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/raja21068/AutoResearchnpx agentmods add skills/raja21068/autoresearch/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/raja21068/autoresearch/paper-orchestra)<a href="https://agentmods.dev/skills/raja21068/autoresearch/paper-orchestra"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/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/raja21068/autoresearch/paper-orchestra"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/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 | $0.02698 |
| Opus 5 | $0.00072 | $0.01349 |
| Sonnet 5 | $0.00029 | $0.00540 |
| Haiku 4.5 | $0.00014 | $0.00270 |
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 8d 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
92% identical to paper-orchestra — 25 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 — 241 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
10 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
- references/single-agent-baseline.md 2.7 KB
- scripts/anti_leakage_check.py 3.3 KB runs code
- scripts/check_tex_packages.py 5.9 KB runs code
- scripts/init_workspace.py 2.5 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.
- 8d ago First seen · 241 lines · 143 tokens per session scan A a706a31bc098
paper-orchestra is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 143 tokens to every session and 2,698 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to paper-orchestra, differing in 25 lines, and is treated as a copy.
Other skills, from other repositories
content-refinement-agent
Step 5 of the PaperOrchestra pipeline (arXiv:2604.05018). Iteratively refine drafts/paper.tex by simulating peer review and applying targeted revisions, with strict accept/revert halt rules, deterministic 0-100 decision bands (Accept/Minor/Major/Reject) that drive a target-met early stop, and a Devil's Advocate…
section-writing-agent
Step 4 of the PaperOrchestra pipeline (arXiv:2604.05018). ONE single multimodal LLM call that drafts the remaining paper sections (Abstract, Methodology, Experiments, Conclusion), extracts numeric values from experimentallog.md into LaTeX booktabs tables, splices the generated figures from Step 2, and merges…
plotting-agent
Step 2 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the visualization plan from outline.json — render plots and conceptual diagrams from experimentallog.md and idea.md, optionally refine via VLM critique loop, and produce context-aware captions. Runs in parallel with the literature-review-agent. TRIGGER…
outline-agent
Step 1 of the PaperOrchestra pipeline (arXiv:2604.05018). Convert (idea.md, experimentallog.md, template.tex, conferenceguidelines.md) into a strict JSON outline containing a plotting plan, literature search plan (Intro + Related Work), and section-level writing plan with citation hints. TRIGGER when the orchestrator…
paper-autoraters
Run the four paper-quality autoraters from PaperOrchestra (arXiv:2604.05018, App. F.3) — Citation F1 (P0/P1 partition + Precision/Recall/F1), Literature Review Quality (6-axis 0-100 with anti-inflation rules), SxS Overall Paper Quality (side-by-side), and SxS Literature Review Quality (side-by-side). TRIGGER when the…
paper-writing-bench
Reverse-engineer raw materials (Sparse idea, Dense idea, experimental log) from an existing AI research paper to build a benchmark case for evaluating paper-writing pipelines. Replicates the PaperWritingBench dataset construction procedure from arXiv:2604.05018 §3 / App. C. TRIGGER when the user asks to "build a…