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/section-writing-agentWrote 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/section-writing-agent)<a href="https://agentmods.dev/skills/raja21068/autoresearch/section-writing-agent"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/section-writing-agent/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/section-writing-agent"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/section-writing-agent.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 | $0.00125 | $0.02695 |
| Opus 5 | $0.00063 | $0.01347 |
| Sonnet 5 | $0.00025 | $0.00539 |
| Haiku 4.5 | $0.00013 | $0.00269 |
Grade A, and why
section-writing-agent 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 5d 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 section-writing-agent — 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 — 232 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Section Writing Agent (Step 4)
Faithful implementation of the Section Writing Agent from PaperOrchestra (Song et al., 2026, arXiv:2604.05018, §4 Step 4, App. F.1 pp. 47–49).
Cost: ONE LLM call (App. B: "Section Writing Agent (1 call): A single, comprehensive multimodal call to draft and compile the complete LaTeX manuscript"). Do NOT split this into per-section calls — the paper explicitly designs it as one comprehensive call so the model can maintain global coherence across sections.
Inputs
workspace/outline.json— the master planworkspace/inputs/idea.md— technical detailsworkspace/inputs/experimental_log.md— raw data for tables and qualitative analysisworkspace/drafts/intro_relwork.tex— the template with Intro + Related Work already filled in by Step 3. This is your starting point. The preamble, package list, style, and the two pre-filled sections must be preserved verbatim.workspace/citation_pool.json— the citation map ({key, title, abstract}for each verified paper)workspace/refs.bib— the BibTeX fileworkspace/inputs/conference_guidelines.md— formatting rulesworkspace/figures/— the actual PNG files from Step 2 (used as multimodal vision input!)workspace/figures/captions.json— caption text per figure_idworkspace/tex_profile.json— TeX package availability flags (written bycheck_tex_packages.pyat Step 0). Read this before generating any LaTeX. It tells you which packages are installed so you select the right cross-reference pattern, font packages, etc. before you write — not after you try to compile.
Output
workspace/drafts/paper.tex— the complete LaTeX paper, with all sections filled. The Step 5 Refinement Agent will iterate on this file.
How to do it
0.5. Read tex_profile.json and select LaTeX patterns
Before composing the prompt, read workspace/tex_profile.json and apply
these rules to every LaTeX choice in the generated paper:
| Profile flag | True → use | False → use instead |
|---|---|---|
use_cleveref |
\cref{fig:X}, \cref{tab:Y} |
Figure~\ref{fig:X}, Table~\ref{tab:Y} |
use_nicefrac |
\nicefrac{a}{b} |
$a/b$ |
use_microtype |
\usepackage{microtype} |
omit the line |
use_t1_fontenc |
\usepackage[T1]{fontenc} |
omit the line |
What ships with it
6 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.
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.
- 5d ago First seen · 232 lines · 125 tokens per session scan A f98b19734962
section-writing-agent is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 125 tokens to every session and 2,695 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to section-writing-agent, differing in 0 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…