Gemini Literature Search

Search query: $ARGUMENTS

Role & Positioning

This skill uses Gemini as a broad literature discovery source:

Use Gemini when you want AI-driven discovery that goes beyond keyword matching — Gemini decomposes topics into sub-problems, explores naming variants, and surfaces papers that traditional API searches may miss.

Constants

• MAX_RESULTS = 15 — Target number of papers Gemini should find.
• MIN_YEAR = 2022 — Default minimum publication year. Override with — year: 2020-.
• DEFAULT_MODEL = auto-gemini-3 — Auto-routes within the Gemini 3 family (Pro / Flash) by server-side capacity. Required by mcp__gemini-cli__ask-gemini and gemini-cli v0.40+; explicit gemini-3-pro-preview is silently downgraded to gemini-2.5-pro on OAuth-personal / Google One AI Pro accounts when capacity is exhausted. Override with — model: gemini-3-flash-preview (Gemini 3 Flash explicit, faster, higher quota), or — model: gemini-2.5-pro / gemini-2.5-flash (legacy, only for users on older gemini-cli < v0.40). The MCP tool accepts all of these verbatim.

Overrides (append to arguments):

• /gemini-search "topic" — max: 20 — request up to 20 papers
• /gemini-search "topic" — year: 2020- — papers from 2020 onward
• /gemini-search "topic" — code-only — only papers with open-source code
• /gemini-search "topic" — venues: NeurIPS,ICML,ICLR — focus on specific venues
• /gemini-search "topic" — model: gemini-3-flash-preview — Gemini 3 Flash (faster, higher quota, less capable than Pro)
• /gemini-search "topic" — model: auto-gemini-3 — auto-routes within the Gemini 3 family by load
• /gemini-search "topic" — model: gemini-2.5-pro — legacy (only if your gemini-cli < v0.40)

Environment & Setup

Prerequisites

1. Node.js v16.0.0+
2. Google Gemini CLI — installed and authenticated

   npm install -g @google/gemini-cli
   gemini auth
   

3. gemini-mcp-tool — MCP bridge for Claude Code (jamubc/gemini-mcp-tool)

   npm install -g gemini-mcp-tool
   

MCP Configuration

In ~/.claude.json (or %APPDATA%\Claude\claude_desktop_config.json for Claude Desktop), add:

{
  "mcpServers": {
    "gemini-cli": {
      "command": "gemini-mcp"
    }
  }
}

Alternative via npx (auto-install):

{
  "mcpServers": {
    "gemini-cli": {
      "command": "npx",
      "args": ["-y", "gemini-mcp-tool"]
    }
  }
}

Or one-line setup:

claude mcp add gemini-cli -- npx -y gemini-mcp-tool

Authentication

Gemini CLI uses your Google account or an API key. Reuse an existing Google account login, or set an API key in the same shell before starting Claude Code so the MCP process inherits it:

export GEMINI_API_KEY='your-key-here'
claude

A separate tool-shell export does not update an already running host or MCP process. If you keep credentials in an environment file, load it explicitly before starting the host rather than relying on automatic .env loading.

• Free key from Google AI Studio
• Flash model (gemini-2.5-flash) has a generous free tier (500 req/min)

Available MCP Tools

Verify Setup

gemini --version

Workflow

Step 1: Parse Arguments

Parse $ARGUMENTS for:

• query: The research topic (required)
• max: Override MAX_RESULTS
• year: Minimum publication year (e.g., 2020-)
• code-only: Only include papers with open-source code
• venues: Comma-separated venue filter
• model: Override DEFAULT_MODEL

Step 2: Execute Search (MCP Priority)

Priority 1 — Gemini MCP (preferred):

Try calling 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 — do not just use the exact query. 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 MIN_YEAR onward unless older foundational work is necessary.

For EACH paper found, provide ALL of the following in this exact format:
- Title: [exact title]
- Authors: [full author list]
- Year: [publication year]
- Venue: [exact conference/journal name + year, or "arXiv preprint" if not published]
- 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 MAX_RESULTS papers with good coverage across:
- strong recent papers from top venues
- surveys/reviews if they exist
- papers with open-source code
- closely related variants of the topic

Format as a numbered list with all fields for each paper.',
  model: 'auto-gemini-3'
})

Priority 2 — Gemini CLI fallback (if MCP unavailable):

If mcp__gemini-cli__ask-gemini fails or is not configured, fall back to CLI:

gemini -p 'You are a research literature scout. Search comprehensively for papers on: "QUERY"
...same prompt as above...' 2>/dev/null

• Timeout: 120 seconds
• Stderr: Pipe to /dev/null — contains hook warnings, not part of the response

When to use which:

• MCP is preferred because it integrates natively with Claude Code's tool system, handles model selection, and avoids shell escaping issues.
• CLI fallback ensures the skill works even when MCP is not configured or the MCP server process has crashed.

Step 3: Parse Results

Extract structured paper information from Gemini's response. For each paper, normalize to:

{
  title, authors, year, venue,
  arxiv_id,    // "N/A" if not available
  doi,         // "N/A" if not available
  code_url,    // "No code" if not available
  summary      // one-sentence contribution
}

If Gemini returns fewer papers than requested, note this but do not re-query.

Step 4: Present Results

Format results as a structured table:

| # | Title | Venue | Year | Code | Summary |
|---|-------|-------|------|------|---------|
| 1 | ... | NeurIPS 2024 | 2024 | [GitHub](url) | ... |
| 2 | ... | IEEE TWC | 2023 | No | ... |

For each paper, also show:

• arXiv ID: if available (for cross-reference with /arxiv)
• DOI: if available (canonical link for published papers)
• Code: GitHub/GitLab link or "No"

Step 5: Offer Follow-up

After presenting results, suggest:

/semantic-scholar "topic"    — search published venue papers with citation counts
/arxiv "arXiv:XXXX.XXXXX"   — fetch specific preprint details
/research-lit "topic" — sources: gemini, semantic-scholar  — combined multi-source review
/novelty-check "idea"       — verify novelty against literature

Key Rules

• MCP first, CLI second. Always try mcp__gemini-cli__ask-gemini before falling back to gemini -p.
• Gemini is a discovery source, not a database. Its results may include papers it "knows about" from training data. Always cross-verify critical details (exact titles, venues, years) via /semantic-scholar or /arxiv when precision matters.
• Do not use Gemini for citation counts. It may hallucinate citation numbers. Use Semantic Scholar for authoritative citation data.
• Pipe stderr to /dev/null in CLI mode — Gemini CLI emits hook warnings on stderr.
• Timeout generously in CLI mode — Gemini's thorough search can take 30-60 seconds. Set timeout to 120s.
• If both MCP and CLI are unreachable, suggest using /semantic-scholar, /arxiv, or /research-lit "topic" — sources: web as alternatives.