parallel-web

name	parallel-web
description	Search the web, extract URL content, enrich datasets, and run deep research using parallel-cli, prioritizing academic and scientific sources. Use for ANY web-related task: looking things up, fetching pages or academic PDFs, bulk data enrichment, and exhaustive multi-source research reports — even when the user doesn't mention 'parallel' or 'web' explicitly.
compatibility	Requires parallel-cli and internet access.
metadata	{"skill-author":"K-Dense Inc."}

Parallel Web Toolkit

A unified skill for all web-powered tasks: searching, extracting, enriching, and researching — with academic and scientific sources as the default priority.

Routing — pick the right capability

Read the user's request and match it to one of the capabilities below. For web search, extract, enrichment, and deep research, read the corresponding reference file for detailed instructions.

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
Add web-sourced fields to a list of companies/people/products	Data Enrichment	references/data-enrichment.md
Get an exhaustive, multi-source report (user says "deep research", "exhaustive", "comprehensive")	Deep Research	references/deep-research.md
Install or authenticate parallel-cli	Setup	Below
Check status of a running research/enrichment task	Status	Below
Retrieve completed research results by run ID	Result	Below

Decision guide

• Default to Web Search for a single lookup, research question, or "what is X?" query. It's fast and cost-effective. When the query touches a scientific or technical topic, include academic domains (see references/web-search.md) to surface peer-reviewed and preprint sources alongside general results.
• Use Web Extract when the user provides a URL or asks you to read/fetch a specific page. Prefer this over the built-in WebFetch tool. Particularly useful for extracting full text from academic PDFs, preprint servers, and journal articles.
• Use Data Enrichment when the user has multiple entities (a CSV, a list of companies/people/products, or even a short inline list) and wants to find or add the same kind of information for each one. The key signal is a repeated lookup across a set of items — e.g., "find the CEO for each of these companies" or "get the founding year for Apple, Stripe, and Anthropic." Even if the user doesn't say "enrich," use parallel-cli enrich whenever the task is the same query applied to multiple entities. Do NOT use Web Search in a loop for this — the enrichment pipeline handles batching, parallelism, and structured output automatically.
• Use Deep Research only when the user explicitly asks for deep, exhaustive, or comprehensive research. It is 10-100x slower and more expensive than Web Search — never default to it. Deep research is especially valuable for literature reviews and multi-paper synthesis.
• If parallel-cli is not found when running any command, follow the Setup section below.

Academic source priority

Across all capabilities, prefer academic and scientific sources when the query is technical or scientific in nature. This means:

• Peer-reviewed journal articles and conference proceedings over blog posts or news articles
• Preprints (arXiv, bioRxiv, medRxiv) when peer-reviewed versions aren't available
• Institutional and government sources (NIH, WHO, NASA, NIST) over commercial sites
• Primary research over secondary summaries

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.

Context chaining

Several capabilities support multi-turn context via interaction_id. When a research or enrichment task completes, it returns an interaction_id. If the user asks a follow-up question related to that task, pass --previous-interaction-id to carry context forward automatically. This avoids restating what was already found.

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Setup

If parallel-cli is not installed, install and authenticate:

curl -fsSL https://parallel.ai/install.sh | bash

If unable to install that way, use uv instead:

uv tool install "parallel-web-tools[cli]"

Then authenticate. First, check if a .env file exists in the project root and contains PARALLEL_API_KEY. If so, load it with dotenv:

dotenv -f .env run parallel-cli auth

If dotenv isn't available, install it with pip install python-dotenv[cli] or uv pip install python-dotenv[cli].

If there's no .env file or it doesn't contain the key, fall back to interactive login:

parallel-cli login

Or set the key manually: export PARALLEL_API_KEY="your-key"

Verify with:

parallel-cli auth

If parallel-cli is not found after install, add ~/.local/bin to PATH.

Check task status

parallel-cli research status "$RUN_ID" --json

Report the current status to the user (running, completed, failed, etc.).

Get completed result

parallel-cli research poll "$RUN_ID" --json

Present results in a clear, organized format.