Search logo

Search

Organization
brightdata
search

Search the web via the Bright Data CLI — `bdata search` for Google/Bing/Yandex SERP, `bdata discover` for intent-ranked semantic results. Use when the user wants SERP results, needs URLs to feed into scraping, or wants semantic web discovery with optional page content. Hands off to `scrape` once target URLs are chosen, and to `data-feeds` when the user wants structured data from a known platform. Requires the Bright Data CLI; proactively guides install + login if missing.

Overview

Publisherbrightdata
Repositoryskills
Skill namesearch
Stars
268
Forks
36
Bundled files
3
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.

  • 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 brightdata on GitHub. Read the source before you install it.

Installation

Install the 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/brightdata/skills/tree/main/skills/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/brightdata/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/search .claude/skills/search
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 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 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 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.

Bright Data — Search

Find things on the web. Two commands live in this skill:

  • bdata search — classic keyword SERP (Google/Bing/Yandex). Best when you want "what ranks for keyword X."
  • bdata discover — AI intent-ranked discovery with optional page content. Best when you want "pages about topic Y that match intent Z."

For structured data from a known platform (Amazon, LinkedIn, TikTok, …), stop and use data-feeds instead.

Setup gate (run first)

bash
if ! command -v bdata >/dev/null 2>&1; then
    echo "bdata CLI not installed — see bright-data-best-practices/references/cli-setup.md"
elif ! bdata zones >/dev/null 2>&1; then
    echo "bdata not authenticated — run: bdata login  (or: bdata login --device for SSH)"
fi

Halt and route to skills/bright-data-best-practices/references/cli-setup.md if either check fails.

Pick your path

SituationAction
Single keyword query, just SERPbdata search "<query>" --engine google --json --pretty
Paginated SERP (more results)loop --page 0, --page 1, … (0-indexed)
Multiple queriesshell loop over a queries file
Intent-ranked / semantic (not keyword)bdata discover "<query>" --intent "<intent>" --num-results 20
Want page bodies along with results, one passbdata discover ... --include-content
News / images / shopping SERPbdata search "<query>" --type news (or images, shopping)
Want Amazon/LinkedIn/TikTok/… structured datastop — hand off to data-feeds
Have URLs, want contenthand off to scrape

Action

Core commands:

bash
# Google SERP, structured JSON
bdata search "site:example.com privacy policy" --engine google --json --pretty

# Localized Bing (German results, German language)
bdata search "datenschutz" --engine bing --country de --language de --json

# Second page of results (0-indexed)
bdata search "machine learning papers" --page 1 --json

# Mobile SERP (rankings differ from desktop)
bdata search "best coffee shops" --device mobile --json

# News vertical
bdata search "openai" --type news --json --pretty

# Intent-ranked discovery
bdata discover "enterprise LLM platforms" \
    --intent "vendor pages with pricing" \
    --num-results 15 --json

# Discovery with page content in markdown
bdata discover "webhook best practices" \
    --include-content --num-results 10 -o results.json

# Date-filtered discovery
bdata discover "react server components" \
    --start-date 2025-01-01 --end-date 2025-12-31 --num-results 20

Full flag reference: references/flags.md.

search vs discover — pick the right one

You wantUse
"What Google ranks for this exact keyword"search
"Pages that match this meaning/intent"discover
"News / images / shopping vertical SERP"search --type <vertical>
"Results + page bodies in one call"discover --include-content
"Dedup / semantic ranking across queries"discover

Verification gate

  1. JSON parses cleanly: jq . <output> returns 0.
  2. Result array non-empty — if empty, the query is legitimately zero-result; relax the query and re-run. Don't claim success on empty results without telling the user.
  3. Required fields present:
    • search: results live at .organic[]; each has title + link
    • discover: results live at .results[]; each has title + link; if --include-content, also content
  4. For discover --include-content: no block-page signatures in the content field (same list as scrape, case-insensitive):
    • Access Denied
    • Just a moment
    • Attention Required
    • Checking your browser
    • captcha
    • cf-browser-verification
    • cloudflare (with < 2KB total body)
  5. Geo sanity: if the user expected country-specific results, inspect TLDs / languages of top results. If mis-localized, re-run with explicit --country and --language.

Red flags

  • Using search to fetch content from Amazon, LinkedIn, TikTok, etc. when data-feeds returns clean structured data in one call.
  • Scraping every SERP result blindly — filter first (domain allowlist, keyword in title, relevance heuristic).
  • Confusing search (keyword) with discover (semantic). They answer different questions.
  • Running multiple queries without deduping URLs across result sets before scraping.
  • Assuming SERP order is universal — it's personalized by geo + device. Always set --country and --device explicitly for reproducibility.
  • Using --page as a result count — it's a page index, not a limit. Each page returns ~10 results.
  • Assuming SERP results are at .results[] — for bdata search they live at .organic[]. (Discover uses .results[].)
  • Hardcoding --num-results 100 on discover without realizing the pipeline polls until that many are found; can be slow.

References

  • references/flags.md — full flags for search and discover with when-to-use notes.
  • references/patterns.md — multi-query dedup, SERP → filter → scrape pipeline, search vs discover decision, legacy curl fallback, shared verification checklist.
  • references/examples.md — (1) single Google query, (2) localized Bing, (3) batch queries + dedup into URL list, (4) discover --include-content end-to-end.

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 Search AI skill do?

Search the web via the Bright Data CLI — `bdata search` for Google/Bing/Yandex SERP, `bdata discover` for intent-ranked semantic results. Use when the user wants SERP results, needs URLs to feed into scraping, or wants semantic web discovery with optional page content. Hands off to `scrape` once target URLs are chosen, and to `data-feeds` when the user wants structured data from a known platform. Requires the Bright Data CLI; proactively guides install + login if missing.

Why use Search on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/brightdata/skills/tree/main/skills/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 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 Search?

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

Is the Search AI skill free?

Yes. It is published on GitHub by brightdata 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

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇