菜单
Run Stanford STORM for perspective-driven, article-style deep research reports with retriever-backed citations. Use when a task needs Wikipedia-like synthesis, outline-driven research, or comparison against heavier open-source research frameworks.
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-stanford-storm |
| description | Run Stanford STORM for perspective-driven, article-style deep research reports with retriever-backed citations. Use when a task needs Wikipedia-like synthesis, outline-driven research, or comparison against heavier open-source research frameworks. |
Use this skill for Wikipedia-style article generation and perspective-driven research. STORM is usually slower and heavier than API search tools, so reserve it for article synthesis benchmarks.
op run --env-file .env.climbhill -- python .agents/skills/deep-research-stanford-storm/scripts/run_storm.py \
--topic "AI for Science research agents" \
--rm-name you \
--fast-model gpt-4o-mini \
--strong-model gpt-4o-mini
Common 1Password-backed variables:
OPENAI_API_KEY or another LiteLLM-compatible model keyBING_SEARCH_API_KEY or YDC_API_KEYPrepare 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 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.