# parallel-web

> All-in-one web toolkit powered by parallel-cli with a strong emphasis on academic and scientific sources — covering web search, URL extraction, bulk data enrichment, and deep research reports grounded in peer-reviewed literature.

**Use case**: Search the web and extract content with academic source priority

**Canonical URL**: https://agentcookbooks.com/skills/parallel-web/

**Topics**: claude-code, skills, science, science

**Trigger phrases**: "search the web for", "fetch this URL", "enrich this dataset from the web", "deep research report on", "find academic papers on"

**Source**: [K-Dense AI](https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/scientific-skills/parallel-web)

**License**: MIT

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## What it does

`parallel-web` is a Claude Code skill from K-Dense AI's [scientific-agent-skills repo](https://github.com/K-Dense-AI/scientific-agent-skills). It turns Claude into a web research agent powered by `parallel-cli` — covering four modes: fast web search (prioritizing peer-reviewed papers, preprints, and scholarly databases over general web results), URL extraction (fetching and parsing page content, including academic PDFs), bulk data enrichment (adding web-sourced fields to CSV/list inputs), and deep research (exhaustive multi-source reports grounded in academic literature).

A session produces sourced web content — search results, extracted page text, enriched datasets, or structured research reports — with academic sources prioritized over general web content.

## When to use it

Reach for it when:

- You want any kind of web-based lookup or research and want academic/scientific sources prioritized over general web results
- You're enriching a dataset with web-sourced metadata (institution names, publication counts, technology descriptions) across many rows
- You need a comprehensive research report on a topic that draws on multiple web sources and synthesizes them into a structured document

When *not* to reach for it:

- Systematic literature reviews with documented, reproducible search methodology — use `literature-review` for that audit trail
- Specific database API queries (UniProt, PubChem, ClinicalTrials) — use `database-lookup`

## Install

Copy the `SKILL.md` from K-Dense AI's [parallel-web folder](https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/scientific-skills/parallel-web) into `.claude/skills/parallel-web/` in your project. Requires `parallel-cli` installed and configured (`pip install parallel-cli` + API key setup). Requires internet access.

Trigger phrases: "search the web for", "fetch this URL", "enrich this dataset from the web", "deep research report on".

## What a session looks like

A typical session has three phases:

1. **Mode selection.** Claude identifies whether you need fast search, URL extraction, bulk enrichment, or deep research based on your request and configures the appropriate `parallel-cli` mode.
2. **Execution.** The search or extraction runs via `parallel-cli`, prioritizing peer-reviewed sources (PubMed, arXiv, Semantic Scholar) in results ranking before general web sources.
3. **Output.** Fast search returns a ranked list of results with URLs and snippets. URL extraction returns cleaned page content as markdown. Bulk enrichment returns your CSV with new columns. Deep research returns a multi-section report with inline citations.

## Receipts

**Where it works well:**
- Quick research questions where you want a sourced answer grounded in academic content rather than a general web summary — the academic source prioritization filters out low-quality results automatically
- Bulk dataset enrichment for modest CSV sizes (hundreds of rows) where manual web lookup would be impractical — the parallel execution handles batching and rate limiting

**Where it backfires:**
- Without `parallel-cli` configured and authenticated, the skill falls back to slower or unavailable modes — the setup step is a prerequisite that some users skip
- Deep research mode is thorough but slow; for simple factual questions it's overkill compared to fast search mode

**Pattern that works:** for research questions where source quality matters, start with fast search to find the right sources, then use URL extraction on the most relevant results to get the full text — two-step retrieval produces better quality than a single deep research run for targeted questions.

## Source and attribution

Skill authored by K-Dense, Inc. The canonical SKILL.md lives in the [`parallel-web` folder](https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/scientific-skills/parallel-web) of K-Dense AI's scientific-agent-skills repository.

License: MIT. Install, adapt, and redistribute with attribution preserved.

This page documents the skill from a practitioner's perspective. For the formal spec and any updates, defer to the source repo.