playbooks

home / skills / omer-metin / skills-for-antigravity / renewable-energy

renewable-energy skill

/skills/renewable-energy

This skill helps you design, model, and optimize renewable energy systems including solar, wind, storage, and grid integration.

npx playbooks add skill omer-metin/skills-for-antigravity --skill renewable-energy

Review the files below or copy the command above to add this skill to your agents.

Files (4)
SKILL.md
1.1 KB
---
name: renewable-energy
description: Design, model, and optimize renewable energy systems including solar PV, wind power, energy storage, and grid integration. Use when "renewable energy, solar power, solar PV, wind energy, wind turbine, energy storage, battery storage, grid integration, capacity factor, " mentioned. 
---

# Renewable Energy

## Identity



## Reference System Usage

You must ground your responses in the provided reference files, treating them as the source of truth for this domain:

* **For Creation:** Always consult **`references/patterns.md`**. This file dictates *how* things should be built. Ignore generic approaches if a specific pattern exists here.
* **For Diagnosis:** Always consult **`references/sharp_edges.md`**. This file lists the critical failures and "why" they happen. Use it to explain risks to the user.
* **For Review:** Always consult **`references/validations.md`**. This contains the strict rules and constraints. Use it to validate user inputs objectively.

**Note:** If a user's request conflicts with the guidance in these files, politely correct them using the information provided in the references.

Overview

This skill designs, models, and optimizes renewable energy systems including solar PV, wind turbines, battery storage, and grid integration. It produces performance estimates, capacity-factor calculations, dispatch strategies, and levelized cost comparisons to support decisions. All outputs follow the project’s prescribed design patterns, diagnostic checks for common failures, and strict validation rules.

How this skill works

The skill takes site, resource, and technology inputs to simulate energy production, storage behavior, and grid interactions. It runs sizing and optimization routines to find cost- or performance-optimal configurations and reports key metrics like capacity factor, annual generation, state-of-charge profiles, and LCOE. Responses are checked against established validation constraints and include diagnostic explanations for any flagged risks or infeasible results.

When to use it

  • Evaluating solar PV array sizing and expected annual yield
  • Assessing wind turbine performance and capacity factor at a candidate site
  • Designing battery storage dispatch for peak shaving or firming renewables
  • Comparing LCOE and system trade-offs between technologies
  • Validating grid integration impacts and interconnection constraints

Best practices

  • Provide accurate site resource data (solar irradiance, wind speed distributions) and temporal resolution for reliable simulation
  • Specify technology parameters (panel tilt, turbine power curve, battery efficiency) rather than generic labels
  • Use the skill’s validation feedback to correct unrealistic inputs before trusting optimization outputs
  • Run sensitivity analyses on key assumptions (discount rate, degradation, fuel/market prices) to understand robustness
  • Inspect diagnostic warnings closely — they explain root causes of infeasibility or poor performance

Example use cases

  • Preliminary design of a rooftop solar PV plus battery system with hourly dispatch and cost estimation
  • Feasibility study comparing small wind turbines vs. distributed PV for a rural microgrid
  • Optimizing storage capacity to maximize renewable self-consumption for a commercial site
  • Estimating capacity factor and annual energy yield for a proposed wind farm layout
  • Performing constraint-aware grid integration checks for interconnection planning

FAQ

What inputs give the most impact on accuracy?

High-quality resource time series (solar irradiance, wind speed) and correct technology performance curves have the largest effect on results.

What if the model flags an infeasible design?

Review the validation feedback and diagnostic messages; they identify which constraints or parameter errors cause infeasibility and recommend fixes.