| name | academic-research |
| description | Search academic papers, build literature reviews, and synthesize research findings — combines Exa MCP (research_paper category, arxiv filtering) with arxiv-mcp-server for paper discovery, download, and deep analysis. Triggers on academic paper, literature review, research synthesis, arxiv, find papers, scholarly search. |
Academic Research
This skill provides comprehensive guidance for academic paper search, literature reviews, and research synthesis using Exa MCP and arxiv-mcp-server.
When to Use This Skill
- Searching for academic papers on a topic
- Conducting literature reviews
- Finding papers by specific authors
- Discovering recent research in a field
- Downloading and analyzing arXiv papers
- Synthesizing findings across multiple papers
- Tracking citation networks and influential papers
- Researching state-of-the-art methods in AI/ML
Available Tools
Exa MCP Server (Web Search with Academic Filtering)
Tools: mcp__exa__web_search_exa, mcp__exa__get_code_context_exa, mcp__exa__deep_search_exa
Key Parameters for Academic Search:
category: "research_paper" - Filter results to academic papers
includeDomains: ["arxiv.org"] - Restrict to arXiv
startPublishedDate / endPublishedDate - Filter by publication date
ArXiv MCP Server (Paper Search, Download, Analysis)
Tools: search_papers, download_paper, list_papers, read_paper
Capabilities:
- Search arXiv by keyword, author, or category
- Download papers locally (~/.arxiv-papers)
- Read paper content directly
- Deep paper analysis with built-in prompts
Core Workflows
Workflow 1: Quick Paper Discovery
Use case: Find papers on a specific topic quickly
Step 1: Use Exa with research_paper category
mcp__exa__web_search_exa({
query: "transformer attention mechanisms survey",
category: "research_paper",
numResults: 10
})
Step 2: Review titles and abstracts
Step 3: Note arXiv IDs for deeper analysis
Workflow 2: ArXiv-Focused Search
Use case: Search specifically within arXiv
Step 1: Use arxiv MCP search_papers
search_papers({
query: "large language models reasoning",
max_results: 20,
sort_by: "relevance"
})
Step 2: Download papers
download_paper({ arxiv_id: "2301.00234" })
Step 3: Read and analyze
read_paper({ arxiv_id: "2301.00234" })
Workflow 3: Comprehensive Literature Review
Step 1: Broad discovery with Exa (category: "research_paper")
Step 2: Identify key papers and authors
Step 3: Deep dive with arXiv MCP (download + read_paper)
Step 4: Synthesize findings by methodology/approach
Workflow 4: Recent Developments Tracking
Step 1: Time-filtered Exa search
mcp__exa__web_search_exa({
query: "multimodal large language models",
category: "research_paper",
startPublishedDate: "2024-01-01"
})
Step 2: Sort arXiv by submitted_date
search_papers({ query: "multimodal LLM", sort_by: "submitted_date" })
ArXiv Categories Reference
| Category | Description |
|---|
| cs.AI | Artificial Intelligence |
| cs.CL | Computation and Language (NLP) |
| cs.CV | Computer Vision |
| cs.LG | Machine Learning |
| cs.NE | Neural and Evolutionary Computing |
| stat.ML | Statistics - Machine Learning |
| cs.RO | Robotics |
Academic Domain Filtering
For Exa searches, restrict to academic sources:
includeDomains: [
"arxiv.org",
"aclanthology.org",
"openreview.net",
"proceedings.mlr.press",
"papers.nips.cc",
"openaccess.thecvf.com"
]
Tool Selection Guide
| Task | Primary Tool | Alternative |
|---|
| Broad topic search | Exa (research_paper) | arXiv search_papers |
| ArXiv-specific | arXiv search_papers | Exa with includeDomains |
| Download paper | arXiv download_paper | - |
| Full paper content | arXiv read_paper | - |
| Code implementations | Exa get_code_context | - |
| Very recent papers | arXiv (submitted_date) | Exa with date filter |
| Bot-protected sites | Obscura --stealth | Scrapling (Turnstile) |
| Batch stealth scrape | Obscura scrape | - |
Source Extraction Escalation
When a source isn't on ArXiv or Exa can't reach it, escalate through:
- ArXiv MCP → paper is on arXiv (free, full text, best quality)
- Exa contents → URL known, site allows crawling
- Firecrawl → JS-heavy site, no anti-bot
- Obscura
--stealth → site fingerprints headless browsers (JSTOR, Scholar, Persée, PubMed, Academia.edu)
- Scrapling → site uses Cloudflare Turnstile
Obscura Stealth Extraction (Tier 3)
For gated academic sources that block standard headless browsers via canvas/WebGL fingerprinting. Not for bypassing paywalls — for extracting publicly visible metadata, abstracts, and open-access content.
Verified sites (2026-04-24): Google Scholar, JSTOR, Persée, PubMed, Academia.edu, Perseus Digital Library.
bash ~/.claude/skills/academic-research/scripts/academic_stealth_fetch.sh URL
bash ~/.claude/skills/academic-research/scripts/academic_stealth_fetch.sh URL scholar
obscura fetch --stealth --quiet URL --eval "JS_EXPRESSION"
See references/obscura-academic-patterns.md for site-specific JS extraction patterns and gotchas.
Best Practices
- Start broad with Exa's research_paper category, then narrow
- Use date filtering for recent developments
- Download key papers via arXiv MCP for persistent access
- Cross-reference multiple search approaches
- Use technical terms in queries for better results
Domain: Subquadratic Attention
Research domain for post-transformer attention mechanisms that break the O(n^2) barrier. Active area with rapid publication cadence (2024–2026).
Key Papers
| Paper | Year | Key Contribution |
|---|
| FlashAttention-2 (Dao) | 2023 | IO-aware exact attention — foundation for all subsequent work |
| DuoAttention | 2024 | Split attention heads into retrieval (sparse) vs streaming (full) |
| Ring Attention | 2024 | Distributed sequence parallelism across devices |
| MoBA (Mixture of Block Attention) | 2025 | Block-sparse top-k gating with Triton kernel, 1M tokens |
| NSA (Native Sparse Attention, DeepSeek) | 2025 | Hardware-aligned sparse attention patterns |
| TokenSelect | 2025 | Dynamic per-layer token pruning |
Pre-Built Search Queries
# Exa (research_paper category)
"subquadratic attention mechanism" --category "research paper" --after 2024-01-01
"block sparse attention triton kernel" --category "research paper"
"mixture of attention heads sparse" --category "research paper"
"linear attention transformer approximation" --category "research paper" --after 2024-06-01
# ArXiv (cs.LG + cs.CL)
search_papers({ query: "subquadratic attention sparse transformer", max_results: 20, sort_by: "submitted_date" })
search_papers({ query: "block sparse FlashAttention kernel", max_results: 10 })
Evaluation Criteria
When comparing subquadratic attention mechanisms, benchmark on:
| Criterion | What to Measure |
|---|
| Quality | Perplexity degradation vs full attention at target sequence length |
| Speed | Wall-clock speedup on consumer GPUs (RTX 4090, M4 Max) |
| Memory | Reduction factor at 128K / 512K / 1M context |
| Compatibility | Drop-in replacement vs requires retraining |
| Sparsity | How much computation is actually skipped (e.g., 95% at 1M tokens) |
Local Implementation Reference
Working MoBA implementation with Triton kernels: ~/Desktop/Aldea/01-Repos/perplexity-clone/model/moba_block_sparse.py
Reference Documentation
For detailed parameters and advanced usage:
references/exa-academic-search.md - Exa parameters for academic search
references/arxiv-mcp-tools.md - ArXiv MCP server tool reference
references/obscura-academic-patterns.md - Site-specific Obscura extraction patterns with JS expressions and gotchas