Paper Search

paper-search

Search OpenAlex + arXiv and rank by impact relative to the paper's own field and age — not raw citation counts, which always favor old papers and always bury the work published last month. No API key, no auth.

scripts/paper_search.py "lost in the middle long context"        # topic search
scripts/paper_search.py "kv cache compression" --since 2025-06   # only recent work
scripts/paper_search.py "agent memory" --fresh 60                # + arXiv, last 60 days
scripts/paper_search.py --after arXiv:2307.03172 --about "position bias"  # what built on it
scripts/paper_search.py "raft consensus" --field any --limit 25  # non-CS, or wider
scripts/paper_search.py --selftest                               # offline; no network

--after takes arXiv:ID, DOI:x, or an OpenAlex Wxxx. --json for machine output; --help for all flags. Set OPENALEX_MAILTO= for OpenAlex's polite pool.

Reading the output

Papers are bucketed, best first. The buckets are the point — recency and quality are independent axes, and collapsing them into one score hides exactly the tradeoff that matters in a fast-moving field.

| Bucket | Means | |---|---| | LANDMARK | cited fast and far above its field — read this first | | STRONG | peer-reviewed, comfortably above field average | | RISING | recent and being picked up quickly | | FRESH+ | too new to be cited, but credible venue or authors | | OK | real, unremarkable | | FRESH? | too new to be cited and unvetted — verify it yourself | | THIN | uncited preprint, authors with no track record — usually skip |

Each line shows date (age) · citations (velocity) · fwci · [tier] venue · authors h=. FWCI is field-weighted citation impact: 1.0 = exactly the average for that field and year, so it lets a systems paper with 90 citations correctly outrank an LLM paper with 300. [TOP] = top-tier venue (NeurIPS/ICML/ACL/OSDI/SOSP/VLDB/…).

Traps

• Citation counts are a lagging indicator. In AI/infra, the paper that matters may be

6 weeks old with zero citations. Never conclude "nothing exists" from a citation-ranked list — run --fresh 60 before saying a topic is unexplored.

• FWCI is noise below ~18 months — the expected-citation denominator is near zero, so

7 citations can score FWCI 108. The script ignores it below that age; don't reintroduce it by reading the raw number off a young paper.

• Preprint date ≠ publication date. A 2024 TACL paper may be a 2023 arXiv paper; the

idea landed 8 months before the venue date, and in a fast field that lead is the story. Output shows preprint YYYY-MM when they differ — cite the earlier one for priority.

• Author credibility is the weakest signal here; don't lean on it. OpenAlex conflates

common names ("Kevin Lin": h=75 across 825 works, several people), and h= is the max across authors — the value conflation inflates. Prefer the @ Stanford, Berkeley affiliation beside it. On --fresh arXiv hits both are absent by design: arXiv exposes no institutions, and name lookup is worse than useless ("Feng Wang" → 4,865 authors, so you'd attach a stranger's h-index). That is what FRESH? means — open the PDF.

• A landmark is cited by every field. --after on a famous paper returns medical and

legal applications too; pass --about "" to keep the frontier on topic.

• --field cs is the default. Pass --field any for anything else, or results look

mysteriously empty. Venue metadata is imperfect regardless — a paper published at EMNLP may still read [PREPRINT] arXiv, so trust the citation numbers over the tier label.

Workflow for a fast-moving topic

1. paper_search.py "" — find the LANDMARK and what is established.
2. paper_search.py --after --about "" --since — what

built on it since, ranked by impact. This is how you avoid citing a superseded result.

1. paper_search.py "" --fresh 45 — what dropped in the last few weeks, which

step 1 structurally cannot see.

1. Read the abstracts, then the two or three papers that actually earned it.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: wilbeibi
• Source: wilbeibi/wilbeibi-skills
• License: MIT

Install and usage instructions live in the source repository linked above.