literature-review

Dieser Auszug wurde bei der Prüfung gespeichert. Die externe Quelle enthält die vollständige und aktuelle Version.

---
name: literature-review
description: Builds a structured literature review — survey of prior work, state of the art, related-work landscape — where every claim cites a verified reference. Use when the user says "literature review", "lit review", "survey the papers on X", "what's the state of the art", "synthesize prior work", or "organize these papers by theme". Composes sibling skills, search via find-papers (key-free DBLP/Crossref/Semantic Scholar/arXiv), full text via fetch-paper (legal open-access only), and citation checking via verify-citations. Tracks a screening corpus, extracts claims with section-level anchors, organizes by theme, and gates the final document with a deterministic checker so no claim ships uncited, unverified, or resting on a fabricated reference.
---

# Literature Review

Produce a themed, citation-grounded review document from a research question.
This skill is the orchestrator: searching, fetching, and citation verification
are delegated to sibling skills; this skill owns the workspace, the screening
corpus, claim extraction, thematic synthesis, and the final coverage gate.

## When to use

- "Do a literature review on X" / "survey recent work on X"
- "What's the state of the art in X since 2023?"
- "Read these N papers and synthesize them by theme"
- A standalone survey is wanted. For a paper's Related Work *section*,
  do the corpus-building phases here, then hand off to `draft-related-work`.

## Inputs

- A research question or topic, ideally with year range and (optionally)
  target venues.
- `CONTACT_EMAIL` exported for the sibling skills' API politeness contract.
- Optional: an existing list of papers/DOIs the user already has.

## Sibling skills this skill delegates to

| Stage | Delegate to | What it provides |
|---|---|---|
| Search | `find-papers` | `dblp_search.py`, `crossref_search.py`, `s2_search.py`, `arxiv_search.py` (in that skill's script dir) — key-free, rate-limited, cached |
| Full text | `fetch-paper` | `resolve_oa.py` — one DOI/arXiv ID → legal OA copy, transient |
| Reference check | `verify-citations` | validates every BibTeX entry against Crossref/DBLP/S2, flags retractions |

Never reimplement these inline (no ad-hoc `curl` against scholarly APIs);
the sibling scripts carry the rate-limit, backoff, caching, and User-Agent
contract.

## Process

### Phase 1 — Scope

1. Pin down with the user: research question, year range, inclusion and
   exclusion criteria (2–4 each), target size (10–15 papers is a solid
   default; >30 needs explicit user buy-in).
2. Run `python3 scripts/init_review.py "TOPIC"` — creates
   `lit-review/<slug>/` with `corpus.json`, `themes.yml`, `notes/`, and a
   `review.md` skeleton.
3. Write the agreed criteria into `corpus.json` under `criteria`.

### Phase 2 — Search (delegate to find-papers)

1. Follow [references/methodology.md](references/methodology.md) for query
   design, venue enumeration, and snowballing.
2. If the scope names venues, read `venues/conferences/<id>.yml` for the
   `aliases` block (DBLP key, S2 venue string, Crossref container title) —
   this solves the venue-alias problem. Profiles are a starting point: if any
   profile fact becomes load-bearing for the review (e.g. which tracks exist),
   re-verify it against the live `cfp_url` before relying on it.
3. Record every search hit batch:
   `python3 scripts/corpus.py --corpus <ws>/corpus.json import hits.json
   --source dblp --query "..."` (accepts `find-papers --json` output;
   dedupes on DOI). Add single papers with `corpus.py add`.
4. Snowball from **multiple, topically-diverse seeds — one per theme**, not a
   single convenient paper (which drifts the harvested set into one sub-area
   and misses the rest). Admit each neighbor only if you can name its role in
   the question's argument (method-we-extend / baseline / eval-task /
   foundational-lineage), and run the co-citation sanity check before
   screening. Full protocol in
   [references/methodology.md](references/