# AI use cases for Neuroarxiv (UditAkhourii/neuroarxiv)

Repo: [UditAkhourii/neuroarxiv](https://github.com/UditAkhourii/neuroarxiv) | Use cases: 3 | Page: https://dedupedai.com/use-cases/uditakhourii-neuroarxiv/

NeuroArxiv is a TypeScript skill and CLI that checks real arXiv papers before Claude or Codex designs a new architecture, then returns one cited recommendation with a first step and known pitfalls.

This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL.

- Check arXiv prior art before designing a new architecture: https://dedupedai.com/use-cases/uditakhourii-neuroarxiv/check-arxiv-before-new-architecture/
- Review a planned design in Codex against arXiv prior art: https://dedupedai.com/use-cases/uditakhourii-neuroarxiv/codex-design-review-with-prior-art/
- Get a cited algorithm brief from the terminal: https://dedupedai.com/use-cases/uditakhourii-neuroarxiv/terminal-prior-art-brief-for-a-hard-problem/

## How to check arXiv prior art before designing a new architecture with Neuroarxiv

Repo: [UditAkhourii/neuroarxiv](https://github.com/UditAkhourii/neuroarxiv) | Difficulty: Intermediate | Time: 30 minutes | Basis: From the README

Have Claude Code pull real arXiv papers and return one cited design path before any code is written.

**Who it is for:** A software engineer in Claude Code about to design a new caching, queueing, or consensus component who wants to avoid rebuilding a known solution.

**The problem:** Agents often design non-trivial architecture from scratch without checking whether real prior art already solved the problem, and the mistake only shows up as a costly rebuild.

**The outcome:** A single recommended approach from real arXiv papers, with citations, a first step, a named risk, and pitfalls drawn from the limitations of every paper read.

### Steps

1. Run `npx github:UditAkhourii/neuroarxiv install --claude` to install the skill for Claude Code, or `npx github:UditAkhourii/neuroarxiv install` to let the installer detect your agents. (inferred, check the README)
2. Restart Claude Code or start a new session so the skill is loaded.
3. In Claude Code, run `/neuroarxiv "<your design problem>"` before writing any new architecture code. (inferred, check the README)
4. Read the recommended cluster, the stated reasons the runner-up paths lost, and the first step.
5. Check every cited paper link against arXiv before relying on the recommendation.

### Prompt to paste into your agent

```text
Using the NeuroArxiv skill in the UditAkhourii/neuroarxiv repo, run /neuroarxiv on the problem "<your design problem>" before designing anything. Return the single recommended approach, its arXiv citations, the first step, and the pitfalls listed for every paper. Verify that each cited arXiv id resolves before you rely on it.
```

### Caveat

NeuroArxiv only searches arXiv, so it has a narrower net than general web search, and the README reports that it lost on citation breadth in 2 of 5 benchmark problems.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/uditakhourii-neuroarxiv/check-arxiv-before-new-architecture/

## How to review a planned design in Codex against arXiv prior art with Neuroarxiv

Repo: [UditAkhourii/neuroarxiv](https://github.com/UditAkhourii/neuroarxiv) | Difficulty: Intermediate | Time: 30 minutes | Basis: From the README

Install NeuroArxiv for Codex CLI and have it check a planned system design before implementation.

**Who it is for:** A developer using Codex CLI who is about to implement a new distributed or algorithmic component and wants a prior-art check first.

**The problem:** A plan that looks reasonable can repeat a known failure mode that someone already documented in a paper, and the developer may not look for it.

**The outcome:** Codex returns a grounded recommendation with a risk statement and a list of documented pitfalls to design around before coding starts.

### Steps

1. Run `npx github:UditAkhourii/neuroarxiv install --codex` to install the skill for Codex CLI.
2. Restart Codex or start a new session.
3. Ask Codex by name to use NeuroArxiv on your problem, for example: "Use NeuroArxiv on <your design problem> before writing code."
4. Compare the pitfalls section against your plan and change the plan where a documented failure applies.
5. Keep the cited arXiv links with the design notes for the team.

### Prompt to paste into your agent

```text
Use the NeuroArxiv skill from the UditAkhourii/neuroarxiv repo on this problem: <your design problem>. Before writing code, return the recommended approach, the cited arXiv papers, the first step, and the pitfalls from each paper's limitations. Check that every paper id is a real arXiv id before you use it.
```

### Caveat

The README says every claim traces to a fetched abstract, but it still recommends checking the output, and the skill's anti-patterns section names hallucinated citations as the failure to watch for.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/uditakhourii-neuroarxiv/codex-design-review-with-prior-art/

## How to get a cited algorithm brief from the terminal with Neuroarxiv

Repo: [UditAkhourii/neuroarxiv](https://github.com/UditAkhourii/neuroarxiv) | Difficulty: Intermediate | Time: 45 minutes | Basis: From the README

Run the Neuroarxiv CLI on a hard problem and read one recommended approach with citations.

**Who it is for:** An engineer or researcher working in a terminal who needs a quick, cited starting point for a tricky algorithm or systems problem.

**The problem:** Searching arXiv by hand returns many papers with no decision, and it takes time to judge which approach fits the problem.

**The outcome:** A short brief with one committed recommendation, the reasons other paths lost, a first step, and a named risk, all tied to real papers.

### Steps

1. Clone the repository with `git clone https://github.com/UditAkhourii/neuroarxiv.git` and enter it with `cd neuroarxiv`.
2. Run `npm install` and then `npm run build` inside the checkout.
3. Run `node dist/cli.js install` if you want the skill installed into your agents.
4. Run `neuroarxiv "<your problem in one sentence>" --papers 6` to get the brief. (inferred, check the README)
5. Use `neuroarxiv --help` to see the full flag reference if you want to change the run.

### Prompt to paste into your agent

```text
In the UditAkhourii/neuroarxiv repo, run `neuroarxiv "<problem>" --papers 6` and return the recommended cluster, its cited arXiv links, the first step, and the risk. Confirm that each cited arXiv id appears in the fetched metadata before reporting it.
```

### Caveat

The README says the fetch step is deterministic and courtesy-rate-limited, but the per-paper reads use an LLM whose setup the README does not describe, so confirm the model configuration before running it.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/uditakhourii-neuroarxiv/terminal-prior-art-brief-for-a-hard-problem/