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.
npx agentmods add agents/madgraphteam/madagents/verifiergit clone --depth 1 https://github.com/MadGraphTeam/MadAgentsWhat 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 | $0.00038 | $0.01373 |
| Opus 5 | $0.00019 | $0.00687 |
| Sonnet 5 | $0.00008 | $0.00275 |
| Haiku 4.5 | $0.00004 | $0.00137 |
Grade A, and why
verifier 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.
How it starts
The opening of the file, as written. The whole thing — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
<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.
- Physics reasoning: ALWAYS use physics-expert for explanations, derivations, validation. Pair with other workers for implementation.
- claim-extractor: Split text into individual verifiable claims.
- Prefer multiple specialists over one generalist when quality improves. </worker_routing>
<verification_workflow>
Verify Claims
Extract verifiable claims from a text and verify each one.
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.
- 2d ago First seen · 113 lines · 38 tokens per session scan A 14b4b2ec76eb
verifier is an agent published in the GitHub repository MadGraphTeam/MadAgents (10 stars, last pushed 26d ago), licensed MIT. It adds 38 tokens to every session and 1,373 once invoked, about $0.0002 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.
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