Gemini Search Agent Skill

Gemini Literature Search

Search query: $ARGUMENTS

Role & Positioning

This skill uses Gemini as a broad literature discovery source:

| Skill | Source | Best for |

|-------|--------|----------|

| /arxiv | arXiv API | Latest preprints, cutting-edge unrefereed work |

| /semantic-scholar | Semantic Scholar API | Published venue papers (IEEE, ACM, Springer) with citation counts |

| /deepxiv | DeepXiv CLI | Layered reading: search, brief, section map, section reads |

| /exa-search | Exa API | Broad web search: blogs, docs, news, companies, research papers |

| /gemini-search | Gemini MCP / CLI | AI-powered broad literature discovery — searches across multiple angles, aliases, and sub-problems |

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 can be 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

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

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

• gemini-mcp-tool — MCP bridge for Codex CLI (jamubc/gemini-mcp-tool)

   npm install -g gemini-mcp-tool

MCP Configuration

Register the Gemini bridge in Codex CLI:

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

Authentication

Gemini CLI uses your Google account or an API key. Export the key in your shell or project environment:

export GEMINI_API_KEY=your-key-here

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

Available MCP Tools

| Tool | Parameters | Description |

|------|-----------|-------------|

| mcp__gemini-cli__ask-gemini | prompt (required), model (optional), sandbox (optional) | Ask Gemini for analysis or research; supports @file syntax |

| mcp__gemini-cli__sandbox-test | prompt (required), model (optional) | Safe code execution in sandbox |

| mcp__gemini-cli__ping | — | Connection test |

| mcp__gemini-cli__help | — | Show Gemini CLI help |

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.