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End-to-end academic research pipeline: discovery via Semantic Scholar, arXiv, and OpenAlex; supplementary resource discovery via HuggingFace, Kaggle, and CatalyzeX; storage and enrichment via Zotero; literature notes via Obsidian; synthesis via NotebookLM. Use when starting a literature review, finding papers on a topic, discovering code/datasets for a paper, creating literature notes, or understanding the research tool stack. Trigger with phrases like "find papers on", "literature review", "what research tools do we have", "discover datasets for", "find code implementations", "research pipeline", "supplementary resources".
This skill uses the workspace's default tool permissions.
Research Pipeline
A 4-stage pipeline for academic research: discover, enrich, organize, synthesize. Each stage has specific tools and protocols.
Pipeline Architecture
Stage 1: Discovery (S2 / arXiv / OpenAlex)
↓
Stage 1.5: Supplementary Resources (HuggingFace / Kaggle / CatalyzeX)
↓
Stage 2: Storage & Enrichment (Zotero + plugins)
↓
Stage 3: Literature Notes (Obsidian vault)
↓
Stage 4: Synthesis (NotebookLM — manual)
Stage 1: Paper Discovery
Tool Selection
| Tool | Strength | Use For |
|---|---|---|
| Semantic Scholar | 200M+ papers, citation graphs, recommendations | Focused paper search, citation analysis, author profiles |
| arXiv | Preprint search, PDF extraction, category filtering | CS/ML/physics papers, latest preprints |
| OpenAlex | 240M+ works, institutions, venues, OA links | Broad scholarly search, institutional analysis, trend analysis |
Discovery Protocol
- Start with OpenAlex for broad topic scoping (trend analysis, top-cited works)
- Narrow with Semantic Scholar for focused searches (specific authors, citation chains)
- Check arXiv for latest preprints and PDF access
- Cross-reference using DOI or arXiv ID across all three sources
Search Strategy
- Use quoted phrases for exact matches:
"multi-agent systems" - Combine with field-specific search:
ti:"transformer" AND abs:"attention" - Filter by date, citation count, and category
- For foundational work: use
date_toparameter to find classic papers
Stage 1.5: Supplementary Resource Discovery
After finding papers, discover linked code, datasets, and models:
Automated (Claude via MCP)
| Source | MCP Tool | What It Finds |
|---|---|---|
| HuggingFace | get_paper_info(arxiv_id) | Linked models, datasets, spaces |
| HuggingFace | search_datasets/models(query) | Related resources by keyword |
| HF Daily Papers | get_today_papers() | Curated daily feed |
| Kaggle | search_datasets(query) | Competition datasets, notebooks |
Semi-Automated (Claude in Chrome)
- CatalyzeX + Scholar: Navigate to
catalyzex.com/paper/{slug}/code, extractwindow.__NEXT_DATA__for GitHub repos - HuggingFace collections: WebFetch on HF API for collection items (MCP has slug bug)
Manual (Cian via browser)
- CatalyzeX arXiv Labs overlay, DagsHub experiment repos, Google Scholar BibTeX export, HuggingFace web UI bookmarks
Stage 2: Zotero Storage & Enrichment
Detailed workflow: See zotero-workflow skill.
Summary:
- Browser Connector saves papers with metadata
- Cita resolves DOIs (OpenAlex first, then S2)
- Linter fills metadata (Blank Fields Only mode)
- Zoplicate deduplicates
zotero-metadata-sync.pypushes to Supabase
Stage 3: Obsidian Literature Notes
Literature Note Frontmatter Schema
---
type: literature-note
zotero-key: "ABC123"
doi: "10.1234/example"
authors: ["Smith, J.", "Doe, A."]
year: 2025
methods: ["qualitative", "thematic-analysis"]
relevance: ["limerence", "attachment-theory"]
status: unprocessed | processed | synthesized
tags: [literature, psychology]
---
Creation Flow
Tier 1 (Filesystem — works now):
zotero_get_item_metadata(item_key)for metadata- Map Zotero fields to frontmatter schema
- Write tool creates
~/Vaults/ObsidianVault/03-Knowledge/Literature/{title}.md - Include frontmatter + abstract + empty sections for user annotation
Tier 2 (Obsidian CLI — when available):
obsidian create,obsidian search,obsidian backlinks,obsidian property:set
Path Convention
| Content | Path |
|---|---|
| Literature notes | 03-Knowledge/Literature/ |
| Readwise highlights | 03-Knowledge/Readwise/ |
| Concept notes | 03-Knowledge/Concepts/ |
Stage 4: NotebookLM Synthesis
- No API. Manual upload only.
- Sources: PDFs from Supabase + processed Obsidian notes
- ~50 document limit per notebook
- Best for: multi-source synthesis, audio overviews
Integration Points
| Tool | MCP | Role |
|---|---|---|
| Semantic Scholar | semanticscholar | Paper search, citations, recommendations |
| arXiv | arxiv | Paper search, PDF extraction |
| OpenAlex | openalex | Broad scholarly search, trends, institutions |
| HuggingFace | huggingface | Models, datasets, spaces, paper info |
| HF Daily Papers | huggingface-daily-papers | Curated daily feed |
| Kaggle | kaggle | Datasets, competitions, notebooks |
| Zotero | zotero + seerai-zotero | Metadata, citation data, library management |
| Obsidian | Filesystem / CLI | Knowledge management, literature notes |
| Firecrawl | firecrawl | Grey literature, web sources |
| NotebookLM | Manual | Multi-source synthesis |
Rules
- No new scripts. Zotero has 3 scripts (ceiling). Obsidian uses CLI or Write tool.
- No Obsidian MCP. Community servers are fragile. CLI replaces them.
- Frontmatter is contract. The YAML schema is the interface between Zotero and Obsidian.
- Pilot batch first. 3-item minimum before any 10+ item operation.
- MCP-first principle. Use MCPs over scripts wherever possible.
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