NeuroArxiv — a skill to kill from-scratch coding

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This is where the real-time thinking happens: arXiv category coverage, eval design, prior-art hunting, and neurodivergence-inspired research on reasoning architectures — shared with the ADHD community. Got opinions on isolation discipline, corpus gaps, or just want to argue about the next eval problem — come argue with us live.

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Before Claude designs something new, it checks arXiv first.

Real papers, fetched over real HTTP, read in isolation so no source anchors another, converged into ONE recommendation — cited, with a first step and the known ways this has already gone wrong for somebody else. Not a search wrapper: search finds you sources, NeuroArxiv forces a decision grounded in them.

Reach for it before committing to non-trivial architecture, algorithms, or systems techniques — anywhere real prior art plausibly exists and the cost of guessing wrong is a rebuild, not a typo.

👤 Author: Udit Akhouri — github.com/UditAkhourii

Fair fight: cold vs. general web search vs. NeuroArxiv

Not "research vs. no research" — that's an easy win for anyone. The real question is whether NeuroArxiv's isolate-then-converge discipline beats a plain, capable agent with normal web + arXiv access and no special process. Same model, same 5 cross-domain problems (physics, applied math, quantitative biology, ML, statistics), three conditions, run independently. A sample of the web-search condition's citations was verified against the real arXiv API before anything below was scored — it's a genuinely grounded condition, not a strawman.

The headline isn't a ratio. Every transcript was re-read for one specific pattern: does the answer name a source it just cited and flag a real limitation in that source's own claim — not a generic risk, a documented weakness in the specific paper.

| | Cold | Web + arXiv (undisciplined) | NeuroArxiv | | --- | :---: | :---: | :---: | | Problems with a source-skepticism flag | 0/5 | 0/5 | 5/5 | | Total flags | 0 | 0 | 7 |

Zero vs. zero vs. seven, out of five problems each. NeuroArxiv caught a withdrawn proof it had cited and declined to rely on it. It caught a benchmark result validated at only one context length and flagged it before recommending the approach. Cold and web-search both produced real, reasonable answers — neither produced that.

On raw answer quality (specificity, risk quality), NeuroArxiv beat the web-search condition by a modest 1.1x–1.3x, and lost on citation breadth in 2 of 5 problems — arXiv-only search has a narrower net than general web search, and that's reported, not hidden. Full scorecard, per-problem transcripts, and every honest limitation: EVALS.md · raw data: bench/deep-tech-eval-transcripts.md.

Install

One line, no clone, no build step of your own — drops the skill straight into ~/.claude/skills/neuroarxiv:

npx github:UditAkhourii/neuroarxiv install

Restart Claude Code (or start a new session) and /neuroarxiv "<problem>" is live.

Prefer a full local checkout (editing the engine, running the CLI directly, contributing)?

git clone https://github.com/UditAkhourii/neuroarxiv.git
cd neuroarxiv
npm install
npm run build
node dist/cli.js install

Quickstart

neuroarxiv "cache LLM completions across requests without serving stale answers"
neuroarxiv "leader election for a queue with flaky nodes" --papers 6

Inside Claude Code, no install is required to try it once — the skill in skills/neuroarxiv/SKILL.md runs the same loop using WebFetch against arXiv's export API directly. Full flag reference: neuroarxiv --help.

How it works

PROBLEM
  │
  ▼
CATEGORIZE  — map the problem onto 3-5 arXiv categories + search terms
  │
  ▼
FETCH       — real HTTP against export.arxiv.org, category by category
  │               (no LLM call — deterministic, courtesy-rate-limited)
  ▼
DIVERGE     — one isolated LLM read per paper, in parallel
  │               (each sees ONE abstract, never the others)
  ▼
SCORE       — relevance / practicality / rigor, per paper
   + CLUSTER   — group by underlying architectural angle
  │
  ▼
CONVERGE    — pick ONE cluster as the recommended path, synthesize,
                 cite, name the first step, name the risk, list pitfalls
                 pulled from EVERY paper's limitation — not just the winner's

Convergence is the deliberate departure from open-ended research tools: NeuroArxiv doesn't hand back "here are 4 papers, you decide." It commits to one recommendation, states why the runner-ups lost, and names what to watch for even in the paths not taken.

Every claim traces to a fetched abstract — papers, ids, and links are real arXiv metadata, never invented. The read prompt is explicitly forbidden from quoting more than a few words verbatim, and the skill's anti-patterns section calls out hallucinated citations as the failure mode to watch for.

License

MIT

Skill: skills/neuroarxiv/SKILL.md · Eval methodology: EVALS.md

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