Research Literature Review

Research topic: $ARGUMENTS

Constants

• REVIEWER_BACKEND =

  codex

  — Default: Codex MCP (xhigh). Override with

  — reviewer: oracle-pro

  for GPT-5.4 Pro via Oracle MCP. See

  shared-references/reviewer-routing.md

  .
• PAPER_LIBRARY — Local directory containing user's paper collection (PDFs). Check these paths in order:

  1. papers/

     in the current project directory

  2. literature/

     in the current project directory
  3. Custom path specified by user in

     CLAUDE.md

     under

     ## Paper Library

• MAX_LOCAL_PAPERS = 20 — Maximum number of local PDFs to scan (read first 3 pages each). If more are found, prioritize by filename relevance to the topic.
• ARXIV_DOWNLOAD = false — When

  true

  , download top 3-5 most relevant arXiv PDFs to PAPER_LIBRARY after search. When

  false

  (default), only fetch metadata (title, abstract, authors) via arXiv API — no files are downloaded.
• ARXIV_MAX_DOWNLOAD = 5 — Maximum number of PDFs to download when

  ARXIV_DOWNLOAD = true

  .

💡 Overrides:

• /research-lit "topic" — paper library: ~/my_papers/

  — custom local PDF path

• /research-lit "topic" — sources: zotero, local

  — only search Zotero + local PDFs

• /research-lit "topic" — sources: zotero

  — only search Zotero

• /research-lit "topic" — sources: web

  — only search the web (skip all local)

• /research-lit "topic" — sources: web, semantic-scholar

  — also search Semantic Scholar for published venue papers (IEEE, ACM, etc.)

• /research-lit "topic" — sources: deepxiv

  — only search via DeepXiv progressive retrieval

• /research-lit "topic" — sources: all, deepxiv

  — use default sources plus DeepXiv

• /research-lit "topic" — arxiv download: true

  — download top relevant arXiv PDFs

• /research-lit "topic" — arxiv download: true, max download: 10

  — download up to 10 PDFs

Data Sources

This skill checks multiple sources in priority order. All are optional — if a source is not configured or not requested, skip it silently.

Source Selection

Parse

$ARGUMENTS

— sources:

• If

  — sources:

  is specified: Only search the listed sources (comma-separated). Valid values:

  zotero

  ,

  obsidian

  ,

  local

  ,

  web

  ,

  semantic-scholar

  ,

  deepxiv

  ,

  exa

  ,

  all

  .
• If not specified: Default to

  all

  — search every available source in priority order (

  semantic-scholar

  ,

  deepxiv

  , and

  exa

  are excluded from

  all

  ; they must be explicitly listed).

Examples:

/research-lit "diffusion models"                                    → all (default, no S2)
/research-lit "diffusion models" — sources: all                     → all (default, no S2)
/research-lit "diffusion models" — sources: zotero                  → Zotero only
/research-lit "diffusion models" — sources: zotero, web             → Zotero + web
/research-lit "diffusion models" — sources: local                   → local PDFs only
/research-lit "topic" — sources: obsidian, local, web               → skip Zotero
/research-lit "topic" — sources: web, semantic-scholar              → web + S2 API (IEEE/ACM venue papers)
/research-lit "topic" — sources: deepxiv                            → DeepXiv only
/research-lit "topic" — sources: all, deepxiv                       → default sources + DeepXiv
/research-lit "topic" — sources: all, semantic-scholar              → all + S2 API
/research-lit "topic" — sources: exa                               → Exa only (broad web + content extraction)
/research-lit "topic" — sources: all, exa                          → default sources + Exa web search

Source Table

zotero

mcp__zotero__*

obsidian

mcp__obsidian-vault__*

local

Glob: papers/**/*.pdf, literature/**/*.pdf

web

semantic-scholar

tools/semantic_scholar_fetch.py

— sources: semantic-scholar

— sources: web, semantic-scholar

deepxiv

tools/deepxiv_fetch.py

deepxiv

— sources: deepxiv

— sources: all, deepxiv

exa

tools/exa_search.py

exa-py

— sources: exa

— sources: all, exa

Graceful degradation: If no MCP servers are configured, the skill works exactly as before (local PDFs + web search). Zotero and Obsidian are pure additions.

Workflow

Step 0a: Search Zotero Library (if available)

Skip this step entirely if Zotero MCP is not configured.

Try calling a Zotero MCP tool (e.g., search). If it succeeds:

1. Search by topic: Use the Zotero search tool to find papers matching the research topic
2. Read collections: Check if the user has a relevant collection/folder for this topic
3. Extract annotations: For highly relevant papers, pull PDF highlights and notes — these represent what the user found important
4. Export BibTeX: Get citation data for relevant papers (useful for

   /paper-write

   later)
5. Compile results: For each relevant Zotero entry, extract:
   • Title, authors, year, venue
   • User's annotations/highlights (if any)
   • Tags the user assigned
   • Which collection it belongs to

📚 Zotero annotations are gold — they show what the user personally highlighted as important, which is far more valuable than generic summaries.

Step 0b: Search Obsidian Vault (if available)

Skip this step entirely if Obsidian MCP is not configured.

Try calling an Obsidian MCP tool (e.g., search). If it succeeds:

1. Search vault: Search for notes related to the research topic
2. Check tags: Look for notes tagged with relevant topics (e.g.,

   #diffusion-models

   ,

   #paper-review

   )
3. Read research notes: For relevant notes, extract the user's own summaries and insights
4. Follow links: If notes link to other relevant notes (wikilinks), follow them for additional context
5. Compile results: For each relevant note:
   • Note title and path
   • User's summary/insights
   • Links to other notes (research graph)
   • Any frontmatter metadata (paper URL, status, rating)

📝 Obsidian notes represent the user's processed understanding — more valuable than raw paper content for understanding their perspective.

Step 0c: Scan Local Paper Library

Before searching online, check if the user already has relevant papers locally:

1. Locate library: Check PAPER_LIBRARY paths for PDF files

   Glob: papers/**/*.pdf, literature/**/*.pdf
   

2. De-duplicate against Zotero: If Step 0a found papers, skip any local PDFs already covered by Zotero results (match by filename or title).

3. Filter by relevance: Match filenames and first-page content against the research topic. Skip clearly unrelated papers.

4. Summarize relevant papers: For each relevant local PDF (up to MAX_LOCAL_PAPERS):

   • Read first 3 pages (title, abstract, intro)
   • Extract: title, authors, year, core contribution, relevance to topic
   • Flag papers that are directly related vs tangentially related

5. Build local knowledge base: Compile summaries into a "papers you already have" section. This becomes the starting point — external search fills the gaps.

📚 If no local papers are found, skip to Step 1. If the user has a comprehensive local collection, the external search can be more targeted (focus on what's missing).

Step 1: Search (external)

• Use WebSearch to find recent papers on the topic
• Check arXiv, Semantic Scholar, Google Scholar
• Focus on papers from last 2 years unless studying foundational work
• De-duplicate: Skip papers already found in Zotero, Obsidian, or local library

arXiv API search (always runs, no download by default):

Locate the fetch script and search arXiv directly:

# Try to find arxiv_fetch.py
SCRIPT=$(find tools/ -name "arxiv_fetch.py" 2>/dev/null | head -1)
# If not found, check ARIS install
[ -z "$SCRIPT" ] && SCRIPT=$(find ~/.claude/skills/arxiv/ -name "arxiv_fetch.py" 2>/dev/null | head -1)

# Search arXiv API for structured results (title, abstract, authors, categories)
python3 "$SCRIPT" search "QUERY" --max 10

If

arxiv_fetch.py

The arXiv API returns structured metadata (title, abstract, full author list, categories, dates) — richer than WebSearch snippets. Merge these results with WebSearch findings and de-duplicate.

Semantic Scholar API search (only when

semantic-scholar

When the user explicitly requests

— sources: semantic-scholar

— sources: web, semantic-scholar

S2_SCRIPT=$(find tools/ -name "semantic_scholar_fetch.py" 2>/dev/null | head -1)
[ -z "$S2_SCRIPT" ] && S2_SCRIPT=$(find ~/.claude/skills/semantic-scholar/ -name "semantic_scholar_fetch.py" 2>/dev/null | head -1)

# Search for published CS/Engineering papers with quality filters
python3 "$S2_SCRIPT" search "QUERY" --max 10 \
  --fields-of-study "Computer Science,Engineering" \
  --publication-types "JournalArticle,Conference"

If

semantic_scholar_fetch.py

Why use Semantic Scholar? Many IEEE/ACM journal papers are NOT on arXiv. S2 fills the gap for published venue-only papers with citation counts and venue metadata.

De-duplication between arXiv and S2: Match by arXiv ID (S2 returns

externalIds.ArXiv

• If a paper appears in both: check S2's

  venue

  /

  publicationVenue

  — if it has been published in a journal/conference (e.g. IEEE TWC, JSAC), use S2's metadata (venue, citationCount, DOI) as the authoritative version, since the published version supersedes the preprint. Keep the arXiv PDF link for download.
• If the S2 match has no venue (still just a preprint indexed by S2): keep the arXiv version as-is.
• S2 results without

  externalIds.ArXiv

  are venue-only papers not on arXiv — these are the unique value of this source.

DeepXiv search (only when

deepxiv

When the user explicitly requests

— sources: deepxiv

deepxiv

python3 tools/deepxiv_fetch.py search "QUERY" --max 10

Then deepen only for the most relevant papers:

python3 tools/deepxiv_fetch.py paper-brief ARXIV_ID
python3 tools/deepxiv_fetch.py paper-head ARXIV_ID
python3 tools/deepxiv_fetch.py paper-section ARXIV_ID "Experiments"

If

tools/deepxiv_fetch.py

deepxiv

Why use DeepXiv? It is useful when a broad search should be followed by staged reading rather than immediate full-paper loading. This reduces unnecessary context while still surfacing structure, TLDRs, and the most relevant sections.

De-duplication against arXiv and S2:

• Match by arXiv ID first, DOI second, normalized title third
• If DeepXiv and arXiv refer to the same preprint, keep one canonical paper row and record

  deepxiv

  as an additional source
• If DeepXiv overlaps with S2 on a published paper, prefer S2 venue/citation metadata in the final table, but keep DeepXiv-derived section notes when they add value

Exa search (only when

exa

When the user explicitly requests

— sources: exa

exa

EXA_SCRIPT=$(find tools/ -name "exa_search.py" 2>/dev/null | head -1)

# Search for research papers with highlights
python3 "$EXA_SCRIPT" search "QUERY" --max 10 --category "research paper" --content highlights

# Search for broader web content (blogs, docs, news)
python3 "$EXA_SCRIPT" search "QUERY" --max 10 --content highlights

If

tools/exa_search.py

exa-py

Why use Exa? Exa provides AI-powered search across the broader web (blogs, documentation, news, company pages) with built-in content extraction. It fills a gap between academic databases (arXiv, S2) and generic WebSearch by returning richer content with each result.

De-duplication against arXiv, S2, and DeepXiv:

• Match by URL first, then normalized title
• If Exa returns an arXiv paper already found by arXiv/S2, prefer the structured metadata from those sources
• Exa results from non-academic domains (blogs, docs, news) are unique value not covered by other sources

Optional PDF download (only when

ARXIV_DOWNLOAD = true

After all sources are searched and papers are ranked by relevance:

# Download top N most relevant arXiv papers
python3 "$SCRIPT" download ARXIV_ID --dir papers/

• Only download papers ranked in the top ARXIV_MAX_DOWNLOAD by relevance
• Skip papers already in the local library
• 1-second delay between downloads (rate limiting)
• Verify each PDF > 10 KB

Step 2: Analyze Each Paper

For each relevant paper (from all sources), extract:

• Problem: What gap does it address?
• Method: Core technical contribution (1-2 sentences)
• Results: Key numbers/claims
• Relevance: How does it relate to our work?
• Source: Where we found it (Zotero/Obsidian/local/web) — helps user know what they already have vs what's new

Step 3: Synthesize

• Group papers by approach/theme
• Identify consensus vs disagreements in the field
• Find gaps that our work could fill
• If Obsidian notes exist, incorporate the user's own insights into the synthesis

Step 4: Output

Present as a structured literature table:

| Paper | Venue | Method | Key Result | Relevance to Us | Source |
|-------|-------|--------|------------|-----------------|--------|

Plus a narrative summary of the landscape (3-5 paragraphs).

If Zotero BibTeX was exported, include a

references.bib

Step 5: Save (if requested)

• Save paper PDFs to

  literature/

  or

  papers/

• Update related work notes in project memory
• If Obsidian is available, optionally create a literature review note in the vault

Step 6: Update Research Wiki (if active)

This step is optional and automatic. Skip entirely if

research-wiki/

if research-wiki/ directory exists:
    for each top relevant paper found (up to 8-12):
        1. Generate slug: python3 tools/research_wiki.py slug "<title>" --author "<last>" --year <year>
        2. Create page: research-wiki/papers/<slug>.md with structured schema
           (node_id, title, authors, year, venue, tags, one-line thesis, problem/gap,
            method, key results, limitations, reusable ingredients, open questions)
        3. Add edges to graph/edges.jsonl for relationships to existing wiki papers:
           python3 tools/research_wiki.py add_edge research-wiki/ --from "paper:<slug>" --to "<target>" --type <type> --evidence "<text>"
        4. Update gap_map.md if new gaps are identified
    Rebuild query pack:
        python3 tools/research_wiki.py rebuild_query_pack research-wiki/
    Log:
        python3 tools/research_wiki.py log research-wiki/ "research-lit ingested N papers"
else:
    skip — no wiki, no action, no error

Key Rules

• Always include paper citations (authors, year, venue)
• Distinguish between peer-reviewed and preprints
• Be honest about limitations of each paper
• Note if a paper directly competes with or supports our approach
• Never fail because a MCP server is not configured — always fall back gracefully to the next data source
• Zotero/Obsidian tools may have different names depending on how the user configured the MCP server (e.g.,

  mcp__zotero__search

  or

  mcp__zotero-mcp__search_items

  ). Try the most common patterns and adapt.