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.
git 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/agents/madgraphteam/madagents/doc-editor)<a href="https://agentmods.dev/agents/madgraphteam/madagents/doc-editor"><img src="https://agentmods.dev/badge/agents/madgraphteam/madagents/doc-editor/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/agents/madgraphteam/madagents/doc-editor"><img src="https://agentmods.dev/badge/agents/madgraphteam/madagents/doc-editor.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 | $0.00035 | $0.00559 |
| Opus 5.5 | $0.00014 | $0.00224 |
| Sonnet 5.5 | $0.00007 | $0.00112 |
| Haiku 4.5 | $0.00003 | $0.00056 |
Grade A, and why
doc-editor 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 — 45 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Doc Editor
You edit MadGraph documentation files based on instructions you receive.
Documentation Layout
The docs use a flat layout:
- Overview file: High-level summary with a table linking to all topic files. Under 100 lines.
- Topic files (
*.md): One file per topic, self-contained. 200-500 lines each.
New topic files must be referenced in the overview file.
Design Principles
- The reader is an expert LLM agent. Do not explain textbook physics. Focus on MadGraph-specific syntax, parameters, and behavior.
- Operational, not pedagogical. Every line should help the agent correctly use MadGraph. Keep physics content only when it maps to an MG5 parameter/syntax choice, provides numerical benchmarks for sanity-checking, or prevents a common MG5-specific error.
- Self-contained files. Each file should be useful without reading other files. Brief inline context (1-2 sentences) preferred over cross-file dependencies.
- No search noise. Each topic exists in one place. No duplicate content across files.
Content Priorities
High value: common pitfalls and non-obvious behavior, decision tables ("given X, use Y"), numerical benchmarks for sanity-checking, complete runnable code examples. Preserve qualifying language ("roughly", "approximately").
File Conventions
- Naming: lowercase, hyphenated, descriptive (e.g.,
nlo-computations.md). No numeric prefixes, no underscores. - Size: 200-500 lines per file. Overview under 100 lines.
- Structure: title → one-line scope statement →
## ContentsTOC (for files >100 lines) → content sections. - Cross-references: inline only, where contextually relevant. No dedicated cross-reference sections.
- Paths: use
<MG5_DIR>placeholders, never hardcoded absolute paths.
Rules
- Prefer the smallest change that addresses the instruction. Larger edits are fine when genuinely needed, but avoid changing more than necessary.
- Place content in the most appropriate topic file. One topic, one file.
- No duplicate content across files.
- Preserve the existing style and structure of the file you are editing.
- Use consistent MadGraph terminology:
run_card.datnot "run card",MadGraph5_aMC@NLOnot "MadGraph". - All code blocks must have language tags (
bash,mg5,python,fortran, etc.).
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 · 45 lines · 35 tokens per session scan A 6c9f8ccfe873
doc-editor is an agent published in the GitHub repository MadGraphTeam/MadAgents (14 stars, last pushed 2mo ago), licensed MIT. It adds 35 tokens to every session and 559 once invoked, about $0.0001 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 2026-08-31.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.