What is ampl-modeler?

A specialist guide for writing AMPL models, which describe decisions and rules for finding the best solution to problems such as scheduling, supply chains, and energy planning. It covers linear, integer, nonlinear, constraint, and quadratic optimization.

Use it to create or reformulate AMPL models, represent logical constraints, design multiple objectives, and build optimization formulations for operations, logistics, energy, and related problems.

It helps translate real-world rules into a structured model and choose an appropriate problem type and solver. Separating model structure from data makes models easier to maintain and reuse.

ampl-modeler — Skill for Claude Code and Codex by marcos-dv

Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo

git clone --depth 1 https://github.com/marcos-dv/ampl-agents

agentmods

npx agentmods add skills/marcos-dv/ampl-agents/ampl-modeler

Made for: Claude Code, Codex.

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/marcos-dv/ampl-agents/ampl-modeler/github.svg)](https://agentmods.dev/skills/marcos-dv/ampl-agents/ampl-modeler)

Your own site

<a href="https://agentmods.dev/skills/marcos-dv/ampl-agents/ampl-modeler"><img src="https://agentmods.dev/badge/skills/marcos-dv/ampl-agents/ampl-modeler/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/marcos-dv/ampl-agents/ampl-modeler"><img src="https://agentmods.dev/badge/skills/marcos-dv/ampl-agents/ampl-modeler.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 91 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 5,689 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

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.

Model	Per session	Once invoked
Fable 5.1	$0.00091	$0.05689
Opus 5	$0.00046	$0.02844
Sonnet 5	$0.00018	$0.01138
Haiku 4.5	$0.00009	$0.00569

Measured 9d ago against content hash 18927ed8005f, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

ampl-modeler scanned grade A with 1 finding 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 9d 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

IF you have internet access (WebFetch, browser, or curl):

skills/ampl-modeler/SKILL.md · 518 lines

How it starts

The opening of the file, as written. The whole thing — 518 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AMPL Modeler Skill

Always-on constraints (verified solve, model structure, verified references) live in rules/ and apply whenever this skill is active.

Mission

You are an expert mathematical optimization modeler specializing in AMPL. You translate real-world problems into correct, readable, and efficient AMPL formulations. You prefer logic and high-level operators (==>, and, or, min, max, numberof, indicator-style implications) over hand-rolled big-M linearizations.

You build models that separate structure (.mod) from data (loaded by APIs like amplpy in Python, JSON, Python dicts, pandas, and as an alternative for old projects not using APIs or Python .dat files). You choose problem class and solver deliberately. You never claim a solution is optimal without a verified solve.

Code examples convention

Every snippet that runs AMPL (model load, solve, options, display, queries) lists:

Preferred: amplpy (Python)
Pure AMPL (interactive AMPL, .run, or model.mod)

Models: put structure in model.mod and ampl.read("model.mod"); use ampl.eval(r"""...""") only for one-liners or short fragments under 15 lines (teaching snippets, quick probes) — never for production models.

Environment

Assume amplpy is installed and licensed — delegate setup to ampl-installer if not.

from amplpy import AMPL
ampl = AMPL()

Solver classification (commercial vs open-source)

Sources: https://dev.ampl.com/solvers/index.html (capability matrix), https://dev.ampl.com/ampl/python/modules.html (module names).

Open-source modules

Module	Contents	Typical use
highs	HiGHS	LP, MILP — default for prototyping
cbc	CBC	MILP
coin	CBC, Couenne, Ipopt, Bonmin	MIP, MINLP, local NLP
open	All open-source solvers	Full OSS stack
scip	SCIP	MIP
gcg	GCG	Column generation / Dantzig-Wolfe
gokestrel	Kestrel client	NEOS Server (Colab / CE remote commercial)

Read the full file on GitHub · 518 lines

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.

9d ago First seen · 518 lines · 91 tokens per session scan A 18927ed8005f

Subscribe to this mod's changes

ampl-modeler is a skill published in the GitHub repository marcos-dv/ampl-agents (10 stars, last pushed 18d ago), licensed MIT. It adds 91 tokens to every session and 5,689 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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