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 agentmods add skills/marcos-dv/ampl-agents/ampl-pythonnpx skills add marcos-dv/ampl-agents --skill ampl-pythongit clone --depth 1 https://github.com/marcos-dv/ampl-agentsWhat 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 | $0.00075 | $0.03822 |
| Opus 5 | $0.00037 | $0.01911 |
| Sonnet 5 | $0.00015 | $0.00764 |
| Haiku 4.5 | $0.00007 | $0.00382 |
Grade A, and why
ampl-python 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 2d 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): How it starts
The opening of the file, as written. The whole thing — 498 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AMPL Python (amplpy) Skill
Always-on constraints (verified solve, model structure, session hygiene) live in
rules/and apply whenever this skill is active.
Mission
You write correct, maintainable amplpy code that drives AMPL models. You know the amplpy API, official best practices, and Colab/MO-Book workflow patterns. You keep model structure in .mod files, use Python for data, orchestration, and results, and always verify solves.
You are not a general Python agent — optimization and AMPL session semantics come first.
Code examples convention
This skill is amplpy-first. Every operational pattern shows Preferred: amplpy then Pure AMPL for the same action (solve, options, queries, IIS). Model structure belongs in model.mod.
AMPL installation
From https://amplpy.ampl.com/en/latest/getting-started.html and https://dev.ampl.com/ampl/python/modules.html.
python -m pip install amplpy --upgrade
python -m amplpy.modules install highs gurobi xpress
python -m amplpy.modules activate <license-uuid> # https://ampl.com/ce
python -m amplpy.modules run ampl -vvq
from amplpy import AMPL
ampl = AMPL()
Colab:
%pip install -q amplpy
from amplpy import AMPL, ampl_notebook
ampl = ampl_notebook(
modules=["gurobi", "highs", "xpress"],
license_uuid="default",
)
Ensure deployed code documents required modules and license UUID source (env var, not committed secrets).
Solver classification (commercial vs open-source)
Sources: https://dev.ampl.com/solvers/index.html , https://dev.ampl.com/ampl/python/modules.html
Open-source modules
| Module | Solvers | Use |
|---|---|---|
| highs | HiGHS | LP, MILP |
| cbc | CBC | MILP |
| coin | CBC, Couenne, Ipopt, Bonmin | MIP, MINLP, NLP |
| open | All OSS | MIP, MINLP, NLP |
| scip | SCIP | MIP |
| gcg | GCG | Decomposition |
| gokestrel | NEOS Kestrel | Remote solvers (Colab) |
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.
- 2d ago First seen · 498 lines · 75 tokens per session scan A 5aace1338dce
ampl-python is a skill published in the GitHub repository marcos-dv/ampl-agents (10 stars, last pushed 11d ago), licensed MIT. It adds 75 tokens to every session and 3,822 once invoked, about $0.0004 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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