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tavily-extract

Extract clean markdown or text content from specific URLs via the Tavily CLI. Use this skill when the user has one or more URLs and wants their content, says "extract", "grab the content from", "pull the text from", "get the page at", "read this webpage", or needs clean text from web pages. Handles JavaScript-rendered pages, returns LLM-optimized markdown, and supports query-focused chunking for targeted extraction. Can process up to 20 URLs in a single call.

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EthanYoQ/Skill-hub

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SKILL.md2.8 KB

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tavily extract

Extract clean markdown or text content from one or more URLs.

Before running any command

If tvly is not found on PATH, install it first:

curl -fsSL https://cli.tavily.com/install.sh | bash && tvly login

Do not skip this step or fall back to other tools.

See tavily-cli for alternative install methods and auth options.

When to use

You have a specific URL and want its content
You need text from JavaScript-rendered pages
Step 2 in the workflow: search → extract → map → crawl → research

Quick start

# Single URL
tvly extract "https://example.com/article" --json

# Multiple URLs
tvly extract "https://example.com/page1" "https://example.com/page2" --json

# Query-focused extraction (returns relevant chunks only)
tvly extract "https://example.com/docs" --query "authentication API" --chunks-per-source 3 --json

# JS-heavy pages
tvly extract "https://app.example.com" --extract-depth advanced --json

# Save to file
tvly extract "https://example.com/article" -o article.md

Options

Option	Description
`--query`	Rerank chunks by relevance to this query
`--chunks-per-source`	Chunks per URL (1-5, requires `--query`)
`--extract-depth`	`basic` (default) or `advanced` (for JS pages)
`--format`	`markdown` (default) or `text`
`--include-images`	Include image URLs
`--timeout`	Max wait time (1-60 seconds)
`-o, --output`	Save output to file
`--json`	Structured JSON output

Extract depth

Depth	When to use
`basic`	Simple pages, fast — try this first
`advanced`	JS-rendered SPAs, dynamic content, tables

Tips

Max 20 URLs per request — batch larger lists into multiple calls.
Use --query + --chunks-per-source to get only relevant content instead of full pages.
Try basic first, fall back to advanced if content is missing.
Set --timeout for slow pages (up to 60s).
If search results already contain the content you need (via --include-raw-content), skip the extract step.

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使用 AACT (Aggregate Analysis of ClinicalTrials.gov) PostgreSQL 数据仓库进行批量、历史、聚合性临床试验数据挖掘。Use this skill when the user requests bulk SQL analysis over the full clinical trials data warehouse — historical trial trends, disease landscapes, similar-design matching, or multi-year aggregations across hundreds of thousands of NCT records. 触发场景包括：AACT 查询、临床试验批量分析、PostgreSQL 试验数据、全量 NCT 检索、试验数据挖掘、历史试验分析、clinical trials data warehouse、SQL trials、bulk trial analysis、disease landscape、试验设计相似性匹配、跨年度聚合、sponsor/phase/country 多维统计。**与 clinical-trials-v2 差异**：本 skill 走批量 SQL · 离线大数据（PostgreSQL）；v2 走实时 API · 单查询。两者互补：单条 NCT 实时状态用 v2，百万级历史挖掘用本 skill。支持云端公共 PostgreSQL（aact-db.ctti-clinicaltrials.org · 零部署）和每日 dump 本地还原（高性能 · 离线）两种连接方式，自动检测优先用本地。跨平台（macOS/Linux/Windows）参数化 SQL 防注入，read-only 强制保护。

中文简介正在准备中。当前显示原文说明。