Search scientific and technical topics with Exa, prioritize scholarly sources using publication and domain filters, and extract text from webpages or PDFs in batches to collect material for research.
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Searches scientific and technical web content with Exa and extracts page or PDF text from URLs in batches. Supports scholarly discovery with the publication category and academic domain filters. Applies to requests to search the web, look up current research, fetch a page, or extract an article using Exa.
实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
A skill for web-powered research tasks backed by Exa: web search and URL extraction. Exa's index combines high-quality keyword and semantic retrieval, which makes it well-suited to scientific, technical, and conceptual queries.
Read the user's request and match it to one of the capabilities below. Read the corresponding reference file for detailed instructions before running commands.
| User wants to... | Capability | Where |
|---|---|---|
| Look something up, research a topic, find current info | Web Search | references/web-search.md |
| Fetch content from a specific URL (webpage, article, PDF) | Web Extract | references/web-extract.md |
| Install or authenticate | Setup | Below |
--category publication to bias toward scholarly sources, and/or an academic --include-domains allowlist. See references/web-search.md for the two-pass academic strategy.For technical or scientific queries, prefer academic and scientific sources:
Two levers to steer Exa toward scholarly content:
--category publication biases retrieval toward scholarly sources.--include-domains with a scholarly allowlist (arxiv.org, nature.com, pubmed.ncbi.nlm.nih.gov, etc.) restricts the domain pool.Combine both to narrow the source pool; verify publication type and peer-review status on each source. See references/web-search.md for the full pattern.
When citing academic sources, include author names and publication year where available (e.g., Smith et al., 2025) in addition to the standard citation format. If a DOI is present, prefer the DOI link.
This skill uses the exa-py Python SDK. The scripts in scripts/ declare their dependencies via PEP 723 inline metadata, so you can run them directly with uv run without a separate install step:
uv run "$SKILL_PATH/scripts/exa_search.py" --help
If you prefer a persistent install:
uv pip install "exa-py>=2.23.0,<3"
All commands read the API key from the EXA_API_KEY environment variable. Get your Exa API key at dashboard.exa.ai/api-keys.
First, check if a .env file exists in the project root and contains EXA_API_KEY. If so, load it:
dotenv -f .env run -- uv run "$SKILL_PATH/scripts/exa_search.py" "your query"
If dotenv isn't available, install it: uv pip install 'python-dotenv[cli]'.
If there's no .env, export the key for the session:
export EXA_API_KEY="your-key"
Use --help to verify installation and CLI parsing; it does not require an API key or validate authentication. Authentication is checked only when a real query is made.
The extractor accepts 1–100 URLs per POST https://api.exa.ai/contents call
and exports each SDK status (id, status, source). It rejects larger batches.
Use --max-age-hours 0 to request fresh content, -1 for cache only, or a
positive age up to 720 hours. Omission uses Exa's cache/fallback policy.
published_date is estimated publication metadata, not retrieval time.
The tested SDK drops per-URL error details, so report failures without inventing
a cause. See URL Extraction.
Reviewed the Search API,
Contents API, and
Exa changelog on 2026-09-30. Both routes use
JSON POST requests and x-api-key authentication (the SDK sets this header).
Search is POST https://api.exa.ai/search; its SDK kwargs use snake_case and
are serialized to camelCase. The current scholarly category is publication;
research paper is a compatibility alias in this wrapper. No offset or cursor
pagination is documented for these endpoints.
Helpers are tested with exa-py==2.23.0, including mocked HTTP transport through
the real SDK. Search content flags are explicit: no flags means metadata only;
--highlights does not implicitly fetch text. Authenticated search/extraction
examples below and in the references are illustrative; no paid live calls were
made during this review.
Every script in this skill sets the x-exa-integration request header to k-dense-ai--scientific-agent-skills so Exa can attribute usage from the K-Dense AI scientific-agent-skills repo to this integration. Do not remove or rename this header when adapting the scripts.
SKILL.md — this file (routing and setup)references/web-search.md — detailed web search reference with academic strategyreferences/web-extract.md — URL content extraction referencescripts/exa_search.py — CLI wrapper around client.searchscripts/exa_extract.py — CLI wrapper around client.get_contents还没有评价。欢迎分享你的使用感受。
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Estimate intracellular reaction rates from steady-state carbon-13 tracing measurements. Validate carbon atom mappings and measurement inputs, simulate labeling, fit constrained fluxes, and diagnose which pathway rates the data can meaningfully constrain.
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Plan protein characterization experiments with Adaptyv's cloud laboratory, estimate costs, submit sequences, track progress, and retrieve measured results. Supports binding, thermal stability, expression, fluorescence, and enzyme activity workflows through the API and Python SDK.
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Analyze time series with aeon, a Python toolkit for data that changes over time. Supports classification, regression, clustering, forecasting, anomaly detection, and pattern search, with guidance on preprocessing, algorithm selection, and evaluation.
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Investigate how DNA variants may affect gene activity using precomputed AlphaGenome Atlas results and model predictions. Rank candidate variants, examine tissue-specific effects, and inspect the features behind scores to develop research hypotheses about biological mechanisms.
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Plan and document studies that assess whether an analytical procedure suits its intended use. Select the governing framework, evaluate calibration, accuracy, precision, and detection limits, and compare methods using acceptance criteria defined before data collection.
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Organize single-cell measurements with cell and gene metadata, read and write h5ad or Zarr files, and prepare subsets or combined datasets. Includes guidance on handling large annotated matrices without loading all measurement values into memory.
中文简介正在准备中。当前显示英语简介。