MCP HubMCP Hub
Volver a habilidades

parallel-web

K-Dense-AI
Actualizado Today
26,534
2,743
26,534
Ver en GitHub
Metapdfdata

Acerca de

La habilidad parallel-web es un kit de herramientas web todo en uno para desarrolladores, especializado en fuentes académicas y científicas. Maneja búsquedas web, extracción de contenido de URL, enriquecimiento de datos masivos e informes de investigación profunda, priorizando bases de datos y literatura académica. Úsala para cualquier tarea que involucre consultas web, obtención de datos o investigación, especialmente cuando se requiere rigor académico.

Instalación rápida

Claude Code

Recomendado
Principal
npx skills add K-Dense-AI/claude-scientific-skills -a claude-code
Comando PluginAlternativo
/plugin add https://github.com/K-Dense-AI/claude-scientific-skills
Git CloneAlternativo
git clone https://github.com/K-Dense-AI/claude-scientific-skills.git ~/.claude/skills/parallel-web

Copia y pega este comando en Claude Code para instalar esta habilidad

Documentación

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...CapabilityWhere
Look something up, research a topic, find current infoWeb Searchreferences/web-search.md
Fetch content from a specific URL (webpage, article, PDF)Web Extractreferences/web-extract.md
Add web-sourced fields to a list of companies/people/productsData Enrichmentreferences/data-enrichment.md
Get an exhaustive, multi-source report (user says "deep research", "exhaustive", "comprehensive")Deep Researchreferences/deep-research.md
Install or authenticate parallel-cliSetupBelow
Check status of a running research/enrichment taskStatusBelow
Retrieve completed research results by run IDResultBelow

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.


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.

Repositorio GitHub

K-Dense-AI/claude-scientific-skills
Ruta: skills/parallel-web
0
agent-skillsai-scientistbioinformaticschemoinformaticsclaudeclaude-skills

Habilidades relacionadas

content-collections

Meta

Esta habilidad proporciona una configuración probada en producción para Content Collections, una herramienta centrada en TypeScript que transforma archivos Markdown/MDX en colecciones de datos con tipado seguro mediante validación Zod. Úsala al construir blogs, sitios de documentación o aplicaciones Vite + React con mucho contenido para garantizar seguridad de tipos y validación automática de contenido. Abarca todo, desde la configuración del plugin de Vite y compilación MDX hasta la optimización de despliegue y validación de esquemas.

Ver habilidad

polymarket

Meta

Esta habilidad permite a los desarrolladores crear aplicaciones con la plataforma de mercados de predicción Polymarket, incluyendo la integración de API para operaciones y datos de mercado. También proporciona transmisión de datos en tiempo real a través de WebSocket para monitorear operaciones en vivo y actividad del mercado. Úsela para implementar estrategias de trading o crear herramientas que procesen actualizaciones de mercado en tiempo real.

Ver habilidad

creating-opencode-plugins

Meta

Esta habilidad ayuda a los desarrolladores a crear complementos de OpenCode que se conectan a más de 25 tipos de eventos, como comandos, archivos y operaciones LSP. Proporciona la estructura del complemento, las especificaciones de la API de eventos y los patrones de implementación para módulos en JavaScript/TypeScript. Úsala cuando necesites interceptar, monitorear o extender el ciclo de vida del asistente de IA de OpenCode con lógica personalizada basada en eventos.

Ver habilidad

sglang

Meta

SGLang es un framework de alto rendimiento para el servicio de LLM que se especializa en generación rápida y estructurada para JSON, expresiones regulares y flujos de trabajo de agentes utilizando su caché de prefijos RadixAttention. Ofrece una inferencia significativamente más rápida, especialmente para tareas con prefijos repetidos, lo que lo hace ideal para salidas complejas y estructuradas, y conversaciones multiturno. Elige SGLang sobre alternativas como vLLM cuando necesites decodificación restringida o estés construyendo aplicaciones con uso extensivo de prefijos compartidos.

Ver habilidad