Energy Systems

Community

beita6969

energy-systems

Analyzes energy systems including renewable energy resource assessment, power grid modeling, battery storage optimization, energy efficiency evaluation, and techno-economic analysis of energy technologies; trigger when users discuss solar, wind, grid integration, energy storage, or power system design.

Overview

Publisher	beita6969
Repository	`ScienceClaw`
Skill name	`energy-systems`
Stars	950
Forks	106
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

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.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by beita6969 on GitHub. Read the source before you install it.

Installation

Install the Energy Systems AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/beita6969/ScienceClaw/tree/main/skills/energy-systems
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.

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/beita6969/ScienceClaw.git /tmp/ScienceClaw
mkdir -p .claude/skills
cp -r /tmp/ScienceClaw/skills/energy-systems .claude/skills/energy-systems

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Energy Systems 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 Energy Systems 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 Energy Systems 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.

When to Trigger

Activate this skill when the user mentions:

Solar energy, photovoltaic, wind power, hydropower
Power grid, load balancing, dispatch optimization
Battery storage, lithium-ion, energy density, cycling
Energy efficiency, HVAC, building energy modeling
Techno-economic analysis, LCOE, payback period
Electric vehicles, charging infrastructure, V2G
Hydrogen economy, fuel cells, electrolysis

Step-by-Step Methodology

Define the energy system scope - Specify system boundaries: single building, microgrid, regional grid, or national scale. Identify energy sources (solar, wind, fossil, nuclear, hydro), storage technologies, and demand profiles.
Resource assessment - For solar: analyze irradiance data (GHI, DNI, DHI), calculate capacity factor, account for degradation and soiling. For wind: analyze wind speed distributions (Weibull), compute power curves, assess turbulence intensity. Use TMY (Typical Meteorological Year) data or site-specific measurements.
System modeling - Size components: panels/turbines (capacity), inverters, batteries (energy and power), converters. Model energy balance: generation - consumption - storage - curtailment = grid exchange. Use hourly or sub-hourly time resolution.
Grid integration - Analyze grid interconnection requirements: voltage, frequency, power factor. Assess variability and ramping impacts. Model dispatch optimization (merit order, economic dispatch, unit commitment). Evaluate ancillary services potential.
Storage analysis - Characterize storage technology: energy density (Wh/kg), power density (W/kg), round-trip efficiency, cycle life, calendar life, self-discharge rate. Optimize sizing based on arbitrage value, peak shaving, or reliability requirements.
Economic analysis - Calculate LCOE (levelized cost of energy) with discount rate, capital costs, O&M, fuel costs, and lifetime. Compute NPV, IRR, and payback period. Include incentives (ITC, PTC, feed-in tariffs). Conduct sensitivity analysis on key assumptions.
Environmental assessment - Calculate avoided CO2 emissions using grid emission factors. Perform lifecycle emissions analysis (cradle-to-gate). Compare with conventional alternatives.

Key Databases and Tools

NREL (SAM, PVWatts, NSRDB) - Solar and renewable energy tools
Global Wind Atlas - Wind resource data
EIA / IEA - Energy statistics and projections
HOMER Energy - Microgrid optimization
OpenDSS - Distribution system simulation
PyPSA - Open-source power system analysis

Output Format

Resource assessment as tables: annual/monthly capacity factor, energy yield (kWh/kWp).
System diagram with component sizes, power flows, and energy balance.
LCOE breakdown: capital, O&M, fuel, financing costs per kWh.
Economic results as NPV, IRR, payback period with sensitivity tornado chart.
Time series plots: generation, demand, storage state-of-charge, grid exchange.

Quality Checklist

Resource data source and time resolution specified
System losses itemized (inverter, wiring, degradation, soiling, curtailment)
Discount rate and financial assumptions documented
Storage degradation and replacement costs included in economics
Grid emission factor source and year specified
Sensitivity analysis covers key uncertainties (resource, cost, discount rate)
Units consistent (kW vs. kWh, AC vs. DC clearly distinguished)
Comparison with alternatives provided for context

Frequently asked questions

What does the Energy Systems AI skill do?

Why use Energy Systems on TypingMind?

Because you install it once and use it with any model. Energy Systems 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 Energy Systems in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/beita6969/ScienceClaw/tree/main/skills/energy-systems. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Energy Systems?

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 Energy Systems?

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

Is the Energy Systems AI skill free?

Yes. It is published on GitHub by beita6969 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

When to Trigger

Step-by-Step Methodology

Key Databases and Tools

Output Format

Quality Checklist

Frequently asked questions

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