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.
npx skills add nimabahrami/pypsa-skills-kit --skill pypsa-custom-constraintsgit clone --depth 1 https://github.com/nimabahrami/pypsa-skills-kitWrote 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.
[](https://agentmods.dev/skills/nimabahrami/pypsa-skills-kit/pypsa-custom-constraints)<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.
<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>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.
| Model | Per session | Once 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 |
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.
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.
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 dimsnapshot(references/generic-patterns.md) - ! fixed-capacity components = CONSTANTS, not variables -> mixed fleets: ADD constant part to constraint RHS/LHS explicitly
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.
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.
- 11d ago First seen · 67 lines · 0 tokens per session scan A 5fcfe0d09b74
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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