Research Lit

Web-side execution adapter

• This skill is workflow guidance for the ChatGPT web-side connector.
• Loading this SKILL.md is only the setup step; it does not mean the task is complete.
• After loading, continue to execute the workflow, constraints, and output format below before answering.
• Mentions of local automation, local file operations, local command execution, or external integrations are descriptive only. Use capabilities available in the current ChatGPT session, or ask the user for needed files/links.
• For literature search, current facts, factual verification, source tracing, numeric values, material properties, legal/medical/financial/current information, or any evidence-heavy claim: use available search/browsing tools first and cite verifiable sources. Do not answer such tasks only from memory.
• Preserve the original workflow and scope unless the user explicitly asks for changes.

Research Literature Review

Research topic: $ARGUMENTS

Constants

• REVIEWER_BACKEND = codex — Default: local coding-assistant integration (xhigh). Override with — reviewer: oracle-pro for a strong reviewer model 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 skill "topic" — paper library: ~/my_papers/` — custom local PDF path
• research-lit skill "topic" — sources: zotero, local` — only search Zotero + local PDFs
• research-lit skill "topic" — sources: zotero` — only search Zotero
• research-lit skill "topic" — sources: web` — only search the web (skip all local)
• research-lit skill "topic" — sources: web, semantic-scholar` — also search Semantic Scholar for published venue papers (IEEE, ACM, etc.)
• research-lit skill "topic" — sources: deepxiv` — only search via DeepXiv progressive retrieval
• research-lit skill "topic" — sources: all, deepxiv` — use default sources plus DeepXiv
• research-lit skill "topic" — arXiv full-text reading when accessible` — download top relevant arXiv PDFs
• research-lit skill "topic" — arXiv full-text reading when accessible, 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.

Source Selection

Parse $ARGUMENTS for a — sources: directive:

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

Source Table

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 skill 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 web search capability 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 is not found, fall back to web search capability for arXiv (same as before).

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

Semantic Scholar API search (only when semantic-scholar is in sources):

When the user explicitly requests — sources: semantic-scholar (or — sources: web, semantic-scholar), search for published venue papers beyond arXiv:

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 is not found, skip.

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 is in sources):

When the user explicitly requests — sources: deepxiv (or includes deepxiv in a combined source list), use the DeepXiv adapter for progressive retrieval:

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 or the deepxiv CLI is unavailable, skip this source gracefully and continue with the remaining requested sources.

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 is in sources):

When the user explicitly requests — sources: exa (or includes exa in a combined source list), use the Exa tool for broad AI-powered web search with content extraction:

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 or the exa-py SDK is unavailable, skip this source gracefully and continue with the remaining requested sources.

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 web search capability 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 snippet for direct use in paper writing.

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/ does not exist in the project.

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., external integration or external integration). Try the most common patterns and adapt.