菜单
Run Google Gemini deep research and search-grounded Gemini research tasks with bounded scope and captured outputs. Use when research should rely on Gemini, Google Search grounding, or Google-hosted document context.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | deep-research-gemini |
| description | Run Google Gemini deep research and search-grounded Gemini research tasks with bounded scope and captured outputs. Use when research should rely on Gemini, Google Search grounding, or Google-hosted document context. |
Use this skill for Gemini Deep Research Agent or Gemini search-grounded research flows. Gemini Deep Research is useful when research should be grounded in Google Search or uploaded documents.
op run --env-file .env.climbhill -- python .agents/skills/deep-research-gemini/scripts/gemini_research.py \
--prompt "Research current deep research APIs. Keep the answer concise and cite sources." \
--mode grounded \
--model gemini-3.5-flash
Required 1Password-backed variables:
GOOGLE_API_KEYGOOGLE_CLOUD_PROJECT when project-scoped Gemini or Google Cloud APIs need a project resource name$1 per-call budget. Use
--mode deep-research-start only for a deliberate live Deep Research Agent
experiment.Prepare and run bounded calls across deep research provider APIs with explicit cost limits, API-key sources, benchmark prompts, and output capture. Use when comparing providers, creating reproducible API commands, or normalizing raw provider outputs after a research run.
Run Exa-backed deep research, neural search, and page-content retrieval with bounded result and page-read counts. Use when a task needs Exa Search, Exa Contents, or Exa Research inside a source-discovery or benchmark workflow.
Run GPT Researcher as a self-hosted open-source deep research framework using an LLM plus a search provider. Use when a task needs inspectable local report generation, configurable retrievers, or comparison against commercial research APIs.
Use Jina Reader and Jina Search APIs for deep research retrieval, URL-to-markdown conversion, and markdown-oriented search results. Use when a workflow needs clean page extraction, search snippets, or reader output before synthesis.
Run LangChain Open Deep Research or LangGraph-based iterative research workflows with configurable retrieval, reflection, and server-backed execution. Use when a benchmark or task needs inspectable graph orchestration rather than a single hosted research API.
Run OpenAI deep research models through the Responses API with explicit effort, model, cost, and output controls. Use when launching citation-backed OpenAI research runs, smoke-testing OpenAI provider behavior, or capturing reproducible benchmark output.