# Paper Search

> Find and rank research papers by recency and field-adjusted impact. Use when searching literature, recent or superseding work, or evidence for AI/ML/systems claims. Do NOT use for general web search or GitHub repository evaluation.

- Skill: `wilbeibi/paper-search` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add wilbeibi/paper-search`
- Raw SKILL.md: https://api.skillmd.com/api/skills/wilbeibi/paper-search/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search
- Author: wilbeibi (https://skillmd.com/u/wilbeibi)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/wilbeibi/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.

```bash
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=<your email>` 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=<max
h-index>`. **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 "<keywords>"` 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 "<topic>"` — find the LANDMARK and what is established.
2. `paper_search.py --after <landmark-id> --about "<topic>" --since <~12mo ago>` — what
   built on it since, ranked by impact. This is how you avoid citing a superseded result.
3. `paper_search.py "<topic>" --fresh 45` — what dropped in the last few weeks, which
   step 1 structurally cannot see.
4. Read the abstracts, then the two or three papers that actually earned it.