Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/woodfishhhh/EZ_math_modelnpx agentmods add skills/woodfishhhh/ez_math_model/paper-orchestraWrote 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/woodfishhhh/ez_math_model/paper-orchestra)<a href="https://agentmods.dev/skills/woodfishhhh/ez_math_model/paper-orchestra"><img src="https://agentmods.dev/badge/skills/woodfishhhh/ez_math_model/paper-orchestra/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/woodfishhhh/ez_math_model/paper-orchestra"><img src="https://agentmods.dev/badge/skills/woodfishhhh/ez_math_model/paper-orchestra.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 | $0.00143 | $0.02913 |
| Opus 5 | $0.00072 | $0.01456 |
| Sonnet 5 | $0.00029 | $0.00583 |
| Haiku 4.5 | $0.00014 | $0.00291 |
Grade A, and why
paper-orchestra 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 10d 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.
This is a copy
100% identical to paper-orchestra — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 264 lines — stays where its author put it; the contents beside it link to each section on GitHub.
paper-orchestra (Orchestrator)
Top-level driver for the PaperOrchestra pipeline. Read this document and follow
the steps below. The detailed prompts and rules live in each sub-skill's
SKILL.md and references/ directories — you (the host agent) will load them
as you go.
Source paper: Song et al., PaperOrchestra: A Multi-Agent Framework for Automated AI Research Paper Writing, arXiv:2604.05018, 2026. https://arxiv.org/pdf/2604.05018
What this skill produces
A complete submission package P = (paper.tex, paper.pdf) written into
workspace/final/, plus a full audit trail under workspace/ (outline,
figures, refs, drafts, refinement worklog, provenance snapshot).
Inputs (the (I, E, T, G, F) tuple from the paper)
The workspace MUST contain:
| File | Symbol | Required | Description |
|---|---|---|---|
workspace/inputs/idea.md |
I |
yes | Idea Summary (Sparse or Dense variant — see references/io-contract.md) |
workspace/inputs/experimental_log.md |
E |
yes | Experimental Log: setup, raw numeric data, qualitative observations |
workspace/inputs/template.tex |
T |
yes | LaTeX template for the target conference (with \section{...} commands) |
workspace/inputs/conference_guidelines.md |
G |
yes | Formatting rules, page limit, mandatory sections |
workspace/inputs/figures/ |
F |
no | Optional pre-existing figures. If empty, the plotting agent generates everything. |
scripts/init_workspace.py will scaffold this layout. scripts/validate_inputs.py
will check it before the pipeline runs.
Pipeline (read references/pipeline.md for the full diagram)
Step 1: Outline ──▶ outline.json (1 LLM call)
Step 2: Plotting ─┐
├──▶ figures/*.png + captions.json (~20-30 calls)
Step 3: Lit Review ─┘ (~20-30 calls)
intro_relwork.tex + refs.bib
Step 4: Section Writing ──▶ drafts/paper.tex (1 LLM call)
Step 5: Content Refine ──▶ final/paper.tex + final/paper.pdf (~5-7 calls, ~3 iters)
What ships with it
12 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/anti-leakage-prompt.md 2.5 KB
- references/host-integration.md 7.8 KB
- references/io-contract.md 7.1 KB
- references/paper-summary.md 4.1 KB
- references/pipeline.md 7.2 KB
- scripts/anti_leakage_check.py 3.3 KB runs code
- scripts/build_pdf.py 28 KB runs code
- scripts/check_idea_density.py 5.6 KB runs code
- scripts/check_tex_packages.py 5.9 KB runs code
- scripts/init_workspace.py 4.5 KB runs code
- scripts/validate_consistency.py 9.7 KB runs code
- scripts/validate_inputs.py 4.8 KB runs code
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.
- 10d ago First seen · 264 lines · 143 tokens per session scan A db87ce7c41cb
paper-orchestra is a skill published in the GitHub repository woodfishhhh/EZ_math_model (41 stars, last pushed 1mo ago), licensed MIT. It adds 143 tokens to every session and 2,913 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to paper-orchestra, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
math-modeling-solver
A Chinese-language guide for solving mathematical modeling competition problems. It covers China’s CUMCM and America’s MCM/ICM, where teams use mathematics and code to answer real-world problem statements.
mathmodel-skill
An end-to-end workflow for mathematical-modeling competitions, including CUMCM, MCM/ICM, and the Electrical Cup. It guides teams from choosing a problem through modeling, solving, checking, writing, rule compliance, and final review.
math-modeling-paper
A Chinese-language guide for writing papers for mathematical modelling competitions, where teams use mathematics and data to study a real-world problem.
interpret-modeling-problems
A method for turning a mathematical modelling contest problem and its attachments into a checked plan for solving and documenting it.
1start-mathmodel
A workflow entry point for the Chinese national mathematical modelling competition. It coordinates problem analysis, modelling, programming, diagrams, paper writing, and verification.
math-modeling-finalizer
A finalization guide for mathematical-modeling projects after the main results are largely fixed.