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-studygit 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-study)<a href="https://agentmods.dev/skills/madgraphteam/madagents/mg-study"><img src="https://agentmods.dev/badge/skills/madgraphteam/madagents/mg-study/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-study"><img src="https://agentmods.dev/badge/skills/madgraphteam/madagents/mg-study.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 | $0.00141 | $0.01703 |
| Opus 5.5 | $0.00056 | $0.00681 |
| Sonnet 5.5 | $0.00028 | $0.00341 |
| Haiku 4.5 | $0.00014 | $0.00170 |
Grade A, and why
mg-study 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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/mg-study [topic]
/mg-study is a warm-up skill: it scans MadGraph source ahead of any task and caches what it finds, so the agent enters later work already knowing its way around — instead of accumulating knowledge only as a byproduct of answering questions. Invoke it deliberately between tasks. It is not auto-invoked.
/mg-study NLO, /mg-study decays, /mg-study the EFT machinery — study a topic. Bare /mg-study — each consultant studies its own core area.
Orchestration
A per-owner parallel dispatch: the relevant consultants in parallel, each into its own consultants/<agent-name>/ subtree; the lead's own pass on lead/; aggregate.
- Topic given — dispatch the consultants that own the topic (NLO → the nlo + loop slices (
nlo-syntax,amcatnlo,madloop,fks, ...); decays → the decay + downstream slices (chain-decay,bw-window,madspin-interface, ...)). Route by slice ownership as for any dispatch. - No topic — dispatch each consultant to study its own core source areas (the ones its card lists under "Source code areas").
Per-consultant dispatch — four phases
Each consultant, dispatched on its own subtree, first reads its existing pages (always gap-aware: add what is missing, extend what is thin, correct what source now contradicts; never duplicate). Then it works the area through four phases, by reading source:
- Explore — locate the relevant files and functions; record paths and structure.
- Physics — extract what the code computes and the physics/algorithm it encodes. Understanding, not paraphrase.
- Gaps — record what cannot be settled from source alone.
- Cautions — flag source-visible hazards: a default that assumes a regime, a silent fallback, a code path that drops a contribution. A caution is a pointer ("watch out for X here"), not a claim about the runtime outcome.
What each phase records
- Facts — input-independent and statically verifiable: files scanned, verbatim content, file:line citations, what the code computes as readable from source, and cautions grounded in a visible default. Cache at confidence. A study page is a cited source record of your slice, not advice. One exception — a version-dependent numeric answer (a default value, an exact count) is not a cached fact: record its registration site and the one-line recipe to read it; the number is read fresh at need, never stored (see
ma-wiki-write's "Cache the lookup, not the value"). - Probe-candidates — any runtime prediction or open question a phase raises (a comprehension prediction "this should produce …"; a gap "can't tell from source whether …"; a caution's runtime consequence "this may silently give …"). A runtime claim is never cached as fact from reading alone.
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 · 68 lines · 141 tokens per session scan A 715b7f77901e
mg-study is a skill published in the GitHub repository MadGraphTeam/MadAgents (14 stars, last pushed 2mo ago), licensed MIT. It adds 141 tokens to every session and 1,703 once invoked, about $0.0006 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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