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search AI Agent Skill

Quellcode ansehen: brightdata/skills

Critical

Installation

npx skills add brightdata/skills --skill search

7.0K

Installationen

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)

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

Situation Action
Single keyword query, just SERP bdata search "<query>" --engine google --json --pretty
Paginated SERP (more results) loop --page 0, --page 1, … (0-indexed)
Multiple queries shell 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 pass bdata discover ... --include-content
News / images / shopping SERP bdata search "<query>" --type news (or images, shopping)
Want Amazon/LinkedIn/TikTok/… structured data stop — hand off to data-feeds
Have URLs, want content hand off to scrape

Action

Core commands:

# 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 want Use
"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.

Installationen

Installationen 7.0K
Globales Ranking #336 von 600

Sicherheitsprüfung

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socket Safe
Warnungen: 0 Bewertung: 90
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So verwenden Sie diesen Skill

1

Install search by running npx skills add brightdata/skills --skill search in your project directory. Führen Sie den obigen Installationsbefehl in Ihrem Projektverzeichnis aus. Die Skill-Datei wird von GitHub heruntergeladen und in Ihrem Projekt platziert.

2

Keine Konfiguration erforderlich. Ihr KI-Agent (Claude Code, Cursor, Windsurf usw.) erkennt installierte Skills automatisch und nutzt sie als Kontext bei der Code-Generierung.

3

Der Skill verbessert das Verständnis Ihres Agenten für search, und hilft ihm, etablierte Muster zu befolgen, häufige Fehler zu vermeiden und produktionsreifen Code zu erzeugen.

Was Sie erhalten

Skills sind Klartext-Anweisungsdateien — kein ausführbarer Code. Sie kodieren Expertenwissen über Frameworks, Sprachen oder Tools, das Ihr KI-Agent liest, um seine Ausgabe zu verbessern. Das bedeutet null Laufzeit-Overhead, keine Abhängigkeitskonflikte und volle Transparenz: Sie können jede Anweisung vor der Installation lesen und prüfen.

Kompatibilität

Dieser Skill funktioniert mit jedem KI-Coding-Agenten, der das skills.sh-Format unterstützt, einschließlich Claude Code (Anthropic), Cursor, Windsurf, Cline, Aider und anderen Tools, die projektbezogene Kontextdateien lesen. Skills sind auf Transportebene framework-agnostisch — der Inhalt bestimmt, für welche Sprache oder welches Framework er gilt.

Data sourced from the skills.sh registry and GitHub. Install counts and security audits are updated regularly.

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