AD 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, battery state of charge, and consumption data from Home Assistant. Schedules deferrable household loads (EV charging, HVAC pre-conditioning, pool pump, dishwasher, laundry, water heater) to cheapest rate windows. Calculates cost savings, optimizes solar self-consumption, and supports virtual power plant (VPP) demand response signals. Works with Nordpool, ENTSO-e, Tibber, Octopus Energy, Amber Electric, and any utility rate plan worldwide. Built for distributed energy resource (DER) optimization and residential demand-side management.
Grid-aware energy load shifter for Home Assistant.
As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 49/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 5 mutating operations with no state check
- 55Failures and branches. 1 branches
- 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
- 100Steps. 38 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1558 tokens
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 13 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (3 tags): a typed call is more reliable
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 690: enough signal without eating the budget
- +4Structure: 14 headings
- +3Step-by-step instructions: 38 items
- +4Has examples (2 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.