git:20260409.ea96729 to git:20260421.a0413ff

54 added, 37 removed. Audit A to A.

---

name: academic-research

- description: This skill should be used for academic paper search, literature reviews, and research synthesis. Combines Exa MCP (research_paper category, arxiv.org filtering) with arxiv-mcp-server for paper discovery, download, and deep analysis. Use when searching for papers, conducting literature reviews, analyzing research trends, or synthesizing findings across multiple papers.

+ description: Search academic papers, build literature reviews, and synthesize research findings — combines Exa search scripts (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)

+ ### Exa CLI Scripts (Web Search with Academic Filtering)

- **Tools**: `mcp__exa__web_search_exa`, `mcp__exa__get_code_context_exa`, `mcp__exa__deep_search_exa`

+ Four scripts at `~/.claude/skills/exa-search/scripts/`:

- **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

+ | Script | Purpose |

+ |--------|---------|

+ | `exa_search.py` | Neural search with category/domain/date filters |

+ | `exa_research.py` | Synthesized answers with citations |

+ | `exa_similar.py` | Find papers similar to a given URL |

+ | `exa_contents.py` | Extract full text from paper URLs |

+ **Key flags for academic search**:

+ - `--category "research paper"` — filter to academic papers

+ - `--domain arxiv.org` — restrict to arXiv

+ - `--after YYYY-MM-DD` / `--before YYYY-MM-DD` — date range

### 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 1: Search with research paper category

+ python3 ~/.claude/skills/exa-search/scripts/exa_search.py \

+ "transformer attention mechanisms survey" \

+ --category "research paper" -n 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 1: Broad discovery with Exa

+ python3 ~/.claude/skills/exa-search/scripts/exa_search.py \

+ "topic query" --category "research paper" -n 20

Step 2: Identify key papers and authors

- Step 3: Deep dive with arXiv MCP (download + read_paper)

- Step 4: Synthesize findings by methodology/approach

+ Step 3: Get synthesized overview

+ python3 ~/.claude/skills/exa-search/scripts/exa_research.py \

+ "What are the main approaches to [topic]?" --sources --markdown

+ Step 4: Deep dive with arXiv MCP (download + read_paper)

+ Step 5: 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"

- })

+ python3 ~/.claude/skills/exa-search/scripts/exa_search.py \

+ "multimodal large language models" \

+ --category "research paper" --after 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 |

+ | Broad topic search | `exa_search.py --category "research paper"` | arXiv search_papers |

+ | Synthesized overview | `exa_research.py --sources` | — |

+ | ArXiv-specific | arXiv search_papers | `exa_search.py --domain arxiv.org` |

+ | Download paper | arXiv download_paper | — |

+ | Full paper content | arXiv read_paper | `exa_contents.py` |

+ | Similar papers | `exa_similar.py` | — |

+ | Code implementations | `exa_search.py --category github` | — |

+ | Very recent papers | arXiv (submitted_date) | `exa_search.py --after YYYY-MM-DD` |

## Best Practices

- 1. **Start broad** with Exa's research_paper category, then narrow

- 2. **Use date filtering** for recent developments

+ 1. **Start broad** with `exa_search.py --category "research paper"`, then narrow

+ 2. **Use date filtering** (`--after`, `--before`) for recent developments

3. **Download key papers** via arXiv MCP for persistent access

4. **Cross-reference** multiple search approaches

5. **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

+ ```bash

+ # Exa search (research paper category)

+ python3 ~/.claude/skills/exa-search/scripts/exa_search.py \

+ "subquadratic attention mechanism" --category "research paper" --after 2024-01-01

+ python3 ~/.claude/skills/exa-search/scripts/exa_search.py \

+ "block sparse attention triton kernel" --category "research paper"

+ python3 ~/.claude/skills/exa-search/scripts/exa_search.py \

+ "mixture of attention heads sparse" --category "research paper"

+ python3 ~/.claude/skills/exa-search/scripts/exa_search.py \

+ "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/exa-academic-search.md` — Exa CLI scripts for academic search

+ - `references/arxiv-mcp-tools.md` — ArXiv MCP server tool reference