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academic-research skill

/.claude/skills/academic-research

This skill conducts deep academic research across philosophy, neuroscience, cognitive science, and theoretical CS, producing structured literature reviews and

npx playbooks add skill chrislemke/stoffy --skill academic-research

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---
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:

```markdown
# 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:

```json
{
  "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"]
}
```

Overview

This skill conducts deep academic research across philosophy, neuroscience, cognitive science, and theoretical computer science. It locates, evaluates, and synthesizes scholarly literature to produce concise, citation-ready reports and reading lists. Use it to transform broad questions into targeted literature reviews, annotated bibliographies, or research gap analyses.

How this skill works

I begin by scoping the query: domain, depth (quick/standard/deep), and focus (empirical, theoretical, historical, or debate). I search in waves across focused repositories (arXiv, Semantic Scholar, PhilPapers, PubMed/NCBI) and follow citations to high-impact work. Each source is evaluated for relevance, authority, recency, and type, then triangulated to identify consensus, debates, and gaps. Output is a structured research report with summary, key findings, theoretical landscape, open questions, and recommended reading in APA-style citations.

When to use it

  • You need a literature review or annotated bibliography on a topic in the covered domains.
  • You want a list of seminal and recent papers with short annotations and direct links/DOIs.
  • You need synthesis of empirical findings or competing theoretical positions.
  • You want to map research gaps and propose next-step questions or experiments.
  • You need to verify claims with peer-reviewed sources and distinguish preprints.

Best practices

  • Specify domain and desired depth (quick/standard/deep) so results match your needs.
  • Give a clear focus (empirical findings vs. theory vs. history) to prioritize sources appropriately.
  • Ask one clarifying question if scope or terminology is ambiguous before the search.
  • Prefer primary sources; flag preprints and note peer-review status in outputs.
  • Request follow-up actions: expanded source lists, full-text retrieval, or write-ups for different audiences.

Example use cases

  • Literature review on predictive processing in cognitive neuroscience with 15–20 prioritized papers.
  • Survey of computability results related to probabilistic Turing machines and recent complexity bounds.
  • Annotated reading list for philosophy of mind covering materialism, dualism, and functionalism.
  • Synthesis of empirical evidence on neural correlates of consciousness with consensus map and open questions.
  • Rapid briefing on state of AI alignment theory with key authors, debates, and research gaps.

FAQ

Can you include preprints and how do you mark them?

Yes. Preprints (arXiv, bioRxiv) are included when relevant and explicitly flagged as non-peer-reviewed with their arXiv ID or URL.

What citation style do you use?

Default output uses APA 7th edition with DOIs as https://doi.org/...; alternative styles available on request.