literature-survey

name	literature-survey
description	Use when the user wants a comprehensive literature survey on a specific research topic. Outputs a complete PDF survey (6–20 pages, 60+ real citations, 100+ recommended) with LaTeX source, taxonomy figures, and a classified literature table. Single-stage, no Python runtime.

Literature Survey

Overview

End-to-end literature survey builder. Single stage, full quality from the start. The agent (Claude Code / Cursor / Aider / Codex / …) does the entire build using its own tools (WebFetch, WebSearch, Write, Bash). This SKILL is procedure + reference playbooks + LaTeX template — no Python runtime, no LLM SDK.

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 60+ real entries (100+ recommended) via WebFetch (no memory)
references/02-survey-figures.md	taxonomy / timeline / coverage-matrix / area-map figures
references/03-survey-section-playbook.md	per-section structure for survey-shaped papers
references/04-layout-discipline.md	tables, figures, floats, cross-refs, author + disclosure footnote
references/05-quality-gate.md	self-check before delivery

Read the relevant reference before writing, not after. The full pass does not fit in a single turn — references/00-incremental-execution.md is the only execution mode that completes.

When to Use

• User asks for a "survey" / "review" on a specific topic.
• User has a research topic and wants a structured map of the field with citations.
• User needs background reading curated for a thesis chapter or grant section.

When NOT to Use

• User wants original research with experiments → paper-writer.
• User wants only an outline / topic exploration → research-explorer.
• User wants experiment code → experiment-suite.
• Topic is too broad (e.g., "all of AI") — narrow it before starting.

Workflow

Step 1 — Understand the topic and scope

Confirm with the user:

• Topic — specific research area (e.g., "federated learning in healthcare"). If too broad, narrow it first.
• Scope — broad survey of a field vs. focused review of a sub-area.
• Citation budget — minimum 60 unique entries; aim for 100+ (push higher for a broad survey).
• Language — default Chinese in conversation; the LaTeX paper is English unless requested otherwise.

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

Step 2 — Set up the run directory

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=$(date +%Y-%m-%d_%H%M%S)
RUN=output/literature-survey/$SLUG/$TS/survey_paper

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

In commands below $RUN = output/literature-survey/<slug>/latest/survey_paper.

Step 3 — Build the survey (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 — 60+ real entries (100+ recommended)

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

First (§0 of that reference): read the topic's temporal/scope intent and pick a search posture. If the topic names a year or says "latest/recent" (e.g. "OpenSource LLM 2026"), go recency-led — date-sorted arXiv queries carrying the explicit year, canon only as context. Otherwise span the timeline. This is what prevents "asked for 2026, got all 2024".

Then plan 12–20 query angles, weighted by the posture. For each angle, use AMiner academic search first, web search as supplement:

• AMiner search_papers — Use this FIRST for every query angle. It returns papers with structured metadata (title, authors, year, venue, DOI, citations, abstract) that is far more reliable than web-scraped results. Example: search_papers(query="federated learning survey", max_results=20, sort_by_citation=true).
• AMiner get_paper_details — For key papers, fetch full details (abstract, citations, references) by their AMiner IDs.
• web_search — Use when AMiner returns insufficient results, or for non-academic sources (blogs, benchmarks, datasets, tool documentation).

For each kept candidate: extract canonical title/authors/year/venue/url/citations from the AMiner response directly. For web-search-only results, WebFetch the abstract URL to extract metadata. Append a BibTeX entry to $RUN/bibliography.bib. Every entry must originate from a URL fetched in this session or from an AMiner search result. Memory entries forbidden.

Hard stop: do not draft prose until grep -c "^@" $RUN/bibliography.bib ≥ 60 (aim for 100+).

3.2 Figures — 6–10 survey-shaped

Open: references/02-survey-figures.md.

A survey is defined by how well it organises a field; figures carry that organisation:

• 1 taxonomy / classification diagram (TikZ hierarchy)
• 1 chronological timeline of major works
• 1 area / capability matrix (coverage heatmap)
• 1–2 representative architecture / mechanism diagrams
• 1–2 quantitative trend plots (matplotlib publication style)
• Optional: citation network, paradigm comparison

Save each into $RUN/figures/ with reproducible source alongside.

3.3 Sections — survey-shaped prose

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

Survey sections differ in shape from research-paper sections. Order: introduction → background → methods (themed survey) → discussion → conclusion → related work → abstract last.

3.4 Layout discipline

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

Wrap every table in \begin{table}[!t] with booktabs; every figure in \begin{figure}[!t]. Use ~\cite{} and ~\ref{}. Set \author{AI4S Agent} with a \thanks footnote that always recommends human review. Surveys carry no simulated numerical experiments, so do not include a simulated clause.

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. Survey-specific targets: ≥ 60 bib entries (100+ recommended), ≥ 6 pages, ≥ 1 taxonomy figure, ≥ 1 timeline.

If a gate cannot honestly be met (e.g., the field is genuinely small), say so explicitly. Do not pad.

Step 4 — Deliver

Report:

1. output/literature-survey/<slug>/latest/survey_paper/main.pdf
2. output/literature-survey/<slug>/latest/survey_paper/ — complete LaTeX project (reproducible)
3. output/literature-survey/<slug>/latest/literature_table.md — classified literature table (write this alongside the bib build)
4. Stats per the report format in references/05-quality-gate.md.

Cross-skill data flow (path convention)

A downstream skill (e.g., paper-writer) computing the same slug for the same topic will look here:

• output/literature-survey/<slug>/latest/survey_paper/bibliography.bib — bib starting point.

Important rules

• No LLM SDK in this skill. No import anthropic / import openai. The skill is SKILL.md + references + LaTeX template only.
• No fabricated citations. Every BibTeX entry must trace back to a URL fetched this session. Real or weaker claim — never fake reference.
• Honest stop > padding. If the field is too small for 60 real citations, say so to the user instead of inventing entries.
• Survey scope is 6–20 pages with 60–150 references (100+ recommended). For longer or shorter formats, adjust scope explicitly with the user up front.