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.
git clone --depth 1 https://github.com/marcos-dv/ampl-agentsnpx agentmods add skills/marcos-dv/ampl-agents/ampl-installerWrote 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/marcos-dv/ampl-agents/ampl-installer)<a href="https://agentmods.dev/skills/marcos-dv/ampl-agents/ampl-installer"><img src="https://agentmods.dev/badge/skills/marcos-dv/ampl-agents/ampl-installer/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/marcos-dv/ampl-agents/ampl-installer"><img src="https://agentmods.dev/badge/skills/marcos-dv/ampl-agents/ampl-installer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Output Handling · line 74 Output size or generation rate is not bounded. Unbounded output enables denial-of-service through resource exhaustion, log flooding, or context-window stuffing.Fix: Set explicit limits on output length, generation count, and rate. Use max_tokens and truncation to prevent unbounded output.
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 | $0.00068 | $0.03352 |
| Opus 5 | $0.00034 | $0.01676 |
| Sonnet 5 | $0.00014 | $0.00670 |
| Haiku 4.5 | $0.00007 | $0.00335 |
Grade A, and why
ampl-installer 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 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.
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 — 300 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AMPL Installer Skill
Always-on constraints (verified references, session hygiene) live in
rules/and apply whenever this skill is active.
Mission
You get users from zero to a working amplpy environment: pip install amplpy, amplpy.modules install/activate, solver modules, and a verified ampl.solve(). For users who want to try AMPL without local setup, recommend Google Colab via colab.ampl.com (notebooks install amplpy in-session). You explain which AMPL license fits the user's situation (free CE, free academic, commercial trial, paid commercial) and what solvers each tier unlocks.
You do not build models or write application code — delegate to ampl-modeler / ampl-python after install succeeds.
Verified reference library
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 · 300 lines · 68 tokens per session scan A 780bd4df89dd
ampl-installer is a skill published in the GitHub repository marcos-dv/ampl-agents (10 stars, last pushed 19d ago), licensed MIT. It adds 68 tokens to every session and 3,352 once invoked, about $0.0003 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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