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Multi Search Engine

OrganizationPopular
TokenRhythm
multi-search-engine

Query scholarly metadata and the web through Crossref, Brave, Tavily, and DuckDuckGo with a single CLI surface. Trigger when the user asks for research search, fact lookup, source discovery, or engine comparison. Results retain DOI, publication year, and authors when supplied by Crossref, then deduplicate by DOI, arXiv ID, or normalized URL. API-key engines gate themselves on the relevant environment variable; Crossref and DuckDuckGo need no key.

Overview

PublisherTokenRhythm
Repositoryopensquilla
Skill namemulti-search-engine
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 Multi Search Engine 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/multi-search-engine .claude/skills/multi-search-engine
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Multi Search Engine 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 Multi Search Engine 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 Multi Search Engine 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.

multi-search-engine

A unified CLI for querying several web search engines in parallel and returning a normalized result list. Built on httpx and beautifulsoup4 (both already in OpenSquilla default dependencies, so no extra install beyond pip install opensquilla).

Use cases

  • Building a deep-research round with diverse engine coverage
  • Fact-check a claim against >1 engine
  • Compare scholarly metadata coverage with general web results
  • Find citable publication metadata without requiring an API key

Limitations

  • A single engine sufficient → call its API directly instead
  • Need headless-browser DOM rendering → this skill is HTTP-only

Quick start

bash
python {baseDir}/scripts/search.py \
    --query "openclaw skill registry" \
    --engines crossref,duckduckgo,brave \
    --limit 10 \
    --json

Output:

json
{
  "query": "...",
  "results": [
    {
      "engine": "crossref",
      "title": "...",
      "url": "https://...",
      "snippet": "...",
      "rank": 1,
      "doi": "10.1234/example",
      "year": 2024,
      "authors": ["Ada Example", "Edge Research Consortium"],
      "corporate_authors": ["Edge Research Consortium"]
    }
  ],
  "errors": [
    {"engine": "brave", "reason": "BRAVE_SEARCH_API_KEY/BRAVE_API_KEY not set; skipping"}
  ]
}

doi, year, authors, and corporate_authors are optional. They are emitted only when an engine supplies verifiable values, so the existing five-field web-result shape remains compatible for general engines. The parallel corporate-author list lets BibTeX consumers protect institution names from person-name parsing.

Engines

EngineNeeds keyKey env varStrength
crossrefnooptional CROSSREF_MAILTOScholarly works with canonical DOI, year, and author metadata
duckduckgonoNo-key, privacy-oriented broad web baseline
braveyesBRAVE_SEARCH_API_KEY or legacy BRAVE_API_KEYHigh-quality results, generous free tier
tavilyyesTAVILY_API_KEYDesigned for AI agents, returns clean JSON

The script never errors out when an API-key engine's key is missing — it records a per-engine errors entry and continues with the rest. Pass --strict to fail fast when any requested engine is unavailable.

Routing tips

The host should pick engines by language and availability:

  • Academic queries → crossref first, then brave or tavily for broader context
  • General web queries → duckduckgo plus brave or tavily for triangulation
  • Time-sensitive (last 24h) → brave (recency filter) or tavily
  • Long-tail academic → start with crossref; supplement with direct arXiv when needed

engines.md has the full per-engine guidance.

Boundaries

  • HTTP-only. JS-rendered pages will not be readable; use a headless-browser skill if needed.
  • DuckDuckGo scraping is best-effort — HTML structure changes can break it. The script logs parse failures individually and keeps the run going.
  • Timeout, HTTP 429, and transient HTTP 5xx responses receive at most two retries with bounded backoff. Repeated failures remain per-engine soft errors. Callers must still avoid high-rate loops.
  • Captcha-protected results are not bypassed. If an engine returns a challenge page, the parser will return zero results for that engine and log a warning.

Crossref is a public metadata service, not a full-text search index. The client sends query.bibliographic, respects the requested result limit, and uses canonical https://doi.org/<doi> URLs. Set CROSSREF_MAILTO to identify your application through Crossref's polite-pool convention. See the Crossref REST API etiquette and rate-limit documentation.

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 Multi Search Engine AI skill do?

Query scholarly metadata and the web through Crossref, Brave, Tavily, and DuckDuckGo with a single CLI surface. Trigger when the user asks for research search, fact lookup, source discovery, or engine comparison. Results retain DOI, publication year, and authors when supplied by Crossref, then deduplicate by DOI, arXiv ID, or normalized URL. API-key engines gate themselves on the relevant environment variable; Crossref and DuckDuckGo need no key.

Why use Multi Search Engine on TypingMind?

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

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

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 Multi Search Engine?

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

Is the Multi Search Engine 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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