菜单
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.
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
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.
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.
| name | deep-research-api-calls |
| description | 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. |
Use this skill when you need to call a third-party deep research agent or create a reproducible command for one. Prefer the repository benchmark runner when comparing providers; use direct calls only for focused experiments.
Every benchmark call must target a maximum expected cost below $1. If a
provider exposes effort, search count, source count, reasoning token, or max
output controls, set the lowest mode that can still answer the prompt. Record
actual cost metadata when the provider returns it; otherwise record the pricing
assumption and measured token/search counts.
Load keys from 1Password with op run rather than hard-coding secrets:
op run --env-file .env.climbhill -- python benchmark/run_benchmark.py --max-questions 1 -v
Expected environment names:
| Provider | Environment Variable |
|---|---|
| OpenAI | OPENAI_API_KEY |
| Perplexity | PERPLEXITY_API_KEY |
| Exa | EXA_API_KEY |
| Tavily | TAVILY_API_KEY |
| Jina AI | JINA_API_KEY |
| xAI | XAI_API_KEY |
| You.com | YOU_API_KEY |
| Google Gemini | GOOGLE_API_KEY |
| Provider | Typical Command | Budget Controls |
|---|---|---|
| OpenAI Deep Research | python .agents/skills/deep-research-openai/scripts/run_deep_research.py --prompt "$PROMPT" | prefer mini/deep-research model, cap output, short prompt |
| Perplexity Sonar Deep Research | python .agents/skills/deep-research-perplexity/scripts/ask.py --prompt "$PROMPT" | use low search/context mode where available |
| Exa Research | python .agents/skills/deep-research-exa/scripts/exa_tools.py search "$PROMPT" --num-results 5 --highlights | use search for benchmark smokes; reserve research mode for deeper runs |
| Tavily Search | python .agents/skills/deep-research-tavily/scripts/tavily_search.py --query "$PROMPT" | cap results and include answer mode only when needed |
| Jina AI | python .agents/skills/deep-research-jina/scripts/jina_tools.py search "$PROMPT" | cap result count and fetched pages |
| xAI Grok | python .agents/skills/deep-research-xai-grok/scripts/grok_research.py --query "$PROMPT" | use economical model and cap output |
| You.com ARI | provider-specific ARI endpoint wrapper | avoid high-cost professional report mode for benchmark calls |
| Google Gemini Deep Research | Gemini API or UI workflow where available | cap grounding/search effort and output tokens |
Use short prompts that still exercise multi-step behavior:
deepresearch the deepresearchers: identify current deep research providers,
APIs, cost controls, and citation behavior.AI for Science: compare two AI-for-science agent systems with primary
sources and uncertainty notes.standards trace: find the current source of truth for a technical standard
and summarize revision status.Save raw provider output first. Then normalize it with deep-research-okf-normalize
so reports can be compared as markdown concepts with citations.