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 tondevrel/scientific-agent-skills --skill pyomogit clone --depth 1 https://github.com/tondevrel/scientific-agent-skillsWrote 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/tondevrel/scientific-agent-skills/pyomo)<a href="https://agentmods.dev/skills/tondevrel/scientific-agent-skills/pyomo"><img src="https://agentmods.dev/badge/skills/tondevrel/scientific-agent-skills/pyomo/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/tondevrel/scientific-agent-skills/pyomo"><img src="https://agentmods.dev/badge/skills/tondevrel/scientific-agent-skills/pyomo.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 | $0.00110 | $0.02710 |
| Opus 5 | $0.00055 | $0.01355 |
| Sonnet 5 | $0.00022 | $0.00542 |
| Haiku 4.5 | $0.00011 | $0.00271 |
Grade A, and why
pyomo 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 10d 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 — 308 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pyomo - Mathematical Optimization Modeling
Pyomo allows you to define optimization problems using a natural mathematical syntax (Sets, Parameters, Variables, Constraints). It decouples the model from the solver, allowing the same model to be solved by different engines without code changes.
FIRST: Verify Prerequisites
pip install pyomo
# Also install a solver (e.g., GLPK for linear/integer problems)
# Conda: conda install -c conda-forge glpk ipopt
When to Use
- Strategic Planning: Long-term resource allocation or investment planning.
- Process Engineering: Optimizing chemical plants or refinery operations (Non-linear).
- Energy Systems: Power grid dispatch and unit commitment problems.
- Supply Chain Optimization: Multi-period, multi-commodity flow problems.
- Non-Linear Programming (NLP): When your constraints or objectives involve smooth curves (e.g., x², log(x)).
- Stochastic Programming: Modeling uncertainty in optimization.
- Custom Solver Integration: When you need to use specific solvers like IPOPT, SCIP, or Baron.
Reference Documentation
Official docs: http://www.pyomo.org/
GitHub: https://github.com/Pyomo/pyomo
Search patterns: pyo.ConcreteModel, pyo.Constraint, pyo.Objective, pyo.SolverFactory
Core Principles
Concrete vs. Abstract Models
- ConcreteModel: Data is defined at the time the model is built (most common in Python/Data Science).
- AbstractModel: The structure is defined first, and data is loaded later (standard for large-scale industrial models).
Components
- Var: Unknowns the solver needs to find.
- Set/Param: Data that defines the problem instance.
- Objective: The function to minimize or maximize.
- Constraint: Rules the variables must follow.
Solvers
Pyomo does not have its own solver. It requires external solvers (like glpk for LP/MIP or ipopt for NLP) installed on the system.
Quick Reference
Installation
pip install pyomo
# Also install a solver (e.g., GLPK for linear/integer problems)
# Conda: conda install -c conda-forge glpk ipopt
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.
- 10d ago First seen · 308 lines · 110 tokens per session scan A 777df62df390
pyomo is a skill published in the GitHub repository tondevrel/scientific-agent-skills (21 stars, last pushed 7mo ago), licensed MIT. It adds 110 tokens to every session and 2,710 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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