literature-engineer

# Literature Engineer (evidence collector) Goal: build a **large, verifiable candidate pool** for downstream dedupe/rank, mapping, notes, citations, and drafting. This skill is intentionally **evidence-first**: if you can't reach the target size with verifiable IDs/provenance, the correct behavior is to **block** and ask for more exports / enable network, not to fabricate. ## Load Order Always read: - `references/domain_pack_overview.md` — how domain packs drive topic-specific behavior Domain packs (loaded by topic match): - `assets/domain_packs/llm_agents.json` — pinned classic/survey arXiv IDs for LLM agent topics ## Script Boundary Use `scripts/run.py` only for: - multi-route offline import, normalization, and provenance tagging - online arXiv/Semantic Scholar API retrieval - snowball expansion and deduplication - retrieval report generation Do not treat `run.py` as the place for: - hardcoded pinned arXiv ID lists (use domain packs) - hardcoded topic detection logic (use domain packs) ## Inputs - `queries.md` - `keywords`, `exclude`, `max_results`, `time window` - Optional offline sources (any combination; all are merged): - `papers/import.(csv|json|jsonl|bib)` - `papers/arxiv_export.(csv|json|jsonl|bib)` - `papers/imports/*.(csv|json|jsonl|bib)` - Optional snowball exports (offline): - `papers/snowball/*.(csv|json|jsonl|bib)` ## Outputs - `papers/papers_raw.jsonl` - 1 record per line; minimum fields: - `title` (str), `authors` (list[str]), `yea...