pypsa-custom-constraints

pypsa-custom-constraints is a skill for Claude Code from nimabahrami/pypsa-skills-kit. It costs 126 tokens per session (1,258 once invoked), scanned A, original, MIT.

A method for adding limits to PyPSA optimisation models when the built-in options do not express the required rule. PyPSA optimisation chooses an energy-system design or operation at the lowest stated cost.

In plain words
What is it for?
Use it to impose rules such as carbon budgets, fuel-use limits, transmission expansion limits, technology capacity limits, and other system-wide production or expansion caps.
Why use it?
Custom limits can be silently left out if they are attached incorrectly, producing results that look plausible but violate the intended rule. This add-on provides an order for adding constraints and checking that they were included.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter.

Part of the pypsa-skills plugin — 9 skills shipped together

Good fit Use it to impose rules such as carbon budgets, fuel-use limits, transmission expansion limits, technology capacity limits, and other system-wide production or expansion caps.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/nimabahrami/pypsa-skills-kit/pypsa-custom-constraints
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add nimabahrami/pypsa-skills-kit --skill pypsa-custom-constraints
Clone the repo
git clone --depth 1 https://github.com/nimabahrami/pypsa-skills-kit

Made for: Claude Code.

Or install pypsa-skills, the plugin that ships this one along with the rest of its 9 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for pypsa-custom-constraints

README.md
[![agentmods](https://agentmods.dev/badge/skills/nimabahrami/pypsa-skills-kit/pypsa-custom-constraints/github.svg)](https://agentmods.dev/skills/nimabahrami/pypsa-skills-kit/pypsa-custom-constraints)
Your own site
<a href="https://agentmods.dev/skills/nimabahrami/pypsa-skills-kit/pypsa-custom-constraints"><img src="https://agentmods.dev/badge/skills/nimabahrami/pypsa-skills-kit/pypsa-custom-constraints/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for pypsa-custom-constraints

Your own site · 80×15
<a href="https://agentmods.dev/skills/nimabahrami/pypsa-skills-kit/pypsa-custom-constraints"><img src="https://agentmods.dev/badge/skills/nimabahrami/pypsa-skills-kit/pypsa-custom-constraints.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 126 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,258 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00126 $0.01258
Opus 5 $0.00063 $0.00629
Sonnet 5 $0.00025 $0.00252
Haiku 4.5 $0.00013 $0.00126

Measured 11d ago against content hash 5fcfe0d09b74, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

pypsa-custom-constraints scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/inspect_model.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/pypsa-custom-constraints/SKILL.md · 67 lines

How it starts

The opening of the file, as written. The whole thing — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.

PyPSA Custom Constraints (linopy)

Three steps, in order: 0. native GlobalConstraint type covers it? -> component, ZERO linopy (list below). 1. else express in linopy. 2. PROVE it landed. ! Never deliver without verification checklist. Silently-ignored constraint -> plausible wrong results.

Step 0 - native first (zero linopy)

  • GlobalConstraint types: "primary_energy" (CO2/byproduct cap) | "operational_limit" (carrier net production, e.g. gas/biomass budget) | "transmission_volume_expansion_limit" (MWkm) | "transmission_expansion_cost_limit" (EUR) | "tech_capacity_expansion_limit" (cap per carrier, optional per bus + investment_period; replaces deprecated per-bus-carrier nominal constraints). ! Don't hand-roll these — CCL-style caps = tech_capacity_expansion_limit. ! 1.0.x: tech_capacity_expansion_limit NotImplemented on stochastic (set_scenarios) networks
  • Carrier growth limits (max_growth | max_relative_growth) = component attributes, also zero linopy -> pypsa-network-modeling/references/multi-period.md

Attachment patterns

# Pattern A - callback (works with n.optimize)
def extra_functionality(n, snapshots):
    m = n.model
    p = m.variables["Generator-p"]          # dims: (snapshot, name)
    m.add_constraints(..., name="my-constraint")

n.optimize(extra_functionality=extra_functionality, solver_name="highs")

# Pattern B - explicit (better for debugging)
n.optimize.create_model()
m = n.model
m.add_constraints(..., name="my-constraint")
n.optimize.solve_model(solver_name="highs")

Variable name map (linopy labels)

  • Generator-p | Generator-p_nom | Link-p | Link-p_nom | Line-s | Line-s_nom | Store-e | Store-p | StorageUnit-state_of_charge | StorageUnit-p_dispatch | StorageUnit-p_store
  • ! component dimension in linopy vars = name (NOT the class): select subsets via .sel(name=index). Capacity vars exist only for *_nom_extendable=True
  • ! coefficient alignment: pandas Series w/ mismatched indices -> silent all-NaN. Per-component coefficients = xr.DataArray(..., coords={"name": idx}) | per-snapshot = DataArray on dim snapshot (references/generic-patterns.md)
  • ! fixed-capacity components = CONSTANTS, not variables -> mixed fleets: ADD constant part to constraint RHS/LHS explicitly

Read the full file on GitHub · 67 lines

Files

What ships with it

3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 11d ago First seen · 67 lines · 0 tokens per session scan A 5fcfe0d09b74

Subscribe to this mod's changes

pypsa-custom-constraints is a skill published in the GitHub repository nimabahrami/pypsa-skills-kit (23 stars, last pushed 3mo ago), licensed MIT. It adds 126 tokens to every session and 1,258 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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