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Parallel Web Search

Organization
parallel-web
parallel-web-search

CLI-backed web search. Use when the user explicitly invokes this skill, needs CLI-only controls or saved JSON output, or no Parallel web_search MCP tool is available. When the bundled Parallel Search MCP is available, prefer its web_search tool for ordinary lookups and current-information queries. Only use parallel-deep-research if the user explicitly requests deep or exhaustive research.

Overview

Publisherparallel-web
Repositoryparallel-agent-skills
Skill nameparallel-web-search
Stars
77
Forks
10
Bundled files
Instructions only
LicenseMIT
Links
  • 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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by parallel-web on GitHub. Read the source before you install it.

Installation

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

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/parallel-web/parallel-agent-skills/tree/main/skills/parallel-web-search
  4. 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.
2

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/parallel-web/parallel-agent-skills.git /tmp/parallel-agent-skills
mkdir -p .claude/skills
cp -r /tmp/parallel-agent-skills/skills/parallel-web-search .claude/skills/parallel-web-search
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Parallel Web Search 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 Search 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 Search 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.

Web Search

Search the web for: $ARGUMENTS

Command

Choose a short, descriptive filename based on the query (e.g., ai-chip-news, react-vs-vue). Use lowercase with hyphens, no spaces. Substitute it into the command inline — $FILENAME and <keyword> below are placeholders, not shell variables; do not copy them verbatim.

bash
parallel-cli search "$ARGUMENTS" -q "<keyword1>" -q "<keyword2>" --json --max-results 10 --excerpt-max-chars-total 27000 -o "/tmp/$FILENAME.json"

Concrete example for a query about React 19:

bash
parallel-cli search "latest React 19 features and adoption" -q "React 19" -q "concurrent rendering" --json --max-results 10 --excerpt-max-chars-total 27000 -o "/tmp/react-19-features.json"

The first argument is the objective — a natural language description of what you're looking for. It replaces multiple keyword searches with a single call for broad or complex queries. Add -q flags for specific keyword queries to supplement the objective. The -o flag saves the full results to a JSON file for follow-up questions.

Options if needed:

  • --after-date YYYY-MM-DD for time-sensitive queries
  • --include-domains domain1.com,domain2.com to limit to specific sources
  • --exclude-domains domain.com to filter out noisy sources
  • --mode turbo for simple fact lookups where speed matters most; supports English and Japanese queries
  • --mode fast for high-quality search within an approximately one-second latency budget; requires CLI ≥ 0.9.2, and latency is not guaranteed
  • --mode advanced for harder questions (multi-step, agentic search). Keep the default basic unless the request needs another mode
  • --location us (ISO 3166-1 alpha-2) for geo-targeted results
  • --session-id "<returned-session-id>" to group related Search/Extract calls when a prior response returned one. A session_id or search_id is not a Task interaction ID or research run ID; never send it to research status/poll or --previous-interaction-id

Parsing results

Read the saved -o JSON file as the authoritative payload. Result and excerpt limits bound requested content, but stdout can still exceed the tool's output limit. Truncated stdout is not parseable JSON and is not proof of incomplete saved results. Inspect an existing output path before using it because Search overwrites that file. For each result, extract:

  • title, url, and publish_date if provided; omit unknown dates
  • Useful content from excerpts (skip navigation noise like menus, footers, "Skip to content")

Check the exit status, returned API error and warnings before presenting results. On an error or empty results, report what happened and do not fabricate an answer. An old output file is not evidence that a failed request succeeded. For sparse results, state the coverage limits; refine the objective or queries only when useful for the user's request.

Response format

CRITICAL: Every claim must have an inline citation. Use markdown links like Title pulling only from the JSON output. Never invent or guess URLs.

Synthesize a response that:

  • Leads with the key answer/finding
  • Includes specific facts, names, numbers, dates
  • Cites every fact inline as Source Title — do not leave any claim uncited
  • Organizes by theme if multiple topics

End with a Sources section listing every URL referenced:

text
Sources:
- [Source Title](https://example.com/article) (Feb 2026)
- [Another Source](https://example.com/other) (Jan 2026)

This Sources section is mandatory. Do not omit it.

Only include source dates that were returned or verified in the retrieved content. Leave the date out when unknown.

After the Sources section, mention the output file path (/tmp/$FILENAME.json) so the user knows it's available for follow-up questions.

Setup

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

bash
/parallel:parallel-cli-setup

If a documented command or option is missing, check the installed version and upgrade through its installation method: standalone parallel-cli update, pipx pipx upgrade parallel-web-tools, uv uv tool upgrade parallel-web-tools, Homebrew brew upgrade parallel-web/tap/parallel-cli, or npm npm update -g parallel-web-cli. Verify help in the same terminal before retrying.

For authentication errors, inspect parallel-cli auth --json and its authenticated boolean; exit zero alone does not prove authentication. A 403 can indicate permissions, policy or billing. Report the actual error; check balance only for a billing-specific failure and never add funds without explicit confirmation.

Frequently asked questions

What does the Parallel Web Search AI skill do?

CLI-backed web search. Use when the user explicitly invokes this skill, needs CLI-only controls or saved JSON output, or no Parallel web_search MCP tool is available. When the bundled Parallel Search MCP is available, prefer its web_search tool for ordinary lookups and current-information queries. Only use parallel-deep-research if the user explicitly requests deep or exhaustive research.

Why use Parallel Web Search on TypingMind?

Because you install it once and use it with any model. Parallel Web Search 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 Search in TypingMind?

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

Which AI models can use Parallel Web Search?

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 Search?

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

Is the Parallel Web Search AI skill free?

Yes. It is published on GitHub by parallel-web 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.

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