wanshuiyin/research-lit

Research Literature Review

Research topic: $ARGUMENTS

Constants

• 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 AGENTS.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.
• SOURCES = all — Which literature sources to search. Options: zotero, obsidian, local, web, semantic-scholar, deepxiv, exa, gemini, openalex, all. Full source table and selection rules: see ## Data Sources below.
• 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.
• REVIEWER_BACKEND = codex — Default reviewer route for optional literature synthesis cross-checks. Use --reviewer: oracle-pro only when explicitly requested; if Oracle is unavailable, warn and continue with Codex xhigh or local synthesis.

💡 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: web — only search the web (skip all local)
• /research-lit "topic" — sources: web, semantic-scholar — also search Semantic Scholar for published venue papers
• /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.

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, gemini, openalex, all.
• If not specified: Default to all — search every available source in priority order (semantic-scholar, deepxiv, exa, gemini, and openalex are excluded from all; they must be explicitly listed).

Examples:

/research-lit "diffusion models"                        → all (default)
/research-lit "diffusion models" — sources: all         → all
/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 + Semantic Scholar API
/research-lit "topic" — sources: deepxiv                → DeepXiv only
/research-lit "topic" — sources: all, deepxiv           → default sources + DeepXiv
/research-lit "topic" — sources: all, semantic-scholar  → default sources + Semantic Scholar API
/research-lit "topic" — sources: exa                    → Exa only (broad web + content extraction)
/research-lit "topic" — sources: all, exa               → default sources + Exa web search
/research-lit "topic" — sources: gemini                 → Gemini only (AI-powered broad discovery)
/research-lit "topic" — sources: all, gemini            → default sources + Gemini discovery
/research-lit "topic" — sources: openalex               → OpenAlex only (open citation graph + institutions)
/research-lit "topic" — sources: all, openalex          → default sources + OpenAlex citation graph

Source Table

| Priority | Source | ID | How to detect | What it provides | |----------|--------|----|---------------|-----------------| | 1 | Zotero (via MCP) | zotero | Try calling any mcp__zotero__* tool — if unavailable, skip | Collections, tags, annotations, PDF highlights, BibTeX, semantic search | | 2 | Obsidian (via MCP) | obsidian | Try calling any mcp__obsidian-vault__* tool — if unavailable, skip | Research notes, paper summaries, tagged references, wikilinks | | 3 | Local PDFs | local | Glob: papers/**/*.pdf, literature/**/*.pdf | Raw PDF content (first 3 pages) | | 4 | Web search | web | Always available (WebSearch) | arXiv, Semantic Scholar, Google Scholar | | 5 | Semantic Scholar API | semantic-scholar | $S2_FETCHER resolves (canonical name semantic_scholar_fetch.py, per integration-contract §2 Codex chain) | Published venue papers (IEEE, ACM, Springer) with structured metadata: citation counts, venue info, TLDR. Only runs when explicitly requested | | 6 | DeepXiv CLI | deepxiv | $DEEPXIV_FETCHER resolves (canonical name deepxiv_fetch.py, per integration-contract §2) and deepxiv CLI present | Progressive paper retrieval: search, brief, head, section, trending, web search. Only runs when explicitly requested | | 7 | Exa Search | exa | $EXA_FETCHER resolves (canonical name exa_search.py, per integration-contract §2); fetcher handles exa-py SDK + API key internally | AI-powered broad web search with content extraction (highlights, text, summaries). Covers blogs, docs, news, companies, and research papers beyond arXiv/S2. Only runs when explicitly requested | | 8 | Gemini (MCP / CLI) | gemini | mcp__gemini-cli__ask-gemini is available, or gemini CLI is installed | AI-powered broad literature discovery that decomposes topics into sub-problems, aliases, and variants. Only runs when explicitly requested | | 9 | OpenAlex | openalex | $OPENALEX_FETCHER resolves (canonical name openalex_fetch.py, per integration-contract §2 Codex chain) and Python requests is importable | Open citation graph with institutional affiliations, funding data, and broad work metadata. Only runs when explicitly requested |

If the user explicitly requests Zotero or Obsidian and that source is not configured, stop and tell the user how to enable it. Only sources that were not requested may be skipped silently.

Workflow

Per-source/per-paper fan-out

Retrieval and extraction are breadth-bound. Use fresh spawn_agent shards when delegation is available, or the same work sequentially otherwise. Every shard is read-only and returns {"shard_id": ..., "entries": [{"payload": ..., "dedup_key": "<DOI/arXiv/id>"}]}. The parent invokes every resolved source, unions results, mechanically dedups on canonical IDs, and writes once. Shards do not rank paper quality. Source verification remains deterministic; optional Codex synthesis review is same-family provisional. See fan-out-pattern.md.

Step 0a: Search Zotero Library (if available)

If the user explicitly requested Zotero and the Zotero MCP is not configured, stop and ask the user to configure it. Otherwise skip this step entirely.

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)

If the user explicitly requested Obsidian and the Obsidian MCP is not configured, stop and ask the user to configure it. Otherwise skip this step entirely.

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):

Resolve $ARXIV_FETCHER via the canonical strict-safe Codex chain (see shared-references/integration-contract.md §2):

if [ -z "${ARIS_REPO:-}" ] && [ -f .aris/installed-skills-codex.txt ]; then
    ARIS_REPO=$(awk -F'\t' '$1=="repo_root"{print $2; exit}' .aris/installed-skills-codex.txt 2>/dev/null) || true
fi
ARXIV_FETCHER=""
[ -n "${ARIS_REPO:-}" ] && [ -f "$ARIS_REPO/tools/arxiv_fetch.py" ] && ARXIV_FETCHER="$ARIS_REPO/tools/arxiv_fetch.py"
[ -z "$ARXIV_FETCHER" ] && [ -f tools/arxiv_fetch.py ] && ARXIV_FETCHER="tools/arxiv_fetch.py"
[ -z "$ARXIV_FETCHER" ] && [ -f ~/.codex/skills/arxiv/arxiv_fetch.py ] && ARXIV_FETCHER="$HOME/.codex/skills/arxiv/arxiv_fetch.py"

if [ -n "$ARXIV_FETCHER" ]; then
  # Search arXiv API for structured results (title, abstract, authors, categories).
  if python3 "$ARXIV_FETCHER" search "QUERY" --max 10; then
    echo "D2 contribution: arxiv (helper invocation exit 0)" >&2
  else
    echo "WARN: arxiv_fetch.py invocation failed; D2 aggregate continues with WebSearch results." >&2
  fi
else
  echo "WARN: arxiv_fetch.py not resolved; falling back to WebSearch for arXiv hits." >&2
fi

If $ARXIV_FETCHER is empty (D2 graceful degradation), fall back to WebSearch for arXiv (same as before).

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 is in sources):

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

# Re-resolve $ARIS_REPO + $S2_FETCHER (SKILL bash blocks may run in separate shells).
if [ -z "${ARIS_REPO:-}" ] && [ -f .aris/installed-skills-codex.txt ]; then
    ARIS_REPO=$(awk -F'\t' '$1=="repo_root"{print $2; exit}' .aris/installed-skills-codex.txt 2>/dev/null) || true
fi
S2_FETCHER=""
[ -n "${ARIS_REPO:-}" ] && [ -f "$ARIS_REPO/tools/semantic_scholar_fetch.py" ] && S2_FETCHER="$ARIS_REPO/tools/semantic_scholar_fetch.py"
[ -z "$S2_FETCHER" ] && [ -f tools/semantic_scholar_fetch.py ] && S2_FETCHER="tools/semantic_scholar_fetch.py"
[ -z "$S2_FETCHER" ] && [ -f ~/.codex/skills/semantic-scholar/semantic_scholar_fetch.py ] && S2_FETCHER="$HOME/.codex/skills/semantic-scholar/semantic_scholar_fetch.py"

if [ -n "$S2_FETCHER" ]; then
    if python3 "$S2_FETCHER" search "QUERY" --max 10 --fields title,authors,year,venue,citationCount,externalIds,tldr,url; then
      echo "D2 contribution: semantic_scholar (helper invocation exit 0)" >&2
    else
      echo "WARN: semantic_scholar_fetch.py invocation failed; D2 aggregate continues." >&2
    fi
else
    echo "Semantic Scholar unavailable: $S2_FETCHER unresolved; skipping this optional source." >&2
fi

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 first (externalIds.ArXiv), then normalized title.
• If a paper appears in both and S2 has venue / DOI / citation metadata, use S2 as authoritative metadata while keeping the arXiv PDF link.
• S2 results without externalIds.ArXiv are venue-only papers and should be preserved as unique value.

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:

# Re-resolve $ARIS_REPO + $DEEPXIV_FETCHER (SKILL bash blocks may run in separate shells).
if [ -z "${ARIS_REPO:-}" ] && [ -f .aris/installed-skills-codex.txt ]; then
    ARIS_REPO=$(awk -F'\t' '$1=="repo_root"{print $2; exit}' .aris/installed-skills-codex.txt 2>/dev/null) || true
fi
DEEPXIV_FETCHER=""
[ -n "${ARIS_REPO:-}" ] && [ -f "$ARIS_REPO/tools/deepxiv_fetch.py" ] && DEEPXIV_FETCHER="$ARIS_REPO/tools/deepxiv_fetch.py"
[ -z "$DEEPXIV_FETCHER" ] && [ -f tools/deepxiv_fetch.py ] && DEEPXIV_FETCHER="tools/deepxiv_fetch.py"
[ -z "$DEEPXIV_FETCHER" ] && [ -f ~/.codex/skills/deepxiv/deepxiv_fetch.py ] && DEEPXIV_FETCHER="$HOME/.codex/skills/deepxiv/deepxiv_fetch.py"

if [ -n "$DEEPXIV_FETCHER" ]; then
    if python3 "$DEEPXIV_FETCHER" search "QUERY" --max 10; then
      echo "D2 contribution: deepxiv (helper invocation exit 0)" >&2
      python3 "$DEEPXIV_FETCHER" paper-brief ARXIV_ID || echo "WARN: deepxiv paper-brief failed" >&2
      python3 "$DEEPXIV_FETCHER" paper-head ARXIV_ID || echo "WARN: deepxiv paper-head failed" >&2
      python3 "$DEEPXIV_FETCHER" paper-section ARXIV_ID "Experiments" || echo "WARN: deepxiv paper-section failed" >&2
    else
      echo "WARN: deepxiv_fetch.py search invocation failed; D2 aggregate continues." >&2
    fi
elif command -v deepxiv >/dev/null 2>&1; then
    deepxiv search "QUERY" --limit 10 --format json
    deepxiv paper ARXIV_ID --brief --format json
    deepxiv paper ARXIV_ID --head --format json
    deepxiv paper ARXIV_ID --section "Experiments" --format json
    echo "D2 contribution: deepxiv (CLI fallback)" >&2
else
    echo "DeepXiv unavailable: $DEEPXIV_FETCHER unresolved and no deepxiv CLI; skipping this optional source." >&2
fi

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

De-duplication against other sources:

• Match by arXiv ID first
• Fall back to normalized title when needed
• Keep one canonical paper entry and record deepxiv as an additional source when it overlaps with web/arXiv findings

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:

# Re-resolve $ARIS_REPO + $EXA_FETCHER (SKILL bash blocks may run in separate shells).
if [ -z "${ARIS_REPO:-}" ] && [ -f .aris/installed-skills-codex.txt ]; then
    ARIS_REPO=$(awk -F'\t' '$1=="repo_root"{print $2; exit}' .aris/installed-skills-codex.txt 2>/dev/null) || true
fi
EXA_FETCHER=""
[ -n "${ARIS_REPO:-}" ] && [ -f "$ARIS_REPO/tools/exa_search.py" ] && EXA_FETCHER="$ARIS_REPO/tools/exa_search.py"
[ -z "$EXA_FETCHER" ] && [ -f tools/exa_search.py ] && EXA_FETCHER="tools/exa_search.py"
[ -z "$EXA_FETCHER" ] && [ -f ~/.codex/skills/exa-search/exa_search.py ] && EXA_FETCHER="$HOME/.codex/skills/exa-search/exa_search.py"

if [ -n "$EXA_FETCHER" ]; then
  exa_contributed=false
  # Search for research papers with highlights.
  if python3 "$EXA_FETCHER" search "QUERY" --max 10 --category "research paper" --content highlights; then
    exa_contributed=true
  else
    echo "WARN: exa_search.py research-paper invocation failed; D2 aggregate continues." >&2
  fi
  # Search for broader web content (blogs, docs, news).
  if python3 "$EXA_FETCHER" search "QUERY" --max 10 --content highlights; then
    exa_contributed=true
  else
    echo "WARN: exa_search.py broad-web invocation failed; D2 aggregate continues." >&2
  fi
  [ "$exa_contributed" = "true" ] && echo "D2 contribution: exa (at least one invocation exit 0)" >&2
else
  echo "Exa unavailable: \$EXA_FETCHER unresolved; skipping this optional source." >&2
fi

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

De-duplication against other sources:

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

Gemini search (only when gemini is in sources):

When the user explicitly requests — sources: gemini (or includes gemini in a combined source list), use Gemini for AI-powered broad literature discovery.

Priority 1 — Gemini MCP (preferred): Call mcp__gemini-cli__ask-gemini with the search prompt:

mcp__gemini-cli__ask-gemini({
  prompt: 'You are a research literature scout. Search comprehensively for papers on: "QUERY"

IMPORTANT CONSTRAINTS:
1. Search from MULTIPLE angles — decompose the topic into sub-problems, aliases, neighboring tasks, and common benchmark/settings variants.
2. Prefer papers that are genuinely relevant, not merely keyword-adjacent.
3. Include top venues, journals, surveys, recent preprints, and papers with code when available.
4. Focus on papers from 2022 onward unless older foundational work is necessary.

For EACH paper found, provide ALL of the following:
- Title: [exact title]
- Authors: [full author list]
- Year: [publication year]
- Venue: [exact conference/journal name + year, or "arXiv preprint"]
- arXiv ID: [format 2401.12345, or "N/A"]
- DOI: [if available, or "N/A"]
- Code URL: [GitHub/GitLab link if available, or "No code"]
- Summary: [one-sentence core contribution]

Find at least 15 papers.',
  model: 'auto-gemini-3'
})

Priority 2 — Gemini CLI fallback (if MCP unavailable): Use gemini -p "...same prompt..." 2>/dev/null via Bash (timeout: 120s).

If both MCP and CLI are unavailable, skip this source gracefully and continue with the remaining requested sources.

Why use Gemini? Gemini provides AI-driven discovery that goes beyond keyword matching — it decomposes topics, explores naming variants, and surfaces papers that traditional API-based searches may miss. It fills a different retrieval niche from structured database queries.

De-duplication against other sources:

• Match by arXiv ID first, DOI second, normalized title third
• If Gemini returns a paper already found by Semantic Scholar, prefer S2's citation count and venue metadata
• If Gemini returns a paper already found by arXiv, prefer arXiv's structured metadata
• Do not use Gemini-reported citation counts; they may be inaccurate

OpenAlex search (only when openalex is in sources):

When the user explicitly requests — sources: openalex (or includes openalex in a combined source list), use OpenAlex API for comprehensive academic metadata:

# Re-resolve $ARIS_REPO + $OPENALEX_FETCHER (SKILL bash blocks may run in separate shells).
if [ -z "${ARIS_REPO:-}" ] && [ -f .aris/installed-skills-codex.txt ]; then
    ARIS_REPO=$(awk -F'\t' '$1=="repo_root"{print $2; exit}' .aris/installed-skills-codex.txt 2>/dev/null) || true
fi
OPENALEX_FETCHER=""
[ -n "${ARIS_REPO:-}" ] && [ -f "$ARIS_REPO/tools/openalex_fetch.py" ] && OPENALEX_FETCHER="$ARIS_REPO/tools/openalex_fetch.py"
[ -z "$OPENALEX_FETCHER" ] && [ -f tools/openalex_fetch.py ] && OPENALEX_FETCHER="tools/openalex_fetch.py"
[ -z "$OPENALEX_FETCHER" ] && [ -f ~/.codex/skills/openalex/openalex_fetch.py ] && OPENALEX_FETCHER="$HOME/.codex/skills/openalex/openalex_fetch.py"

# Skip OpenAlex when the helper or its optional dependency is unavailable.
if [ -z "$OPENALEX_FETCHER" ] || ! python3 -c "import requests" >/dev/null 2>&1; then
  echo "OpenAlex source not available (openalex_fetch.py unresolved or 'requests' module missing); skipping." >&2
else
  if python3 "$OPENALEX_FETCHER" search "QUERY" --max 10 \
      --year "2022-" \
      --type article \
      --sort relevance; then
    echo "D2 contribution: openalex (helper invocation exit 0)" >&2
  else
    echo "WARN: openalex_fetch.py invocation failed; D2 aggregate continues with remaining sources." >&2
  fi
fi

If openalex_fetch.py is not found or the requests module is missing, skip this source gracefully and continue with the remaining requested sources.

Why use OpenAlex? OpenAlex provides an open citation graph, institutional affiliations, funding data, and broad work metadata across disciplines.

De-duplication against other sources:

• Match by DOI first, then arXiv ID, then normalized title
• If OpenAlex and Semantic Scholar overlap, prefer S2 for citation counts and venue metadata
• If OpenAlex and arXiv overlap, prefer arXiv's PDF link and metadata while retaining OpenAlex's citation and institution data

Optional PDF download (only when ARXIV_DOWNLOAD = true):

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

# Re-resolve $ARXIV_FETCHER (SKILL bash blocks may run in separate shells).
if [ -z "${ARIS_REPO:-}" ] && [ -f .aris/installed-skills-codex.txt ]; then
    ARIS_REPO=$(awk -F'\t' '$1=="repo_root"{print $2; exit}' .aris/installed-skills-codex.txt 2>/dev/null) || true
fi
ARXIV_FETCHER=""
[ -n "${ARIS_REPO:-}" ] && [ -f "$ARIS_REPO/tools/arxiv_fetch.py" ] && ARXIV_FETCHER="$ARIS_REPO/tools/arxiv_fetch.py"
[ -z "$ARXIV_FETCHER" ] && [ -f tools/arxiv_fetch.py ] && ARXIV_FETCHER="tools/arxiv_fetch.py"
[ -z "$ARXIV_FETCHER" ] && [ -f ~/.codex/skills/arxiv/arxiv_fetch.py ] && ARXIV_FETCHER="$HOME/.codex/skills/arxiv/arxiv_fetch.py"

# Download top N most relevant arXiv papers; skip silently if helper unresolved.
[ -n "$ARXIV_FETCHER" ] && python3 "$ARXIV_FETCHER" 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

If — composed: <canonical-report-path> is present, return/fold the table, landscape, and gaps into that report instead of writing a standalone literature summary. Standalone remains the default; — standalone wins and an existing report never activates composed mode. Keep reusable PDFs/BibTeX and traces. See output-composition.md.

Step 6: Update Research Wiki

If the project has an active research wiki, update it after producing the literature review:

1. Add or update the topic page with the final paper table, grouped themes, and open gaps.
2. Link each paper to its canonical source and local PDF path if available.
3. Record which sources were used: Zotero, Obsidian, local PDFs, arXiv, Semantic Scholar, DeepXiv, Exa, or broader web.
4. Mark unresolved search gaps and papers requiring follow-up reading.
5. Follow the wiki integration contract in shared-references/integration-contract.md.
6. When the wiki helper is available, rebuild query_pack.md after updating literature entries so /idea-creator can reuse the latest gaps and failed directions.

If the wiki path or format is unclear, ask before writing. Do not invent a wiki location.

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
• If a user-requested Zotero or Obsidian source is unavailable, stop and report the missing configuration instead of silently degrading.
• Only unrequested optional sources may be skipped automatically.
• 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.