paper-discovery

name	paper-discovery
description	Find and curate research papers from academic sources. Use when user asks to find papers, search for research, discover articles, or explore a new topic.
tools	["list_available_sources","create_research_question","run_discovery_for_question","list_articles","search_articles","collection_stats"]

Paper Discovery

Find and curate research papers across academic sources (arXiv, PubMed, Semantic Scholar, OpenAlex, etc.).

Tools to Use

For discovery tasks, use ONLY these tools:

Tool	Purpose
list_available_sources	See available search sources
create_research_question	Create a new search query
run_discovery_for_question	Execute the search
list_articles	Browse results
search_articles	Filter/search within results
collection_stats	Check collection size

Quick Discovery (5 min)

For a quick search on a topic:

Step 1: Create the query
create_research_question(
  title="User's topic in 1-2 sentences",
  keywords=["keyword1", "keyword2", "keyword3"],
  sources=["semantic_scholar", "openalex"],
  max_papers=25,
  relevance_threshold=0.7
)

Step 2: Run discovery
run_discovery_for_question(question_id="[from step 1]")

Step 3: Review results
list_articles(limit=20, sort_by="relevance")

Source Selection Guide

Research Area	Recommended Sources
CS/ML/AI	arxiv, semantic_scholar
Medical/Bio	pubmed, biorxiv
General Science	openalex, crossref
Cross-disciplinary	semantic_scholar, openalex

Default: Use semantic_scholar + openalex for broad coverage.

Need a Source Not Listed?

If the user wants papers from a website/journal not in the built-in sources list:

Load the custom-source-setup skill to set up auto-detected scrapers for any website. This allows adding sources like:

• Specific journal websites (Nature, Science, PLOS ONE, etc.)
• Conference proceedings pages (ACL Anthology, NeurIPS, etc.)
• Institutional repositories (NBER, SSRN, arXiv mirrors, etc.)
• Any website with article listings

Example trigger phrases:

• "Can you get papers from NBER?"
• "Add Nature Neuroscience as a source"
• "Scrape articles from this URL: https://..."

Keyword Extraction

Extract keywords from user's request:

1. Core nouns: Main concepts (e.g., "transformers", "attention")
2. Technical terms: Field-specific language (e.g., "multi-head", "self-attention")
3. Modifiers: Scope limiters (e.g., "efficient", "sparse", "2024")

Example:

• User: "Find papers on efficient attention mechanisms in vision transformers"
• Keywords: ["vision transformer", "efficient attention", "ViT", "sparse attention"]

Relevance Threshold Guide

Threshold	Use When
0.8+	User wants only highly relevant papers
0.7	Default - good balance
0.6	Comprehensive search, broader coverage
0.5	Exploratory, casting a wide net

When to Delegate to Research Analyst

Delegate using send_message_to_agent when user needs:

• Deep analysis of discovered papers
• Quality assessment of results
• Literature synthesis across papers
• Citation network exploration

Example delegation:

send_message_to_agent(
  agent_name="Research Analyst",
  message="Analyze these 10 papers on sparse attention and summarize key approaches: [paper IDs]"
)

Workflow Examples

Example 1: Specific Topic Search

User: "Find recent papers on mixture of experts in LLMs"

1. create_research_question(
     title="Mixture of Experts in Large Language Models",
     keywords=["mixture of experts", "MoE", "sparse MoE", "LLM"],
     sources=["arxiv", "semantic_scholar"],
     max_papers=30,
     relevance_threshold=0.75
   )

2. run_discovery_for_question(question_id="...")

3. list_articles(limit=15, sort_by="date")

4. Report: "Found X papers on MoE in LLMs. Top 5: [list].
   Would you like me to analyze any of these in depth?"

Example 2: Broad Exploration

User: "I want to explore what's happening in protein folding research"

1. list_available_sources()  # Show user options

2. create_research_question(
     title="Recent advances in protein structure prediction",
     keywords=["protein folding", "AlphaFold", "protein structure prediction"],
     sources=["biorxiv", "pubmed", "semantic_scholar"],
     max_papers=50,
     relevance_threshold=0.65
   )

3. run_discovery_for_question(question_id="...")

4. collection_stats()  # Show what was found

5. Report summary of results by sub-topic

Error Handling

Error	Solution
No results	Lower threshold, broaden keywords, add sources
Too many results	Raise threshold, add specific keywords
Wrong domain papers	Add negative keywords, change sources
Timeout	Reduce sources, lower max_papers

Response Template

After discovery, report:

## Discovery Results: [Topic]

**Sources searched**: [list]
**Papers found**: [count]
**Relevance threshold**: [value]

### Top Papers:
1. [Title] - [Authors] - [Year]
   Brief: [1 sentence description]

2. ...

### Next Steps:
- Would you like me to analyze any of these papers in depth?
- Should I set this up as a recurring search?
- Want me to adjust the search parameters?