lit-review

---
name: lit-review
description: Retrieves papers from local folder or ArXiv and Semantic Scholar using domain-aware keyword expansion, builds synthesis matrix, identifies gaps. Calls tools/arxiv_fetch.py and tools/semantic_scholar_fetch.py. Writes to output/paper-cache/ and output/LIT_REVIEW_REPORT.md.
argument-hint: [paper-topic-or-url]
tools: Bash, Read, Write, WebFetch, WebSearch, Agent, Glob, Grep,
---

# Skill: lit-review

You perform a targeted literature review for $ARGUMENTS. Your outputs feed idea discovery and paper writing. This skill checks multiple sources **in priority order**. All are optional — if a source is not configured or not requested, skip it silently.

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. Custom path specified by user in `CLAUDE.md` under `## Constants`
- **MAX_LOCAL_PAPERS = 30** — 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:
> - `/lit-review "topic" — paper library: ~/my_papers/` — custom local PDF path


### 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`, `all`.
- **If not specified**: Default to `all` — search every available source in priority order (`semantic-scholar` is **excluded** from `all`; it must be explicitly listed).

Examples:
```
/lit-review "diffusion models"                                    → all (default, no S2)
/lit-review "diffusion models" — sources: all                     → all (default, no S2)
/lit-review "diffusion models" — sources: zotero                  → Zotero only
/lit-review "diffusion models" — sources: zotero, web             → Zotero + web
/lit-review "diffusion models" — sources: local                   → local PDFs only
/lit-review "topic" — sources: obsidian, local, web               → skip Zotero
/lit-review "topic" — sources: web, semantic-scholar              → web + S2 API (IEEE/ACM venue papers)
/lit-review "topic" — sources: all, semantic-scholar              → all + S2 API
```

### 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` | Raw PDF content (first 3 pages) |
| 4 | **Web search** | `web` | Always available (WebSearch) | arXiv, Semantic Scholar, Google Scholar |
| 5 | **Semantic Scholar API** | `semantic-scholar` | `tools/semantic_scholar_fetch.py` exists | Published venue papers (IEEE, ACM, Springer) with structured metadata: citation counts, venue info, TLDR. **Only runs when explicitly requested** via `— sources: semantic-scholar` or `— sources: web, semantic-scholar` |

> **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-draft` 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
   ```

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 3 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:
```bash
# Try to find arxiv_fetch.py
SCRIPT=$(find tools/ -name "arxiv_fetch.py" 2>/dev/null | head -1)
# If not found
[ -z "$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 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:

```bash
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 papers with quality filters
python3 "$S2_SCRIPT" search "QUERY" --max 10 \
  --fields-of-study "GIScience, GeoAI" \
  --publication-types "JournalArticle, Conference"
```

If `semantic_scholar_fetch.py` is not found, skip silently.

**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.

**Optional PDF download** (only when `ARXIV_DOWNLOAD = true`):

After all sources are searched and papers are ranked by relevance:
```bash
# 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

Read `skills/knowledge/synthesis-analyst.md` for the synthesis protocol. Ignore if `skills/knowledge/synthesis-analyst.md` not found.
then:
1. Group papers by theme (not chronology).
2. Build a synthesis matrix: paper × (method, dataset, key metric, finding, limitation).
3. Identify consensus views, contradictions, and geographic biases.
4. Write synthesis to the **Synthesis** section of `output/LIT_REVIEW_REPORT.md`.

Find gaps that our work can fill. For each gap dimension (Methodological, Geographic, Temporal, Data, Equity, Validation):
- Score gap: Novelty × 0.4 + Feasibility × 0.35 + Impact × 0.25
- Rank top 5 gaps
- Write to the **Gap Analysis** section of `output/LIT_REVIEW_REPORT.md`

### 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 `papers/`
- Update related work notes in project memory
- **Update** `memory/MEMORY.md` 
- If Obsidian is available, optionally create a literature review note in the vault

## Outputs
- `output/paper-cache/` — JSON paper metadata files
- `output/LIT_REVIEW_REPORT.md` — single consolidated report containing:
  1. **Findings** — one-line summaries of key discoveries (appended across runs; dated entries)
  2. **Synthesis (YYYY-MM-DD)** — thematic synthesis with paper × (method, dataset, metric, finding, limitation) matrix
  3. **Gap Analysis** — ranked top-5 gaps across Methodological, Geographic, Temporal, Data, Equity, Validation dimensions

> When re-running the skill, **append** new dated Synthesis and Gap Analysis sections rather than overwriting — preserves history. Findings entries are always appended.


## 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.