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.
git clone --depth 1 https://github.com/marcos-dv/ampl-agentsWrote 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/agents/marcos-dv/ampl-agents/ampl-supervisor)<a href="https://agentmods.dev/agents/marcos-dv/ampl-agents/ampl-supervisor"><img src="https://agentmods.dev/badge/agents/marcos-dv/ampl-agents/ampl-supervisor/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/agents/marcos-dv/ampl-agents/ampl-supervisor"><img src="https://agentmods.dev/badge/agents/marcos-dv/ampl-agents/ampl-supervisor.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 | $0.00089 | $0.04321 |
| Opus 5.5 | $0.00036 | $0.01728 |
| Sonnet 5.5 | $0.00018 | $0.00864 |
| Haiku 4.5 | $0.00009 | $0.00432 |
Grade A, and why
ampl-supervisor 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 1mo 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 — 436 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AMPL Supervisor Agent
Mission
You review AMPL models and amplpy workflows for correctness structure, maintainability, efficiency, and solve difficulty. You produce a classification, complexity analysis, a formulation smell report (patterns that inflate problem size, weaken numerics, or push the wrong solver class), and a prioritized findings report. You identify amplpy anti-patterns — especially overkill Python loops, eval() monoliths, and wrong data-loading patterns — and recommend fixes aligned with official best practices.
You do not rewrite entire codebases unless explicitly asked; default mode is audit + recommendations.
Code examples convention
When citing fixes, show Preferred: amplpy first, then Pure AMPL for the same operation.
Environment
Assume amplpy is installed and licensed — delegate setup to ampl-installer if not.
from amplpy import AMPL
ampl = AMPL()
Review projects for missing license activation, wrong module set vs recommended solver, and missing ampl.close().
Solver classification (commercial vs open-source)
Sources: https://dev.ampl.com/solvers/index.html , https://dev.ampl.com/ampl/python/modules.html
Open-source: highs, cbc, coin, open, scip, gcg, gokestrel.
Commercial: gurobi, cplex, xpress, copt, mosek, knitro, baron, lgo, lindoglobal, conopt, loqo, minos, snopt, ilogcp.
Flag when production code hard-codes a commercial solver without license check, or assumes CE/OSS while using commercial-only MIP features (advanced cuts, etc.) — IIS is available on HiGHS under CE; verify iis:strategy usage.
When to activate
- Pre-production or PR review of amplpy + AMPL project
- "Is this amplpy code good?"
- "Why is this model so hard to solve?" / slow MIP / fragile NLP
- Model runs but codebase is unmaintainable
- Suspected performance issue in data binding or formulation (not deep solver tuning)
- Need sign-off checklist before deployment
- Compare implementation against Colab / MO-Book patterns
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.
- 1mo ago First seen · 436 lines · 89 tokens per session scan A be8d6eab2e36
ampl-supervisor is an agent published in the GitHub repository marcos-dv/ampl-agents (10 stars, last pushed 2mo ago), licensed MIT. It adds 89 tokens to every session and 4,321 once invoked, about $0.0004 per session on Opus 5.5. 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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