How do I install the Pypsa skill?

Run npx skillmds@latest add power-agent/pypsa in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Pypsa safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Pypsa?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Pypsa free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

power-agent (@power-agent) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Pypsa by power-agent — Agent Skill

Name: Pypsa
Author: power-agent

Pypsa

Progressive-disclosure workflow for PyPSA studies. Use whenever the user wants to load or build a PyPSA network, inspect components or time series, run power flow, solve operational OPF, or run capacity-expansion / investment optimization — even when they just say "optimize the dispatch", "size the storage", "run an expansion", or hand over a netCDF/CSV network like case39.nc. Exposes network inspection and feasibility before large optimization, and operational optimization before investment. Reach for this instead of answering PyPSA questions unaided.

power-agent bd08c8b 10 files · 137.0 KB Updated Sep 17, 2026

File contents

PyPSA workflow

Start by creating or loading a named network handle. Use network inspection before optimization, and use operational optimization before investment optimization.

Default tool ladder

load_network(file_path) or create_network(...) to create the active network handle.
get_network_info(network_name) and get_component_details(...) to understand topology, assets, and time series.
run_power_flow(network_name, linear=False) for feasibility checks.
optimize_network(network_name, ...) for dispatch or operational studies.
optimize_investment(network_name, ...) for expansion questions.
import_from_csv_folder(...) or export_to_csv_folder(...) once the network state is worth moving.

Example sequence

Using the case39.nc network shipped with this skill:

load_network("case39.nc")                        -> network "case39" active, 39 buses
get_network_info("case39")                       -> 10 generators, 46 branches, 19 loads
run_power_flow("case39", linear=False)           -> converged, max branch loading 87%
optimize_network("case39", solver_name="highs")  -> feasible dispatch, hourly cost reported

Feasibility first, then dispatch. Only move to optimize_investment once a dispatch study cannot answer the question.

Working rules

Do not jump straight into expansion optimization when a dispatch study can answer the question.
Use AC or DC power flow to sanity-check a network before relying on optimization outputs.
Treat PyPSA as a planning and scheduling engine, not the final authority on detailed reactive mitigation.

Escalation triggers

Quote the numbers that tripped each row (bus + v_mag_pu, element + loading, infeasibility cause) rather than saying "it failed".

Observation	Escalate to
Bus `v_mag_pu` < 0.95 or > 1.05 on an AC power flow	`voltage-violation-mitigation`
Line or transformer loaded > 100% of `s_nom`	`thermal-overload-mitigation`
`run_contingency_analysis` N-1 overload or post-outage islanding	`contingency-mitigation`
`optimize_network` / `optimize_investment` infeasible, reserve-short, or heavily curtailed	`operations-planning-mitigation`
`run_power_flow` fails to converge	`convergence-failure-mitigation`

Local assets in this skill

case39.nc — a ready test network.
scripts/network_analysis.py — inspection.
scripts/optimization_analysis.py — OPF result review.
scripts/expansion_analysis.py — capacity-expansion studies.
scripts/contingency_analysis.py — local N-1 screening outside PowerMCP.

Deliver

The network handle and study type.
The main feasibility or cost result.
Whether the next step is mitigation, AC validation, or a larger optimization.

---name: pypsadescription: Progressive-disclosure workflow for PyPSA studies. Use whenever the user wants to load or build a PyPSA network, inspect components or time series, run power flow, solve operational OPF, or run capacity-expansion / investment optimization — even when they just say "optimize the dispatch", "size the storage", "run an expansion", or hand over a netCDF/CSV network like case39.nc. Exposes network inspection and feasibility before large optimization, and operational optimization before investment. Reach for this instead of answering PyPSA questions unaided.---# PyPSA workflowStart by creating or loading a named network handle. Use network inspection before optimization, and use operational optimization before investment optimization.## Default tool ladder1. `load_network(file_path)` or `create_network(...)` to create the active network handle.2. `get_network_info(network_name)` and `get_component_details(...)` to understand topology, assets, and time series.3. `run_power_flow(network_name, linear=False)` for feasibility checks.4. `optimize_network(network_name, ...)` for dispatch or operational studies.5. `optimize_investment(network_name, ...)` for expansion questions.6. `import_from_csv_folder(...)` or `export_to_csv_folder(...)` once the network state is worth moving.### Example sequenceUsing the `case39.nc` network shipped with this skill:```load_network("case39.nc") -> network "case39" active, 39 busesget_network_info("case39") -> 10 generators, 46 branches, 19 loadsrun_power_flow("case39", linear=False) -> converged, max branch loading 87%optimize_network("case39", solver_name="highs") -> feasible dispatch, hourly cost reported```Feasibility first, then dispatch. Only move to `optimize_investment` once a dispatchstudy cannot answer the question.## Working rules- Do not jump straight into expansion optimization when a dispatch study can answer the question.- Use AC or DC power flow to sanity-check a network before relying on optimization outputs.- Treat PyPSA as a planning and scheduling engine, not the final authority on detailed reactive mitigation.## Escalation triggersQuote the numbers that tripped each row (bus + v_mag_pu, element + loading, infeasibility cause) rather than saying "it failed".| Observation | Escalate to ||---|---|| Bus `v_mag_pu` < 0.95 or > 1.05 on an AC power flow | `voltage-violation-mitigation` || Line or transformer loaded > 100% of `s_nom` | `thermal-overload-mitigation` || `run_contingency_analysis` N-1 overload or post-outage islanding | `contingency-mitigation` || `optimize_network` / `optimize_investment` infeasible, reserve-short, or heavily curtailed | `operations-planning-mitigation` || `run_power_flow` fails to converge | `convergence-failure-mitigation` |## Local assets in this skill- `case39.nc` — a ready test network.- `scripts/network_analysis.py` — inspection.- `scripts/optimization_analysis.py` — OPF result review.- `scripts/expansion_analysis.py` — capacity-expansion studies.- `scripts/contingency_analysis.py` — local N-1 screening outside PowerMCP.## Deliver- The network handle and study type.- The main feasibility or cost result.- Whether the next step is mitigation, AC validation, or a larger optimization.

Frequently asked questions