Mmcp.market

gemini-search skill

by wanshuiyin·wanshuiyin/Auto-claude-code-research-in-sleep·17k stars·MIT

Search research papers via Gemini for broad literature discovery. Use when user says "gemini search", "gemini papers", "search with gemini", or wants AI-powered literature discovery beyond arXiv/Semantic Scholar indexes.

A100/100content scan

Is the gemini-search skill safe?

Clean: nothing in its files matched our rules. We read 1 file in the folder on 2026-09-28.

No findings.

Install the gemini-search skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.

git clone --depth 1 https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git /tmp/Auto-claude-code-research-in-sleep
mkdir -p ~/.claude/skills
cp -r /tmp/Auto-claude-code-research-in-sleep/skills/skills-codex/gemini-search ~/.claude/skills/gemini-search
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

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

  • MAXRESULTS = 15** — Target number of papers Gemini should find.
  • MINYEAR = 2022** — Default minimum publication year. Override with — year: 2020-.
  • DEFAULTMODEL = auto-gemini-3 — Auto-routes within the Gemini 3 family (Pro / Flash) by server-side capacity. Required by mcpgemini-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

  1. Node.js v16.0.0+
  2. Google Gemini CLI — installed and authenticated
npm install -g @google/gemini-cli
   gemini auth
  1. 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

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 mcpgemini-cliask-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

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

If mcpgemini-cliask-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 mcpgemini-cliask-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.

More skills from wanshuiyin/Auto-claude-code-research-in-sleep

  • Aablation-plannerUse when main results pass result-to-claim (claim_supported=yes or partial) and ablation studies are needed for paper submission.
  • Aablation-plannerUse when main results pass result-to-claim (`claim_supported = yes` or `partial`) and ablation studies are needed for paper submission. A secondary Codex agent designs ablations from a reviewer's perspective; the local executor reviews feasibility and implements.
  • AalphaxivQuick single-paper lookup via AlphaXiv LLM-optimized summaries with tiered source fallback. Use when user says "explain this paper", "summarize paper", pastes an arXiv/AlphaXiv URL, or provides a bare arXiv ID for quick understanding - not for broad literature search.
  • AalphaxivQuick single-paper lookup via AlphaXiv LLM-optimized summaries with tiered source fallback. Use when user says "explain this paper", "summarize paper", pastes an arXiv/AlphaXiv URL, or provides a bare arXiv ID for quick understanding - not for broad literature search.
  • Aanalyze-resultsAnalyze ML experiment results, compute statistics, generate comparison tables and insights. Use when user says "analyze results", "compare", or needs to interpret experimental data.
  • Aanalyze-resultsAnalyze ML experiment results, compute statistics, generate comparison tables and insights. Use when user says \"analyze results\", \"compare\", or needs to interpret experimental data.
  • AarxivSearch, download, and summarize academic papers from arXiv. Use when user says "search arxiv", "download paper", "fetch arxiv", "arxiv search", "get paper pdf", or wants to find and save papers from arXiv to the local paper library.
  • AarxivSearch, download, and summarize academic papers from arXiv. Use when user says \"search arxiv\", \"download paper\", \"fetch arxiv\", \"arxiv search\", \"get paper pdf\", or wants to find and save papers from arXiv to the local paper library.
  • Aauto-paper-improvement-loopAutonomously improve a generated paper via GPT-6-Astra xhigh review → implement fixes → recompile, for 2 rounds. Use when user says \"改论文\", \"improve paper\", \"论文润色循环\", \"auto improve\", or wants to iteratively polish a generated paper.
  • Aauto-paper-improvement-loopAutonomously improve a generated paper via Claude review through claude-review MCP → implement fixes → recompile, for 2 rounds. Use when user says \"改论文\", \"improve paper\", \"论文润色循环\", \"auto improve\", or wants to iteratively polish a generated paper.
  • Aauto-paper-improvement-loopAutonomously improve a generated paper via Gemini review through gemini-review MCP → implement fixes → recompile, for 2 rounds. Use when user says \"改论文\", \"improve paper\", \"论文润色循环\", \"auto improve\", or wants to iteratively polish a generated paper.
  • Aauto-paper-improvement-loopAutonomously improve a generated paper via GPT-6-Astra xhigh review → implement fixes → recompile, for 2 rounds. Use when user says \"改论文\", \"improve paper\", \"论文润色循环\", \"auto improve\", or wants to iteratively polish a generated paper.

All agent skills → · MCP servers