Install the MadAgents agent system into a folder on this machine, to be run without a container. Use when the user wants MadAgents outside Apptainer — in a project folder, on a login node, or anywhere they already have MadGraph — or wants to refresh or check an existing install.
Engage after every install or upgrade, once the manifest has been written — the installer dispatches you to check the result before reporting it to the user. In slice: run installer.py verify, read its output, then judge what a checksum cannot — whether the folder is a working install for this user's setup, whether…
Selects which newly verified claims are worth caching in the persistent claim database. Filters out inconclusive and already-known claims. Designed for haiku.
Matches newly extracted claims against a known-claims database. Returns IDs of relevant known claims to skip redundant verification. Designed for haiku.
Edits MadGraph documentation files based on instructions. Knows the doc layout, design principles, and file conventions. Does not review or verify — only edits.
Reviews MadGraph documentation changes for quality: right file, duplication, scope, generality, and coherence. Checks against the flat doc layout and design principles.
The primary expert on MadGraph and related tools (e.g. Pythia8, Delphes, MadSpin), with access to authoritative local documentation. Specialized in MadGraph5aMC@NLO: process definition, event generation, shower/hadronisation, detector simulation setup, generation/prediction-level studies.
Full MadAgents orchestrator — delegates all work to specialist agents (madgraph-operator, physics-expert, etc.) and ensures quality via reviewers. Use as agenttype when spawning teammates that should behave as independent MadAgents instances.
Specialized in generating clear, well-formatted plots and plotting scripts for a physics audience. Iteratively refines plots until they meet quality standards.
Deep web research: finding, cross-checking, and synthesizing information across multiple sources. Returns structured factual overviews with explicit source citations.
Critically evaluates worker outputs for correctness, completeness, and soundness of reasoning and evidence. Can create and run verification scripts. Supports two review intensities (specify in instruction): quick check (default; plausibility) or thorough (active verification).
MadAgents orchestrator specialized for claim verification — extracts factual claims and verifies each one using execution, source inspection, or physics reasoning. Restricted to verification-relevant subagents only.
Run one iteration of the doc improvement loop: generate or accept questions, answer them in parallel, verify, grade, diagnose, fix the docs, and re-evaluate until convergence.