Paper writer

Skill ai4s-research/ai4s-skills/skills/paper-writer

Open-source agent skills for AI for Science: topic exploration, literature survey, experiments, paper writing, and integrity audit — driven by any coding agent.

Install

npx -y skills add ai4s-research/ai4s-skills --skill paper-writer

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

What its author says it does

Copied from the file, not written here

Use when the user wants a complete, publication-grade research paper on a specific topic — produces 200+ real citations, 4–8 publication-grade figures, and 7 sections of substantive prose compiled to PDF in one pass. No skeleton stage.

SKILL.md

9.3 KB, as published. Nobody here has run it

Paper Writer

Overview

End-to-end research paper builder. Single stage, full quality from the start — there is no skeleton phase to enrich later. The agent (Claude Code / Cursor / Aider / Codex / …) does the writing using its own tools (WebFetch, WebSearch, Write, Bash). This skill has no Python runtime; it is purely a procedure + reference playbooks + a LaTeX template.

The substantive work is decomposed into reference playbooks under references/:

Reference	Topic
`references/00-incremental-execution.md`	how to actually do this without losing work: batch sizes, persistence, resume — read first
`references/01-bibliography-expansion.md`	grow `bibliography.bib` to 200+ real entries via WebFetch/WebSearch
`references/02-figures-publication-grade.md`	TikZ / matplotlib / seaborn / multi-panel figure recipes
`references/03-section-playbook.md`	per-section structure, length, citation density
`references/04-layout-discipline.md`	tables, figures, floats, cross-refs, author + disclosure footnote
`references/05-quality-gate.md`	self-check before delivery (G1–G8 hard, S1–S4 soft)
`references/06-experiment-provenance.md`	honest provenance for every number (measured / simulated / illustrative)

Read the relevant reference before writing, not after.

The full pass does not fit in a single turn. The bibliography is built across ~20+ small WebFetch/WebSearch batches; sections are drafted one per turn; figures are generated one at a time. Read references/00-incremental-execution.md before starting — it is the only execution mode that actually completes without losing work.

When to Use

User asks to "write a paper" on a specific topic.
User wants Abstract + Introduction + Related Work + Method + Experiment + Results + Conclusion.
User has experiment results (a results.json) and wants them formatted into a paper.

When NOT to Use

User wants only a literature survey → the literature-survey skill.
User wants only the experiment package → the experiment-suite skill.
User wants only direction/topic exploration → the research-explorer skill.
User wants the full multi-skill pipeline → the ai4s-agent skill (which invokes this skill as one stage).

Workflow

Step 1 — Understand requirements

Confirm with the user:

Topic — specific enough to motivate a title; if too broad, narrow it before proceeding.
Experiment provenance — measured (user supplied a results.json produced by the experiment-suite skill or compatible) or simulated. Default is simulated; in that case the disclosure footnote must flag it (see references/06-experiment-provenance.md).
Language — default Chinese in conversation; the paper itself is English unless the user requests otherwise.

Always tell the user that human review by a domain expert is recommended before any scientific publication or production use.

Step 2 — Set up the run directory

Create a timestamped working directory and copy the template. Runs never overwrite each other.

TOPIC="<topic>"
SLUG=$(python3 -c "import re,hashlib,sys; t=sys.argv[1]; n=re.sub(r'[\\s_]+','-',re.sub(r'[^\\w\\s-]','',t.lower().strip())).strip('-')[:40].rstrip('-'); h=hashlib.sha1(t.encode()).hexdigest()[:8]; print(f'{n}-{h}')" "$TOPIC")
TS=$(date +%Y-%m-%d_%H%M%S)
RUN=output/paper-writer/$SLUG/$TS/paper

mkdir -p "$RUN/sections" "$RUN/figures"
cp -r templates/paper/. "$RUN/"
ln -sfn "$TS" "output/paper-writer/$SLUG/latest"

In commands below $RUN = output/paper-writer/<slug>/latest/paper.

The template provides only main.tex (title placeholder), an empty sections/ skeleton, an empty figures/, and compile.sh. Everything substantive is produced in Step 3 below.

Step 3 — Build the paper (REQUIRED — this is the whole job)

Open references/00-incremental-execution.md first. Then carry out the five tracks below across many turns, persisting state to $RUN/ after every batch.

3.1 Bibliography — 200+ real entries

Open: references/01-bibliography-expansion.md.

First choose and record the temporal profile from that reference. AI4S and similarly fast-moving fields default to at least 60% of references from the current calendar year and previous two years; an explicitly recent window uses the stricter recency-led profile. Then plan 15–25 query angles. For each angle: WebSearch → pick candidates → WebFetch each candidate's abstract / arXiv API URL → extract canonical title/authors/year/venue/url → append a BibTeX entry to $RUN/bibliography.bib. Every entry must originate from a URL fetched in this session. Memory entries are forbidden.

Hard stop: do not draft prose until the bibliography has ≥ 200 entries, contains no unknown keys, and passes check_bibliography_freshness.py for the recorded profile.

3.2 Figures — 4–8 publication-grade

Open: references/02-figures-publication-grade.md.

Decide what the paper needs based on its claims and evidence:

Architecture / pipeline diagram only when the paper introduces or compares a real method whose mechanism needs explanation.
Quantitative comparison plots when measured or explicitly simulated results support them.
Heatmap / multi-panel ablation only when the data justifies it.

Generate each figure into $RUN/figures/. Save the matplotlib / TikZ source alongside the PDF so each figure is reproducible. If the experiment-suite produced a figures/manifest.json, reuse those figures by symlink or copy — don't redraw what's already produced.

3.3 Sections — 7 substantive .tex files

Open: references/03-section-playbook.md.

Draft each section per its playbook (length, structure, citation density, equation requirements, anti-patterns). Cite real entries from the bib built in 3.1.

Order: introduction → related_work → method → experiment → results → conclusion → abstract last (you only know the paper's shape after writing the rest).

3.4 Layout discipline

Open: references/04-layout-discipline.md.

Put each figure or table in the section whose prose first introduces or interprets it, immediately after that paragraph in the source. Do not collect artifacts in a fixed section or force one float placement across the paper.
Wrap tables and figures in standard LaTeX floats with standalone captions; choose [htbp], [tbp], or [p] from the artifact's size and narrative role.
Use ~\cite{} and ~\ref{} (non-breaking space).
Let LaTeX assign citation, figure, table, equation, section, and algorithm numbers from 1 in first-appearance order. Never type display numbers manually.
Set \author{AI4S Agent} and attach a \thanks footnote that always recommends human review, and additionally flags simulated numerics when applicable.

3.5 Compile + quality gate

cd "$RUN"
pdflatex -interaction=nonstopmode main.tex
bibtex main
pdflatex -interaction=nonstopmode main.tex
pdflatex -interaction=nonstopmode main.tex

Open: references/05-quality-gate.md.

Run all G1–G8 hard gates and S1–S4 soft gates. If a hard gate fails, fix and re-run; do not ship a paper that fails G1–G4. If you cannot honestly clear a gate (e.g., bibliography stalled at 156 entries because the topic is niche), say so explicitly instead of padding.

Step 4 — Deliver

Report to the user:

output/paper-writer/<slug>/latest/paper/main.pdf — final PDF.
output/paper-writer/<slug>/latest/paper/ — complete LaTeX project (reproducible).
Stats per the report format in references/05-quality-gate.md (pages, bib size, total \cite{}, figure count, table count, provenance, compile warnings).

Cross-skill data flow (path convention)

If a sibling skill has already run for the same topic, reuse its outputs by path:

output/literature-survey/<slug>/latest/bibliography.bib → seed $RUN/bibliography.bib (still bring it up to 200+ in 3.1 with WebFetch).
output/experiment-suite/<slug>/latest/results.json → the source of the numbers cited in 3.3 / 3.5; its simulated flag controls the disclosure clause in 3.4.
output/experiment-suite/<slug>/latest/figures/*.pdf (+ manifest.json) → reuse in 3.2 rather than redrawing.

The slug formula in Step 2 is the contract; all four skills compute the same slug for the same topic.

Important rules

No LLM SDK in this skill. No import anthropic / import openai. The agent runs the procedure; the skill is just SKILL.md + references + template.
No fabricated citations. Every BibTeX entry must trace back to a URL fetched this session. Real or weaker claim — never fake reference.
Simulated numbers stay visibly labelled. Title \thanks + abstract disclosure paragraph + per-caption disclosure. Do not let the simulated label disappear during drafting.
Honest stop > padding. If the topic is too niche for 200+ real citations, say so to the user instead of inventing entries.
Real-paper scope is 8–14 pages with 200+ references. For workshop / blog format, adjust scope explicitly with the user up front.