# Deepxiv CLI

> Access academic papers (arXiv, PMC, bioRxiv, medRxiv) via CLI with semantic search and intelligent content extraction

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

---


# DeepXiv CLI Skill

## 🚀 30-Second Overview

**What**: Access open access academic papers (arXiv, PMC, bioRxiv, medRxiv) through a powerful CLI tool.

**What you can do**:
- 💬 Ask a question and get a cited answer, from arXiv or the web (`deepxiv ask`)
- 🔍 Search papers with semantic search
- 📄 Read papers section-by-section (save tokens!)
- 🧠 Use built-in AI agent for analysis
- 📊 Access biomedical literature (PMC)

**When to use**: User wants to search, read, or analyze academic papers

---

## 🎯 Command Selection Guide

**I want to...** → **Use this command**

| Goal | Command | Example |
|------|---------|---------|
| **Answer a question about the literature** | `deepxiv ask` | `deepxiv ask "what speedup does DEER report on HumanEval"` |
| **Answer a question about anything else** | `deepxiv ask --web` | `deepxiv ask "Claude API pricing" --web` |
| Find papers on a topic | `deepxiv search` | `deepxiv search "agent memory" --limit 5` |
| Quickly understand a paper | `deepxiv paper <id> --brief` | `deepxiv paper 2409.05591 --brief` |
| Read specific section | `deepxiv paper <id> --section` | `deepxiv paper 2409.05591 --section Introduction` |
| See paper structure | `deepxiv paper <id> --head` | `deepxiv paper 2409.05591 --head` |
| Preview first 10k chars | `deepxiv paper <id> --preview` | `deepxiv paper 2409.05591 --preview` |
| Get full text | `deepxiv paper <id>` | `deepxiv paper 2409.05591` |
| Access biomedical paper | `deepxiv pmc <id>` | `deepxiv pmc PMC544940` |
| Find researchers on a topic | `deepxiv talent search` | `deepxiv talent search "young RAG faculty" --semantic` |
| Read one researcher's profile | `deepxiv talent survey <id>` | `deepxiv talent survey 257` |
| Intelligent analysis | `deepxiv agent query` | `deepxiv agent query "analyze this paper"` |

---

## 📚 Core Commands Reference

### 0. Ask a Question (`deepxiv ask`)

**When to use**: The goal is an *answer*, not a specific paper's text. This runs
the whole search → judge → read loop server-side and returns prose with real
citations. Reach for it before hand-rolling `search` + `paper --section`.

```bash
# arXiv (default) — methods, numbers, experimental results
deepxiv ask "what speedup does speculative decoding report on HumanEval"
deepxiv ask "对比几种缓解 MoE 路由崩塌的方法" --effort high

# --web — current events, products, pricing, anything non-academic
deepxiv ask "Anthropic Claude API pricing tiers" --web
deepxiv ask "NeurIPS 2025 best paper winner" --web --search-type news
deepxiv ask "retrieval evaluation methodology" --web --search-type scholar
```

**Requires a registered key.** The auto-registered SDK token returns `403` —
users must register at https://data.rag.ac.cn/register. Every account gets 300
agentic calls/day free, from a quota pool separate from the general daily limit
(so a `429` here does not block `search` or `paper`). If a call 403s, tell the
user to register rather than retrying.

Takes 8–30s end to end; the answer streams, so text appears well before that.

| `--effort` | Rounds | First token (arXiv / web) | Use for |
|---|---|---|---|
| `default` | 1–2 | 3–4s / 5–9s | Facts, finding papers, one specific number |
| `high` | 3 | 7–8s / ≈13s | Comparing sources, needing body-text detail |
| `xhigh` | 4–5 | 9–13s / longer | How a direction evolved, cross-source surveys |

**Writing the query — this matters more than any flag:**
- Be specific. `"what compression ratio does KV cache eviction report on LongBench"`
  works; `"kv cache"` does not.
- Ask for numbers explicitly ("what speedup", "which benchmark") to make it read
  paper bodies rather than abstracts.
- Put scope in the query text: year, venue, category, author, min citations.
- Chinese queries work directly; the answer comes back in the query's language.
- **If the answer misses, rephrase — don't just raise `--effort`.** Effort adds
  reading rounds but cannot redirect first-round recall.

**Reading the output:**
- Answer → stdout; sources and progress → stderr. `deepxiv ask "..." > answer.md`
  captures only the answer.
- Every `[arXiv:ID]` is real — the service is instructed never to invent one, and
  says "no relevant papers" instead of fabricating. Keep those IDs in what you
  report back to the user.
- The sources list is the *retrieval set*, a superset of what's cited. The CLI
  shows only cited sources; `--all-sources` shows the rest.
- **On `--web`, weigh evidence strength.** The service reads only *cached* page
  bodies and never fetches live, so uncached pages contribute just a search
  snippet. The CLI marks pages read in full (📄) versus snippet-only (🔗), and
  the answer flags snippet-only claims. Pass that caveat on to the user instead
  of presenting everything as equally solid.
- On a truncation warning, do not treat the answer as complete — re-run with a
  larger `--max-answer-tokens` or a narrower query.
- Follow up on any cited paper with the normal commands:
  `deepxiv paper <id> --section Method`.

### 1. Search Papers (`deepxiv search`)

**When to use**: Finding relevant papers on a topic

```bash
# Basic search (quick)
deepxiv search "agent memory" --limit 5

# Advanced search with filters
deepxiv search "transformer" --format json
deepxiv search "LLM" --categories cs.AI,cs.CL --min-citations 100

# Date range
deepxiv search "vision models" --date-from 2024-01-01 --date-to 2024-12-31
```

**Expected output**: List of papers with title, arxiv_id, score, citation count
**Token cost**: ≈500-1000 tokens per search
**Tips**:
- Use `--limit 3-5` for quick overview
- Use `--format json` for downstream processing
- Add `--use-fine-rerank` when ranking quality matters more than latency

---

### 2. Read arXiv Papers (`deepxiv paper`)

**When to use**: Understanding specific papers (choose format by depth needed)

#### Option A: Quick Summary (30 seconds)
```bash
deepxiv paper 2409.05591 --brief
# ✓ Best for: Quick understanding
# → Title, TLDR, keywords, citation count
# Token cost: ~500 tokens
# Use when: "What's this paper about?"
```

#### Option B: Paper Structure (2 minutes)
```bash
deepxiv paper 2409.05591 --head
# ✓ Best for: Understanding paper organization
# → Metadata, all sections with summaries
# Token cost: ~1-2k tokens
# Use when: "What does this paper contain?"
```

#### Option C: Quick Scan (3 minutes)
```bash
deepxiv paper 2409.05591 --preview
# ✓ Best for: Fast preview without full read
# → First ~10k characters
# Token cost: ~2k tokens
# Use when: "Let me scan the introduction"
```

#### Option D: Specific Section (1 minute)
```bash
deepxiv paper 2409.05591 --section Introduction
# ✓ Best for: Reading one section in detail
# → Full section content
# Token cost: ~1-5k tokens (varies by section)
# Use when: "I only need the introduction"
```

#### Option E: Complete Paper (for deep analysis)
```bash
deepxiv paper 2409.05591
# or explicitly: deepxiv paper 2409.05591 --raw
# ✓ Best for: Full text analysis
# → Complete paper in markdown
# Token cost: ~10-50k tokens
# Use when: "I need to thoroughly analyze this"
```

---

### 3. Access Biomedical Literature (`deepxiv pmc`)

**When to use**: Need medical, biology, or biomedical research

```bash
# Quick metadata
deepxiv pmc PMC544940 --head

# Full paper
deepxiv pmc PMC544940
```

**Expected output**: Paper metadata or complete paper JSON
**Token cost**: ≈1-2k tokens (metadata), ≈10-30k tokens (full)

---

### 4. Manage Token (`deepxiv token`)

**When to use**: Check your API token status

```bash
deepxiv token
# Shows current token and daily limit info
```

**Note**: Token auto-registers on first use, saves to `~/.env`

---

### 5. Find Researchers (`deepxiv talent`) — beta

**Availability**: ships in `1.1.0b1`, source install only (`pip install git+https://github.com/DeepXiv/deepxiv_sdk.git`). The talent index is still being built out, so coverage is uneven — a search that returns thin or irrelevant people is a data gap, not a bad query.

**When to use**: You need people, not papers — who works on a topic, and what is their record

```bash
# Semantic search: describe the person you want in a sentence
deepxiv talent search "young faculty working on retrieval-augmented generation" --semantic --limit 5

# Keyword mode: matches names and affiliations
deepxiv talent search "Zhicheng Dou"

# Filter and sort
deepxiv talent search --tags LLM,Agent --career-stage student --sort total_citations --limit 10

# Full profile by ID (IDs come from search)
deepxiv talent survey 257                    # text summary
deepxiv talent survey 257 --format markdown  # the generated report
deepxiv talent survey 257 --no-refresh       # read-only, no Scholar refresh
deepxiv talent survey 257 --json             # raw JSON
```

**Output**: search gives id, names, affiliation, h-index, citations, tags.
survey adds bio, education, work history, links, open-source projects, and
publication metrics.

**Quota**: 1 agentic call each — the same pool as `deepxiv ask` (free 300/day).
Requires a registered key. Profiles older than ~14 days refresh from Google
Scholar automatically on `survey`; use `--no-refresh` to avoid that.

---

### 6. Intelligent Agent (`deepxiv agent`)

**When to use**: Need analysis beyond simple retrieval

```bash
# One-time query
deepxiv agent query "What are key innovations in this paper?"

# Multi-turn reasoning
deepxiv agent query "Compare these two approaches" --max-turn 10 --verbose

# First time setup
deepxiv agent config
# Configure your LLM (OpenAI, DeepSeek, OpenRouter, etc.)
```

**Token cost**: Varies by reasoning depth
**Requirements**: Requires langgraph and langchain-core

---

## 🎬 Common Workflows

### Workflow 1: Quick Paper Review (2 minutes)
```bash
# Step 1: Get brief info
deepxiv paper 2409.05591 --brief

# Step 2: Read introduction
deepxiv paper 2409.05591 --section Introduction

# Step 3: Check results (optional)
deepxiv paper 2409.05591 --section Results
```
**Total tokens**: ≈2-3k | **Use case**: Quick assessment before deciding to read fully

### Workflow 2: Deep Paper Analysis (15 minutes)
```bash
# Step 1: Understand structure
deepxiv paper 2409.05591 --head

# Step 2: Read key sections
deepxiv paper 2409.05591 --section Introduction
deepxiv paper 2409.05591 --section Methods
deepxiv paper 2409.05591 --section Results

# Step 3: AI analysis
deepxiv agent query "Summarize the main contributions"
```
**Total tokens**: ≈10-15k | **Use case**: Thorough understanding

### Workflow 3: Literature Search (5 minutes)
```bash
# Step 1: Find relevant papers
deepxiv search "agent memory" --limit 5

# Step 2: Quick assess each
for id in results; do
    deepxiv paper $id --brief
done

# Step 3: Deep read top 2
deepxiv paper <top_id_1>
deepxiv paper <top_id_2>
```
**Total tokens**: ≈5-10k | **Use case**: Literature review

---

## 💪 Capabilities & Limitations

### What deepxiv Can Do ✅

| Capability | Details |
|-----------|---------|
| **Hybrid Search** | BM25 + Vector search across 2M+ arXiv papers |
| **Smart Summaries** | AI-generated TLDRs and keywords |
| **Section Access** | Read specific sections without loading full text |
| **Biomedical Access** | PubMed Central (PMC) papers |
| **Open Access Only** | arXiv, PMC, bioRxiv, medRxiv |
| **Intelligent Analysis** | ReAct agent with multi-turn reasoning |
| **Multiple LLMs** | Compatible with OpenAI, DeepSeek, OpenRouter, etc. |

### What deepxiv Cannot Do ❌

| Limitation | Note |
|-----------|------|
| **Subscription Journals** | Only open access papers (by design) |
| **Real-time Updates** | arXiv has ≈1-2 day delay |
| **Paywalled Content** | Cannot access IEEE, ACM, etc. journals |
| **Daily Limits** | 10,000 requests/day free (email for more) |
| **Proprietary Data** | Only academic papers, not internal docs |

---

## 🔧 Troubleshooting

### Problem: Paper Not Found
```bash
deepxiv paper invalid_id
# Error: NotFoundError: Paper not found
```
**Solution**:
- Check arXiv ID format (should be like `2409.05591`)
- Verify at https://arxiv.org

### Problem: Daily Limit Exceeded
```bash
deepxiv search "topic"
# Error: RateLimitError: Daily limit reached
```
**Solution**:
- Wait until tomorrow (limit resets daily)
- OR email `tommy@chien.io` with name, email, phone for higher limit

### Problem: Token Invalid
```bash
deepxiv paper 2409.05591
# Error: AuthenticationError: Invalid or expired token
```
**Solution**:
- Run `deepxiv config --token YOUR_NEW_TOKEN`
- Or delete `~/.env` to auto-register new token

### Problem: Agent Dependencies Missing
```bash
deepxiv agent query "analyze"
# Error: langgraph not installed
```
**Solution**:
```bash
pip install deepxiv-sdk[agent]
```

### Problem: Timeout on Large Papers
```bash
deepxiv paper <large_paper_id>
# Error: Request timed out
```
**Solution**:
- Use `--brief` or `--head` instead of full paper
- Or use `--section` to read specific parts
- Increase timeout: Use Python API with `Reader(timeout=180)`

---

## 📊 Output Format Guide

### For Search Results
**Format**: Show title, arxiv_id, brief context
```
1. Title of Paper (2409.05591)
   - Field: Computer Science / AI
   - Citations: 150
   - Key idea: Brief 1-line summary of main contribution
```

### For Paper Reading
**Always mention what command was used**:
```
From `deepxiv paper 2409.05591 --brief`:
- Title: MemGPT: Towards LLMs as Operating Systems
- TLDR: Introduces hierarchical memory management for extended context
- Key concepts: agent memory, context management, LLM systems
- (~500 tokens)
```

### For Structured Data
**Use JSON format when**:
- Processing results programmatically
- Building pipelines
- Storing for later analysis
```bash
deepxiv search "topic" --format json  # Pipe to other tools
```

---

## ⏱️ Token Budget Guide

When planning queries, consider token costs:

```
Quick Summary:        500 tokens
Metadata (--head):    1-2k tokens
One Section:          1-5k tokens (depends on section)
Full Paper:           10-50k tokens
Search Query:         0.5-1k tokens
Agent Analysis:       5-20k tokens (reasoning depth)
```

**Recommendation**: For general use, stick to `--brief` and `--section` to keep tokens under 5k per query.

---

## 🚀 Advanced Usage

### Batch Processing (Multiple Papers)
```bash
for id in 2409.05591 2504.21776 2503.04975; do
    echo "=== $id ==="
    deepxiv paper $id --brief
done
```

### Piping to Other Tools
```bash
deepxiv search "topic" --format json | jq '.results[] | .arxiv_id'
```

### Custom Token Configuration
```python
from deepxiv_sdk import Reader
reader = Reader(token="YOUR_TOKEN", timeout=120, max_retries=5)
```

---

## 🌐 Supported & Coming Soon

### Current Support ✅
- **arXiv** - Computer Science, Physics, Math, Economics
- **PMC** - PubMed Central biomedical literature
- **bioRxiv** - Biology preprints (`deepxiv search --biorxiv`, `deepxiv biorxiv DOI`)
- **medRxiv** - Medicine preprints (`deepxiv search --medrxiv`, `deepxiv medrxiv DOI`)

### Coming Soon 🔄
- **Other OA** - Additional open access sources
- **Full OA Ecosystem** - Unified cross-source search

---

## 📞 Getting Help

**Command not working?**
```bash
deepxiv <command> --help
```

**Need a higher daily limit?**
Email: `tommy@chien.io` with name, email, phone

**Found a bug?**
[GitHub Issues](https://github.com/qhjqhj00/deepxiv_sdk/issues)

**Learn more?**
[Full Documentation](https://github.com/qhjqhj00/deepxiv_sdk#readme)

---

## ✨ Tips & Best Practices

1. **Start with `--brief`** - Always start with brief overview
2. **Use sections** - Don't load full papers when sections suffice
3. **Save tokens** - Stack operations to reduce API calls
4. **Check limits** - Run `deepxiv token` before large batches
5. **Use agent wisely** - For complex analysis that needs reasoning
6. **Format choice** - Use `--format json` for piping, plain text for humans
7. **Error handling** - Always handle "Paper not found" and "Limit exceeded" cases

---

**Last Updated**: 2024 | **Version**: 0.2.0 | **Status**: Production Ready