Web Search logo

Web Search

OrganizationPopular
TokenRhythm
web-search

This skill should be used when users need to search the web for information, find current content, look up news articles, search for images, or find videos. It uses DuckDuckGo's search API to return results in clean, formatted output (text, markdown, or JSON). Use for research, fact-checking, finding recent information, or gathering web resources.

Overview

PublisherTokenRhythm
Repositoryopensquilla
Skill nameweb-search
Stars
7K
Forks
566
Bundled files
3
LicenseApache-2.0
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.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by TokenRhythm on GitHub. Read the source before you install it.

Installation

Install the 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.
  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/TokenRhythm/opensquilla.git /tmp/opensquilla
mkdir -p .claude/skills
cp -r /tmp/opensquilla/src/opensquilla/skills/bundled/web-search .claude/skills/web-search
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 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 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 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

Overview

Search the web using DuckDuckGo's API to find information across web pages, news articles, images, and videos. Returns results in multiple formats (text, markdown, JSON) with filtering options for time range, region, and safe search.

When to Use This Skill

Use this skill when users request:

  • Web searches for information or resources
  • Finding current or recent information online
  • Looking up news articles about specific topics
  • Searching for images by description or topic
  • Finding videos on specific subjects
  • Research requiring current web data
  • Fact-checking or verification using web sources
  • Gathering URLs and resources on a topic

Prerequisites

OpenSquilla declares the required duckduckgo-search Python package as a runtime dependency. For source checkouts, run the normal project dependency sync before invoking this bundled skill. The library provides a simple Python interface to DuckDuckGo's search API without requiring API keys or authentication.

Core Capabilities

1. Basic Web Search

Search for web pages and information:

bash
python {baseDir}/scripts/search.py "<query>"

Example:

bash
python {baseDir}/scripts/search.py "python asyncio tutorial"

Returns the top 10 web results with titles, URLs, and descriptions in a clean text format.

2. Limiting Results

Control the number of results returned:

bash
python {baseDir}/scripts/search.py "<query>" --max-results <N>

Example:

bash
python {baseDir}/scripts/search.py "machine learning frameworks" --max-results 20

Useful for:

  • Getting more comprehensive results (increase limit)
  • Quick lookups with fewer results (decrease limit)
  • Balancing detail vs. processing time

3. Time Range Filtering

Filter results by recency:

bash
python {baseDir}/scripts/search.py "<query>" --time-range <d|w|m|y>

Time range options:

  • d - Past day
  • w - Past week
  • m - Past month
  • y - Past year

Example:

bash
python {baseDir}/scripts/search.py "artificial intelligence news" --time-range w

Great for:

  • Finding recent news or updates
  • Filtering out outdated content
  • Tracking recent developments

4. News Search

Search specifically for news articles:

bash
python {baseDir}/scripts/search.py "<query>" --type news

Example:

bash
python {baseDir}/scripts/search.py "climate change" --type news --time-range w --max-results 15

News results include:

  • Article title
  • Source publication
  • Publication date
  • URL
  • Article summary/description

5. Image Search

Search for images:

bash
python {baseDir}/scripts/search.py "<query>" --type images

Example:

bash
python {baseDir}/scripts/search.py "sunset over mountains" --type images --max-results 20

Image filtering options:

Size filters:

bash
python {baseDir}/scripts/search.py "landscape photos" --type images --image-size Large

Options: Small, Medium, Large, Wallpaper

Color filters:

bash
python {baseDir}/scripts/search.py "abstract art" --type images --image-color Blue

Options: color, Monochrome, Red, Orange, Yellow, Green, Blue, Purple, Pink, Brown, Black, Gray, Teal, White

Type filters:

bash
python {baseDir}/scripts/search.py "icons" --type images --image-type transparent

Options: photo, clipart, gif, transparent, line

Layout filters:

bash
python {baseDir}/scripts/search.py "wallpapers" --type images --image-layout Wide

Options: Square, Tall, Wide

Image results include:

  • Image title
  • Image URL (direct link to image)
  • Thumbnail URL
  • Source website
  • Dimensions (width x height)

6. Video Search

Search for videos:

bash
python {baseDir}/scripts/search.py "<query>" --type videos

Example:

bash
python {baseDir}/scripts/search.py "python tutorial" --type videos --max-results 15

Video filtering options:

Duration filters:

bash
python {baseDir}/scripts/search.py "cooking recipes" --type videos --video-duration short

Options: short, medium, long

Resolution filters:

bash
python {baseDir}/scripts/search.py "documentary" --type videos --video-resolution high

Options: high, standard

Video results include:

  • Video title
  • Publisher/channel
  • Duration
  • Publication date
  • Video URL
  • Description

7. Region-Specific Search

Search with region-specific results:

bash
python {baseDir}/scripts/search.py "<query>" --region <region-code>

Common region codes:

  • us-en - United States (English)
  • uk-en - United Kingdom (English)
  • ca-en - Canada (English)
  • au-en - Australia (English)
  • de-de - Germany (German)
  • fr-fr - France (French)
  • wt-wt - Worldwide (default)

Example:

bash
python {baseDir}/scripts/search.py "local news" --region us-en --type news

8. Safe Search Control

Control safe search filtering:

bash
python {baseDir}/scripts/search.py "<query>" --safe-search <on|moderate|off>

Options:

  • on - Strict filtering
  • moderate - Balanced filtering (default)
  • off - No filtering

Example:

bash
python {baseDir}/scripts/search.py "medical information" --safe-search on

9. Output Formats

Choose how results are formatted:

Text format (default):

bash
python {baseDir}/scripts/search.py "quantum computing"

Clean, readable plain text with numbered results.

Markdown format:

bash
python {baseDir}/scripts/search.py "quantum computing" --format markdown

Formatted markdown with headers, bold text, and links.

JSON format:

bash
python {baseDir}/scripts/search.py "quantum computing" --format json

Structured JSON data for programmatic processing.

10. Saving Results to File

Save search results to a file:

bash
python {baseDir}/scripts/search.py "<query>" --output <file-path>

Example:

bash
python {baseDir}/scripts/search.py "artificial intelligence" --output ai_results.txt
python {baseDir}/scripts/search.py "AI news" --type news --format markdown --output ai_news.md
python {baseDir}/scripts/search.py "AI research" --format json --output ai_data.json

The file format is determined by the --format flag, not the file extension.

Output Format Examples

Text Format

1. Page Title Here
   URL: https://example.com/page
   Brief description of the page content...

2. Another Result
   URL: https://example.com/another
   Another description...

Markdown Format

markdown
## 1. Page Title Here

**URL:** https://example.com/page

Brief description of the page content...

## 2. Another Result

**URL:** https://example.com/another

Another description...

JSON Format

json
[
  {
    "title": "Page Title Here",
    "href": "https://example.com/page",
    "body": "Brief description of the page content..."
  },
  {
    "title": "Another Result",
    "href": "https://example.com/another",
    "body": "Another description..."
  }
]

Common Usage Patterns

Research on a Topic

Gather comprehensive information about a subject:

bash
# Get overview from web
python {baseDir}/scripts/search.py "machine learning basics" --max-results 15 --output ml_web.txt

# Get recent news
python {baseDir}/scripts/search.py "machine learning" --type news --time-range m --output ml_news.txt

# Find tutorial videos
python {baseDir}/scripts/search.py "machine learning tutorial" --type videos --max-results 10 --output ml_videos.txt

Current Events Monitoring

Track news on specific topics:

bash
python {baseDir}/scripts/search.py "climate summit" --type news --time-range d --format markdown --output daily_climate_news.md

Finding Visual Resources

Search for images with specific criteria:

bash
python {baseDir}/scripts/search.py "data visualization examples" --type images --image-type photo --image-size Large --max-results 25 --output viz_images.txt

Fact-Checking

Verify information with recent sources:

bash
python {baseDir}/scripts/search.py "specific claim to verify" --time-range w --max-results 20

Academic Research

Find resources on scholarly topics:

bash
python {baseDir}/scripts/search.py "quantum entanglement research" --time-range y --max-results 30 --output quantum_research.txt

Market Research

Gather information about products or companies:

bash
python {baseDir}/scripts/search.py "electric vehicle market 2025" --max-results 20 --format markdown --output ev_market.md
python {baseDir}/scripts/search.py "EV news" --type news --time-range m --output ev_news.txt

Implementation Approach

When users request web searches:

  1. Identify search intent:

    • What type of content (web, news, images, videos)?
    • How recent should results be?
    • How many results are needed?
    • Any filtering requirements?
  2. Configure search parameters:

    • Choose appropriate search type (--type)
    • Set time range if currency matters (--time-range)
    • Adjust result count (--max-results)
    • Apply filters (image size, video duration, etc.)
  3. Select output format:

    • Text for quick reading
    • Markdown for documentation
    • JSON for further processing
  4. Execute search:

    • Run the search command
    • Save to file if results need to be preserved
    • Print to stdout for immediate review
  5. Process results:

    • Read saved files if needed
    • Extract URLs or specific information
    • Combine results from multiple searches

Quick Reference

Command structure:

bash
python {baseDir}/scripts/search.py "<query>" [options]

Essential options:

  • -t, --type - Search type (web, news, images, videos)
  • -n, --max-results - Maximum results (default: 10)
  • --time-range - Time filter (d, w, m, y)
  • -r, --region - Region code (e.g., us-en, uk-en)
  • --safe-search - Safe search level (on, moderate, off)
  • -f, --format - Output format (text, markdown, json)
  • -o, --output - Save to file

Image-specific options:

  • --image-size - Size filter (Small, Medium, Large, Wallpaper)
  • --image-color - Color filter
  • --image-type - Type filter (photo, clipart, gif, transparent, line)
  • --image-layout - Layout filter (Square, Tall, Wide)

Video-specific options:

  • --video-duration - Duration filter (short, medium, long)
  • --video-resolution - Resolution filter (high, standard)

Get full help:

bash
python {baseDir}/scripts/search.py --help

Best Practices

  1. Be specific - Use clear, specific search queries for better results
  2. Use time filters - Apply --time-range for current information
  3. Adjust result count - Start with 10-20 results, increase if needed
  4. Save important searches - Use --output to preserve results
  5. Choose appropriate type - Use news search for current events, web for general info
  6. Use JSON for automation - JSON format is easiest to parse programmatically
  7. Respect usage - Don't hammer the API with rapid repeated searches

Troubleshooting

Common issues:

  • "Missing required dependency": Sync or reinstall the OpenSquilla project dependencies
  • No results found: Try broader search terms or remove time filters
  • Timeout errors: The search service may be temporarily unavailable; retry after a moment
  • Rate limiting: Space out searches if making many requests
  • Unexpected results: DuckDuckGo's results may differ from Google; try refining the query

Limitations:

  • Results quality depends on DuckDuckGo's index and algorithms
  • No advanced search operators (unlike Google's site:, filetype:, etc.)
  • Image and video searches may have fewer results than web search
  • No control over result ranking or relevance scoring
  • Some specialized searches may work better on dedicated search engines

Advanced Use Cases

Combining Multiple Searches

Gather comprehensive information by combining search types:

bash
# Web overview
python {baseDir}/scripts/search.py "topic" --max-results 15 --output topic_web.txt

# Recent news
python {baseDir}/scripts/search.py "topic" --type news --time-range w --output topic_news.txt

# Images
python {baseDir}/scripts/search.py "topic" --type images --max-results 20 --output topic_images.txt

Programmatic Processing

Use JSON output for automated processing:

bash
python {baseDir}/scripts/search.py "research topic" --format json --output results.json
# Then process with another script
python analyze_results.py results.json

Building a Knowledge Base

Create searchable documentation from web results:

bash
# Search multiple related topics
python {baseDir}/scripts/search.py "topic1" --format markdown --output kb/topic1.md
python {baseDir}/scripts/search.py "topic2" --format markdown --output kb/topic2.md
python {baseDir}/scripts/search.py "topic3" --format markdown --output kb/topic3.md

Resources

scripts/search.py

The main search tool implementing DuckDuckGo search functionality. Key features:

  • Multiple search types - Web, news, images, and videos
  • Flexible filtering - Time range, region, safe search, and type-specific filters
  • Multiple output formats - Text, Markdown, and JSON
  • File output - Save results for later processing
  • Clean formatting - Human-readable output with all essential information
  • Error handling - Graceful handling of network errors and empty results

The script can be executed directly and includes comprehensive command-line help via --help.

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 Web Search AI skill do?

This skill should be used when users need to search the web for information, find current content, look up news articles, search for images, or find videos. It uses DuckDuckGo's search API to return results in clean, formatted output (text, markdown, or JSON). Use for research, fact-checking, finding recent information, or gathering web resources.

Why use Web Search on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/TokenRhythm/opensquilla/tree/main/src/opensquilla/skills/bundled/web-search. 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 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 Web Search?

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

Is the Web Search AI skill free?

Yes. It is published on GitHub by TokenRhythm under the Apache-2.0 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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