Parallel Web

Community

dralkh

parallel-web

All-in-one web toolkit powered by parallel-cli, with a strong emphasis on academic and scientific sources. Use this skill whenever the user needs to search the web, fetch/extract URL content, enrich data with web-sourced fields, or run deep research reports. Covers: web search (fast lookups, research, current info — prioritizing peer-reviewed papers, preprints, and scholarly databases), URL extraction (fetching pages, articles, academic PDFs), bulk data enrichment (adding fields to CSV/lists from the web), and deep research (exhaustive multi-source reports grounded in academic literature). Also handles setup, status checks, and result retrieval. Use this skill for ANY web-related task — even if the user doesn't mention 'parallel' or 'web' explicitly. If they want to look something up, fetch a page, enrich a dataset, investigate a topic, find academic papers, check citations, or review scientific literature, this is the skill to use.

Overview

Publisher	dralkh
Repository	`seerai`
Skill name	`parallel-web`
Stars	96
Forks	5
Bundled files	4
License	MIT
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
4 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by dralkh on GitHub. Read the source before you install it.

Installation

Install the Parallel Web AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/dralkh/seerai/tree/main/skills/parallel-web
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/dralkh/seerai.git /tmp/seerai
mkdir -p .claude/skills
cp -r /tmp/seerai/skills/parallel-web .claude/skills/parallel-web

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Parallel Web in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Parallel Web on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Parallel Web is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.

Setup

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

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

If unable to install that way, use uv instead:

bash
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:

bash
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:

bash
parallel-cli login

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

Verify with:

bash
parallel-cli auth

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

Check task status

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

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

Get completed result

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

Present results in a clear, organized format.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Parallel Web AI skill do?

Why use Parallel Web on TypingMind?

Because you install it once and use it with any model. Parallel Web is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Parallel Web in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/dralkh/seerai/tree/main/skills/parallel-web. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Parallel Web?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Parallel Web?

As many as you like. As long as a model supports skills, you can use Parallel Web with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Parallel Web AI skill free?

Yes. It is published on GitHub by dralkh under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Create Agent Adapter

paperclipai

OrganizationPopular

Create or modify Paperclip agent adapters across server, UI, and CLI surfaces. Use when adding support for a new CLI agent, API agent, custom process, or adapter package.

Create Paperclip Bundled Skill

paperclipai

OrganizationPopular

Turn an idea, tweet, or task into a skill in the Paperclip skills catalog (packages/skills-catalog). Use when asked to FIND or MAKE a skill and publish it as a bundled/optional catalog skill: research…

Tavily

Tavily web search, content extraction, and research tools.

Deep Research

Multi-source deep research using firecrawl and exa MCPs. Searches the web, synthesizes findings, and delivers cited reports with source attribution. Use when the user wants thorough research on any…

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Parallel Web Toolkit

Routing — pick the right capability

Decision guide

Academic source priority

Context chaining

Setup

Check task status

Get completed result

Bundled files

Frequently asked questions

Related AI Skills

Set up your own AI workspace now