菜单
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.
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-exa |
| description | 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. |
Use this skill for neural web search, source discovery, and Exa's agentic deep research endpoint.
op run --env-file .env.climbhill -- python .agents/skills/deep-research-exa/scripts/exa_tools.py \
search "deepresearch the deepresearchers" \
--num-results 5 \
--highlights
Required 1Password-backed variable:
EXA_API_KEYsearch command with --num-results 5 --highlights for under-$1 benchmark smokes.research --model exa-research only when the task needs Exa's agentic research endpoint.--num-results to 5 or fewer.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 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.
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.