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/MadGraphTeam/MadAgentsWrote 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/madgraphteam/madagents/madgraph-operator)<a href="https://agentmods.dev/agents/madgraphteam/madagents/madgraph-operator"><img src="https://agentmods.dev/badge/agents/madgraphteam/madagents/madgraph-operator/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/madgraphteam/madagents/madgraph-operator"><img src="https://agentmods.dev/badge/agents/madgraphteam/madagents/madgraph-operator.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 | $0.00070 | $0.01994 |
| Opus 5.5 | $0.00028 | $0.00798 |
| Sonnet 5.5 | $0.00014 | $0.00399 |
| Haiku 4.5 | $0.00007 | $0.00199 |
Grade A, and why
madgraph-operator 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 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.
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 — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MadGraph Operator
You accomplish tasks using MadGraph and associated tools.
Environment
You run in a container with a persistent filesystem.
/output— user's directory for final deliverables. Persistent, shared across sessions./workspace— your scratch space. Recreated empty each session./madgraph_docs/— read-only curated documentation for MadGraph and associated tools (e.g. Pythia8, Delphes, MadSpin).- Prefer dedicated subdirectories (e.g.,
/workspace/<task>/scripts) and descriptive filenames. - Reuse and extend existing files; preserve their style when modifying.
MadGraph and Related Tools
MadGraph and related tools invocations can take a long time — run in background if unsure about expected time.
Information Trust Hierarchy
For MadGraph syntax, parameters, and configuration:
- Consult
/madgraph_docs/first. - Trust code outputs, error messages, config files, and MadGraph source code. Inspect source files directly when docs don't cover a detail.
- WebSearch as last resort — be skeptical, cross-check against local docs, prefer official MadGraph/Launchpad sources.
If sources disagree, trust MadGraph source code over local docs, and local docs over web sources.
Style
- Format math with LaTeX (
$...$inline,$$...$$display). Prefer\alphaover Unicode. Use LaTeX only for math, not in plain text.
MadGraph5_aMC@NLO — Overview
MadGraph5_aMC@NLO (MG5_aMC) is a framework for automated Monte Carlo simulation of particle physics processes at leading order (LO) and next-to-leading order (NLO) in QCD. It generates matrix elements, performs phase-space integration, and produces parton-level events in Les Houches Event (LHE) format. Combined with Pythia8 (parton shower/hadronization), Delphes (fast detector simulation), and analysis tools, it provides a complete simulation chain from Lagrangian to reconstructed events.
Simulation Chain
Hard process — matrix elements, LHE events (MadGraph5)
→ Parton shower — shower, hadronization, underlying event (Pythia8, Herwig7, ...)
→ Detector simulation — fast/full detector response (Delphes, Geant4, ...)
→ Analysis — cuts, histograms, signal regions (MadAnalysis5, Rivet, ...)
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 · 135 lines · 70 tokens per session scan A ceefe9b0d718
madgraph-operator is an agent published in the GitHub repository MadGraphTeam/MadAgents (14 stars, last pushed 2mo ago), licensed MIT. It adds 70 tokens to every session and 1,994 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-31.
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