist: The call-service command restricts actions to energy-related domains only: switch, automation, script, climate, water_heater, input_boolean, input_number, number. All other domains (e.g. lock, alarm_control_panel) are blocked with exit code 2.
Commands
CommandWhat it doesExamplediscoverList all energy entitiesha_bridge.py discoverenergy-summaryOne-shot dashboard (prices + consumption + solar + storage)ha_bridge.py energy-summarystatus <entity>Read a single entity's state and attributesha_bridge.py status sensor.electricity_pricecall-service <d/s>Call an energy-related HA service (restricted to allowed domains)ha_bridge.py call-service switch/turn_on --entity-id switch.ev_chargerhistory <entity>Get state changes over last N hoursha_bridge.py history sensor.grid_import --hours 24 All commands output JSON to stdout.
Load-Shifting Workflow
Follow these steps when asked about energy optimization: Discover available energy entities: run discover or energy-summary Read prices: Check pricing entities' state and attributes β look for: Hourly price arrays in today / tomorrow / prices_today / rates attributes price_level attribute (CHEAP / NORMAL / EXPENSIVE) Current vs. average price comparison Identify deferrable loads: Find switch.* entities for schedulable devices (EV charger, pool pump, dishwasher, washer/dryer, water heater) Find the cheapest window: Scan hourly prices for the contiguous N-hour block with the lowest sum (N = estimated run time of device) Execute: Call switch/turn_on at the optimal time, or automation/trigger if the user has an existing automation
Interpreting Price Data
Different integrations expose prices differently: Hourly arrays (Nordpool, ENTSO-e, Octopus): Read today/tomorrow attributes β find cheapest hours Price level (Tibber): Read price_level β act when CHEAP or VERY_CHEAP Real-time (Amber Electric): Read 5-minute pricing β shift loads immediately when cheap Utility meter tariffs: Read sensor.*_peak vs sensor.*_offpeak β user's HA automations switch tariffs at configured times Static TOU: Read current_price attribute β compare against historical average
Cost Savings Estimate
When recommending a shift, show estimated savings: savings = (current_rate - cheapest_rate) Γ device_power_kw Γ run_duration_hours
Solar Self-Consumption
If solar sensors exist, align loads with peak production: Read sensor.forecast_solar_* or sensor.solcast_* for today's forecast Shift loads to hours with highest expected production This avoids grid import entirely β savings = full retail rate Γ kWh shifted
HVAC Pre-Conditioning
HVAC is the largest residential load (40-50% of electricity). Pre-cool or pre-heat during cheap/solar hours so the home coasts through expensive peak periods: Read climate.* entities for current HVAC mode and setpoint During cheapest window: lower cooling setpoint by 2-3F (pre-cool) or raise heating setpoint by 2-3F (pre-heat) During peak window: raise cooling setpoint by 2-3F to coast on thermal mass Savings estimate: 1.5-3 kW shifted Γ price differential Γ hours
Water Heater Scheduling
Electric water heaters (4.5 kW typical) are ideal deferrable loads: Find switch.water_heater or water_heater.* entities Heat during cheapest/solar window to full temperature Turn off during peak hours (tank maintains temperature for 4-6 hours) Savings estimate: 4.5 kW Γ price differential Γ 3-4 hours/day
Battery Arbitrage
If home battery entities exist (sensor.battery_soc, sensor.powerwall_*, sensor.enphase_*): Read current state of charge and charge/discharge rate limits Charge from grid during cheapest hours (or from solar) Discharge to home during peak price hours to avoid grid import Advanced: If battery supports grid export and VPP enrollment, discharge to grid during extreme price events ($2,000+/MWh) Savings estimate: battery_capacity_kwh Γ (peak_rate - valley_rate)
Demand Response / VPP Integration
For homes enrolled in utility demand response or virtual power plant programs: Read demand response signal entities (if available via HA integration) When DR event active: shed non-critical loads, pre-cool/pre-heat, discharge battery Estimate DR payment: kW reduced Γ event duration Γ program rate
Entity Reference
For detailed entity patterns across providers, read: energy_entities.md
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Docs1 Scripts
- SKILL.md
- README.md
- references/energy_entities.md
- scripts/ha_bridge.py