academic-research
Conduct deep academic research for philosophy, neuroscience, cognitive science, and theoretical computer science (computability, complexity, AI theory, logic). Use when user asks to: research academic topics, find scholarly papers, conduct literature reviews, analyze citations, synthesize research findings, explore philosophical arguments, investigate consciousness/cognition, study computability/decidability/Turing machines, or analyze academic debates. Triggers on: 'research papers', 'literature review', 'academic sources', 'scholarly articles', 'philosophy of mind', 'computability theory', 'neuroscience studies', 'find papers on', 'what does the research say'.
When & Why to Use This Skill
This Claude skill automates deep academic research and literature synthesis across specialized fields including philosophy, neuroscience, cognitive science, and theoretical computer science. It streamlines the discovery of scholarly papers from authoritative databases like arXiv, Semantic Scholar, and PhilPapers, providing structured research reports with verified citations, authority assessments, and cross-referenced findings to accelerate the academic writing and peer-review process.
Use Cases
- Literature Review: Automating the search, evaluation, and synthesis of dozens of scholarly articles to identify research gaps and consensus on specific topics.
- Citation Analysis: Identifying seminal works and tracking the evolution of academic debates by following citation networks and 'cited by' metrics.
- Theoretical Exploration: Investigating complex interdisciplinary concepts such as consciousness, computability, or AI theory by aggregating insights from multiple domains.
- Source Verification: Assessing the credibility of scientific claims by verifying peer-review status, author authority, and publication recency.
- Structured Reporting: Generating comprehensive research summaries and bibliographies in APA format for academic papers, theses, or grant proposals.
| name | academic-research |
|---|---|
| description | "Conduct deep academic research for philosophy, neuroscience, cognitive science, and theoretical computer science (computability, complexity, AI theory, logic). Use when user asks to: research academic topics, find scholarly papers, conduct literature reviews, analyze citations, synthesize research findings, explore philosophical arguments, investigate consciousness/cognition, study computability/decidability/Turing machines, or analyze academic debates. Triggers on: 'research papers', 'literature review', 'academic sources', 'scholarly articles', 'philosophy of mind', 'computability theory', 'neuroscience studies', 'find papers on', 'what does the research say'." |
Academic Research Skill
Conduct comprehensive academic research mimicking Claude.ai's Research feature, specialized for philosophy, neuroscience, cognitive science, and theoretical CS.
Research Workflow
1. Scope the Query
Before searching, clarify:
- Domain: Philosophy / Neuroscience / Cognitive Science / Theoretical CS
- Depth: Quick (3-5 sources) | Standard (10-15) | Deep (20+)
- Focus: Empirical findings / Theoretical frameworks / Historical development / Current debates
If unclear, ask one clarifying question before proceeding.
2. Search Strategy
Use web search with academic-focused queries. Search in waves:
Wave 1 - Core sources:
"[topic]" site:semanticscholar.org"[topic]" site:arxiv.org"[topic]" site:philpapers.org(for philosophy)"[topic]" site:ncbi.nlm.nih.gov(for neuroscience)
Wave 2 - Expand with:
"[topic]" review paper OR survey"[topic]" [key author name]"[topic]" [specific journal from references/domains.md]
Wave 3 - Follow citations:
- Search for highly-cited papers found in Wave 1-2
- Look for "cited by" to find recent work building on seminal papers
3. Source Evaluation
For each source, extract and assess:
- Relevance (0-10): How directly does it address the query?
- Authority: Peer-reviewed? Citation count? Author credentials?
- Recency: Prioritize last 5 years unless historical context needed
- Type: Empirical study / Review / Theoretical / Commentary
Flag preprints (arXiv, bioRxiv) as non-peer-reviewed.
4. Triangulation
Cross-reference findings to identify:
- Consensus: Claims supported by multiple independent sources
- Debates: Conflicting findings or interpretations
- Gaps: Underexplored questions
- Key figures: Most-cited authors and seminal works
5. Synthesis Output
Structure the report as:
# Research Report: [Topic]
## Summary
[2-3 paragraph executive summary]
## Key Findings
1. [Finding with citation]
2. [Finding with citation]
...
## Theoretical Landscape
[Major positions, schools of thought, competing frameworks]
## Open Questions
[Active debates, unresolved issues, research gaps]
## Recommended Reading
- [Paper 1] - [1-sentence annotation]
- [Paper 2] - [1-sentence annotation]
...
## References
[Full citations, preferably with DOIs/URLs]
Domain-Specific Guidance
See references/domains.md for:
- Key journals and venues per domain
- Important authors and research groups
- Domain-specific terminology
- Relevant arXiv categories
Citation Format
Default: APA 7th edition. Include:
- DOI when available (as URL: https://doi.org/...)
- arXiv ID for preprints: arXiv:XXXX.XXXXX
- Direct URL to paper when no DOI
Quality Standards
- Never cite a paper without verifying it exists via search
- Distinguish peer-reviewed from preprints
- Note when findings are contested or preliminary
- Include publication year for temporal context
- Prefer primary sources over secondary summaries
Subagent Mode
When invoked programmatically, return structured data:
{
"query": "original research question",
"domain": "identified domain",
"sources_found": 15,
"key_findings": ["finding 1", "finding 2"],
"consensus_level": "high|moderate|low|contested",
"top_papers": [
{"title": "...", "authors": "...", "year": 2023, "url": "..."}
],
"research_gaps": ["gap 1", "gap 2"]
}