Pandapower

Organization

Power-Agent

pandapower

Progressive-disclosure workflow for pandapower power-system studies. Use whenever the user wants to load, build, or inspect a pandapower network, run AC power flow, check bus voltages or line and transformer loading, or screen N-1 contingencies — even when they just say "run a load flow", "check this case", "is anything overloaded", or name a case file like case39.json. Exposes a clean base-case solve before advanced outage studies. Reach for this instead of answering pandapower grid questions unaided.

Overview

Publisher	Power-Agent
Repository	`PowerSkills`
Skill name	`pandapower`
Stars	77
Forks	23
Bundled files	7
License	MIT
Links	Skill source Repository

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.
7 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by Power-Agent on GitHub. Read the source before you install it.

Installation

Install the Pandapower 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/Power-Agent/PowerSkills/tree/main/powerskills-tool/skills/pandapower
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/Power-Agent/PowerSkills.git /tmp/PowerSkills
mkdir -p .claude/skills
cp -r /tmp/PowerSkills/powerskills-tool/skills/pandapower .claude/skills/pandapower

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

Use it in TypingMind

Enable Pandapower 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 Pandapower 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 Pandapower 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.

pandapower workflow

Start with the network model and a clean base-case solve. Use contingency analysis only after the steady-state case is credible.

Default tool ladder

load_network(file_path) or create_empty_network()
get_network_info() to inspect buses, lines, transformers, generators, loads, and switches.
run_power_flow(...) to establish the base operating point.
run_contingency_analysis(...) only after the base case solves and the monitoring limits are clear.

Example sequence

Using the case39.json network shipped with this skill:


load_network("case39.json")   -> 39 buses, 34 lines, 12 transformers, 10 generators
get_network_info()            -> confirms slack bus, switch states, and rating fields
run_power_flow()              -> converged; report min/max res_bus.vm_pu and worst loading_percent
run_contingency_analysis()    -> ranks N-1 outages by the violations each one causes

Read the base-case numbers before running contingencies, then use the escalation table below on whatever the solve actually reports.

Working rules

Do not run contingencies before checking the base-case voltages and loading.
Use pandapower for AC feasibility and fast screening, then escalate only when the question requires more.

Escalation triggers

Map what the solve reports to the mitigation skill that handles it, and quote the numbers that tripped it (bus + vm_pu, element + loading %) rather than saying "violations exist".

Observation	Escalate to
`res_bus.vm_pu` < 0.95 or > 1.05	`voltage-violation-mitigation`
`res_line` / `res_trafo` `loading_percent` > 100	`thermal-overload-mitigation`
`run_contingency_analysis` flags any N-1 violation or islanding	`contingency-mitigation`
`run_power_flow` fails to converge	`convergence-failure-mitigation`
The study is DER (PV, storage) integration on a distribution network	`der-hosting-capacity-mitigation`

Local assets in this skill

case39.json — a ready test network.
scripts/network_analysis.py — structured base-case review.
scripts/contingency_analysis.py — local N-1 screening.

Deliver

The network loaded and whether the base case converged.
The key voltage or loading findings.
Whether contingency or mitigation work is now justified.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Pandapower AI skill do?

Why use Pandapower on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Power-Agent/PowerSkills/tree/main/powerskills-tool/skills/pandapower. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Pandapower?

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 Pandapower?

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

Is the Pandapower AI skill free?

Yes. It is published on GitHub by Power-Agent 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

pandapower workflow

Default tool ladder

Example sequence

Working rules

Escalation triggers

Local assets in this skill

Deliver

Bundled files

Frequently asked questions

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