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 skills add MadGraphTeam/MadAgents --skill mg-setupgit 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/skills/madgraphteam/madagents/mg-setup)<a href="https://agentmods.dev/skills/madgraphteam/madagents/mg-setup"><img src="https://agentmods.dev/badge/skills/madgraphteam/madagents/mg-setup/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/madgraphteam/madagents/mg-setup"><img src="https://agentmods.dev/badge/skills/madgraphteam/madagents/mg-setup.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 | $0.00129 | $0.01086 |
| Opus 5.5 | $0.00052 | $0.00434 |
| Sonnet 5.5 | $0.00026 | $0.00217 |
| Haiku 4.5 | $0.00013 | $0.00109 |
Grade A, and why
mg-setup 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 — 29 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/mg-setup
The setup workflow — the four-step sequence for building a MadGraph configuration. The orchestration disciplines it leans on are already in your context, not restated here: dual-spec, regime classification, slice-boundary premises, whole-spec reconciliation, revise-before-caveat, and "a clean run is not evidence" all live in lead-discipline.md. This skill is the setup-specific scaffold over them.
Step 1 — Frame the physics-spec. Capture the user's prompt verbatim (dual-spec discipline). If too vague to act, surface and ask.
Step 2 — Classify the regime. Classify the physics regime (regime-classification discipline) and route to the slices each regime implicates — sub-threshold parent → ma-bw-window-consultant; NLO bracket syntax → ma-nlo-syntax-consultant; EFT operators → ma-eft-consultant; and so on.
Step 3 — Build the candidate. Translate physics-spec + regime classification into a candidate MadGraph configuration. Dispatch the slices the regime named plus any others your reading requires; consultants author within slice, you conduct, iterate as returns reveal what's next.
The simulation-spec is what the configured artifacts do, not what you meant them to do, and this is exactly where the two silently diverge. A model name does not fix its vertices, a restriction card silently prunes couplings, a chain drops a sub-decay the parentheses never bound — each artifact is opaque until its owning slice reads it. So build every load-bearing choice on what the artifact actually carries, read by its owner, in both directions: a contribution the physics needs can be silently absent (a dead process, a removed coupling), and one you never intended silently present (a contaminating topology, an extra coupling). On a don't-run setup both are invisible until something is read.
The build is not done until each load-bearing choice is grounded in what its artifact does — never its name — and the candidate is coherent across stages.
Step 4 — Reconcile and present. Compare the assembled simulation-spec against the physics-spec, then ship.
- Per-requirement. Verify each physics-spec requirement is satisfied by the assembled simulation-spec, given everything else now in it. A DIRECT-cited value carries its own evidence; re-dispatch the owning slice only to Establish a load-bearing value that shipped non-DIRECT.
- Whole-spec. An assembly of individually-correct slice outputs can still be globally wrong — a dead process, a form that computes a different observable, a contaminating topology, a silently dropped coupling. Confirm the emergent cross-slice invariants with a lens that did not build them:
- Trivial invariants (BR ≤ 1 arithmetic, a count, a sign) you check yourself — no dispatch.
- Runtime realization — invoke
/mg-probeon the assembled commands with a target-derived expectation list (per/mg-probe), drawn from Step 2's regime classification; re-dispatchma-physics-consultantfor the target-state only if that classification is not specific enough. The probe is the independent lens — it acts (reads the generated artifacts), where the build did not; its card carries the cheap-vs-launch discipline.
- Reconcile. A deviation from a target-derived expectation is a finding: revise via the owning slice (revise-before-caveat). A revision does not close the finding — re-verify a substantive fix (per Re-verify a fix in lead-discipline): re-probe once against the revised artifact, the expectation list widened to the fix's blast radius (the deviated expectation plus the ones that passed before the fix); a trivial source-cited fix clears by inspection. Risk gate: if after this a load-bearing part of the spec remains unestablished — a finding that will not settle, a substantive fix the re-probe could not clear, a non-DIRECT load-bearing claim you could not establish, or an invariant the source cannot settle — do not bury it in a caveat: surface it prominently and recommend
/mg-deep-verify, leaving the call to the user. Do not loop here, and do not auto-invoke the cascade. The gate is tight — a clean build settles and never trips it.
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 · 29 lines · 129 tokens per session scan A 2b0e26b0043a
mg-setup is a skill published in the GitHub repository MadGraphTeam/MadAgents (14 stars, last pushed 2mo ago), licensed MIT. It adds 129 tokens to every session and 1,086 once invoked, about $0.0005 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 2026-09-03.
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