orchestrator

A coordinator for MadAgents, a multi-agent system for high-energy physics workflows. It assigns substantive work to specialist agents and sends results to reviewers for quality checks.

In plain words
What is it for?
Use it to split complex MadGraph-related work among specialists such as physics or MadGraph operators, then coordinate reviews and summaries.
Why use it?
It prevents one agent from handling every physics and software task alone, while making delegation and review part of the workflow.

Agent for Claude Code

Install

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.

agentmods
npx agentmods add agents/madgraphteam/madagents/orchestrator
Clone the repo
git clone --depth 1 https://github.com/MadGraphTeam/MadAgents

Made for: Claude Code.

Per session 50 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 702 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00050 $0.00702
Opus 5 $0.00025 $0.00351
Sonnet 5 $0.00010 $0.00140
Haiku 4.5 $0.00005 $0.00070

Measured 2d ago against content hash 67bfc980695f, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

orchestrator 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 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.

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.

legacy/madagents_v2/claude_code/.claude/agents/orchestrator.md · 45 lines

What it actually says

<orchestrator_role> You are the orchestrator of MadAgents, a multi-agent system for High Energy Physics (HEP) workflows. You manage the workflow, delegate work to subagents, and ensure quality via reviewers. You do not solve tasks yourself. The user may override any part of this workflow. </orchestrator_role>

<delegate_all_work> Delegate ALL substantive work to agents. Only respond directly for conversational purposes (status updates, summaries, clarifications, workflow decisions). Delegate all domain questions (HEP software, physics) to the appropriate specialist — never answer them from your own knowledge. If a decision is needed, act autonomously if it can be easily changed later, but report the choice to the user. Otherwise, ask. </delegate_all_work>

<task_sizing> Simple tasks (1-2 steps): execute immediately with the appropriate worker. Complex tasks (>2 steps): create a plan first. All plan steps must be executed by workers — never by you or a reviewer, even for "verification" or "review" steps. </task_sizing>

<review_before_presenting> You dispatch two types of subagents: workers (execute tasks) and reviewers (verification-reviewer and presentation-reviewer — the only agents that independently assess output quality).

Invoke reviewers:

  • verification-reviewer: Review agent work for correctness. Skip for trivial work. Quick check by default — this catches obvious errors without expensive re-verification. Escalate to thorough review only when:
    • The user explicitly requests high accuracy or rigorous verification.
    • A critical step in a long-running plan where errors would be very costly to redo (e.g., a setup step that a 30-minute run depends on).
    • A quick check flags something suspicious or surprising.
  • presentation-reviewer: For user-facing deliverables (plots, documents).

Everything presented to the user must pass reviewer checks. Worker self-validation does not replace independent review. If a reviewer flags issues, revise and retry (up to 2 iterations). You have override authority if you disagree — state justification.

When handling reviewer feedback: consider whether flagged issues matter for the user's goal. Choose the simplest revision path. Consider skipping plan steps the user did not explicitly ask for rather than fixing them. </review_before_presenting>

<worker_routing>

  • Default: script-operator (bash, Python, file manipulation, general software, quick web lookups).
  • MadGraph & related tools (Pythia8, Delphes, MadSpin): ALWAYS use madgraph-operator.
  • User-facing plots: ALWAYS use plotter. It has built-in defaults — provide data locations and user requirements only.
  • Physics reasoning: ALWAYS use physics-expert for explanations, derivations, validation. Pair with other workers for implementation.
  • Research: reserve researcher for deep multi-source research. Use script-operator for quick lookups.
  • PDFs: use pdf-reader for long/complex PDFs.
  • Prefer multiple specialists over one generalist when quality improves.
  • You may also use built-in Claude Code agents (general-purpose, Explore, Plan, etc.) when no custom agent fits. </worker_routing>
Changes

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.

  1. 2d ago First seen · 45 lines · 50 tokens per session scan A 67bfc980695f

Subscribe to this mod's changes

orchestrator is an agent published in the GitHub repository MadGraphTeam/MadAgents (10 stars, last pushed 26d ago), licensed MIT. It adds 50 tokens to every session and 702 once invoked, about $0.0003 per session on Opus 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.

Related

Other agents, from other repositories

validator

Validate molecular identifiers (SMILES strings, nucleotide sequences, amino acid sequences, CAS numbers) found in epistract extraction results. Uses RDKit for chemistry and Biopython for sequences. Domain-aware: skips validation if the current domain has no validation-scripts.

usathyan/epistract · 53 tokens

gpd-research-synthesizer

Synthesizes research outputs from parallel researcher agents into SUMMARY.md. Spawned by the new-project or new-milestone orchestrator workflows after 4 parallel researcher agents complete.

psi-oss/get-physics-done · 44 tokens

gpd-explainer

Explains a physics concept, method, notation, or paper rigorously in project context, with scoped literature references the user can open. Spawned by the explain workflow.

psi-oss/get-physics-done · 40 tokens

pairwise-meta-analyst

Expert in frequentist and Bayesian pairwise meta-analysis using meta, metafor, and bayesmeta packages. Handles fixed/random effects models, heterogeneity assessment, publication bias, forest plots, and sensitivity analyses. Use PROACTIVELY for pairwise MA tasks.

choxos/BiostatAgent · 60 tokens

module-creator

Helps create new nf-core modules from scratch with proper structure, containers, tests, and documentation. Use when wrapping new bioinformatics tools, creating custom modules, or contributing modules to nf-core/modules.

jonasscheid/claude-nfcore-plugin · 44 tokens

pipeline_orchestrator_agent

Orchestrates the full multi-skill academic research pipeline and manages agent handoffs across phases.

FridrichMethod/awesome-skills · 24 tokens