web-search

brave/brave-search-skills · updated Apr 8, 2026

MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.

$npx skills add https://github.com/brave/brave-search-skills --skill web-search
0 commentsdiscussion
summary

Web search with ranked results, snippets, and support for freshness filters, SafeSearch, and custom ranking via Goggles.

  • Returns structured results across multiple types: web pages, news, videos, discussions, FAQ, infobox, and locations in a single response
  • Supports freshness filtering (past day/week/month/year or custom date ranges), SafeSearch levels, and location-aware results via headers
  • Goggles enable custom result ranking: boost trusted sources, suppress spam, or build focused
skill.md

Web Search

Requires API Key: Get one at https://api.search.brave.com

Plan: Included in the Search plan. See https://api-dashboard.search.brave.com/app/subscriptions/subscribe

Quick Start (cURL)

Basic Search

curl -s "https://api.search.brave.com/res/v1/web/search?q=python+web+frameworks" \
  -H "Accept: application/json" \
  -H "X-Subscription-Token: ${BRAVE_SEARCH_API_KEY}"

With Parameters

curl -s "https://api.search.brave.com/res/v1/web/search" \
  -H "Accept: application/json" \
  -H "X-Subscription-Token: ${BRAVE_SEARCH_API_KEY}" \
  -G \
  --data-urlencode "q=rust programming tutorials" \
  --data-urlencode "country=US" \
  --data-urlencode "search_lang=en" \
  --data-urlencode "count=10" \
  --data-urlencode "safesearch=moderate" \
  --data-urlencode "freshness=pm"

Endpoint

GET https://api.search.brave.com/res/v1/web/search
POST https://api.search.brave.com/res/v1/web/search

Note: Both GET and POST methods are supported. POST is useful for long queries or complex Goggles.

Authentication: X-Subscription-Token: <API_KEY> header

Optional Headers:

  • Accept-Encoding: gzip — Enable gzip compression

When to Use Web Search

Feature Web Search (this) LLM Context (llm-context) Answers (answers)
Output Structured results (links, snippets, metadata) Pre-extracted page content for LLMs End-to-end AI answers with citations
Result types Web, news, videos, discussions, FAQ, infobox, locations, rich Extracted text chunks, tables, code Synthesized answer + source list
Unique features Goggles, structured data (schemas), rich callbacks Token budget control, threshold modes Multi-iteration search, streaming, OpenAI SDK compatible
Speed Fast (~0.5-1s) Fast (<1s) Slower (~30-180s)
Best for Search UIs, data extraction, custom ranking RAG pipelines, AI agents, grounding Chat interfaces, thorough research

Parameters

Parameter Type Required Default Description
q string Yes - Search query (1-400 chars, max 50 words)
country string No US Search country (2-letter country code or ALL)
search_lang string No en Language preference (2+ char language code)
ui_lang string No en-US UI language (e.g., "en-US")
count int No 20 Max results per page (1-20)
offset int No 0 Page offset for pagination (0-9)
safesearch string No moderate Adult content filter (off/moderate/strict)
freshness string No - Time filter (pd/pw/pm/py or date range)
text_decorations bool No true Include highlight markers
spellcheck bool No true Auto-correct query
result_filter string No - Filter result types (comma-separated)
goggles string No - Custom ranking filter (URL or inline)
extra_snippets bool No - Get up to 5 extra snippets per result
operators bool No true Apply search operators
units string No - Measurement units (metric/imperial)
enable_rich_callback bool No false Enable rich 3rd party data callback
include_fetch_metadata bool No false Include fetched_content_timestamp on results

Freshness Values

Value Description
pd Past day (24 hours)
pw Past week (7 days)
pm Past month (31 days)
py Past year (365 days)
YYYY-MM-DDtoYYYY-MM-DD Custom date range

Result Filter Values

Filter types: discussions, faq, infobox, news, query, videos, web, locations

# Only web and video results
curl "...&result_filter=web,videos"

Location Headers (Optional)

For location-aware results, add these headers. Lat/Long is sufficient when coordinates are known — the other headers are only needed as a fallback when coordinates are unavailable.

Header Type Description
X-Loc-Lat float User latitude (-90.0 to 90.0)
X-Loc-Long float User longitude (-180.0 to 180.0)
X-Loc-Timezone string IANA timezone (e.g., "America/San_Francisco")
X-Loc-City string City name
X-Loc-State string State/region code (ISO 3166-2)
X-Loc-State-Name string State/region full name (e.g., "California")
X-Loc-Country string 2-letter country code
X-Loc-Postal-Code string Postal code (e.g., "94105")

Priority: X-Loc-Lat + X-Loc-Long take precedence. When provided, downstream services resolve the location directly from coordinates and the text-based headers (City, State, Country, Postal-Code) are not used for location resolution. Provide text-based headers only when you don't have coordinates. Sending both won't break anything — lat/long simply wins.

Response Format

Response Fields

Field Type Description
type string Always "search"
query.original string The original search query
query.altered string? Spellcheck-corrected query (if changed)
query.cleaned string? Cleaned/normalized query
query.spellcheck_off bool? Whether spellcheck was disabled
query.more_results_available bool Whether more pages exist
query.show_strict_warning bool? True if strict safesearch blocked adult results
query.search_operators object? Applied search operators (applied, cleaned_query, sites)
web.type string Always "search"
web.results[].title string Page title
web.results[].url string Page URL
web.results[].description string? Snippet/description text
web.results[].age string? Human-readable age (e.g., "2 days ago")
web.results[].language string? Content language code
web.results[].meta_url object URL components (scheme, netloc, hostname, path)
web.results[].thumbnail object? Thumbnail (src, original)
web.results[].thumbnail.original string? Original full-size image URL
web.results[].thumbnail.logo bool? Whether the thumbnail is a logo
web.results[].profile object? Publisher identity (name, url, long_name, img)
web.results[].page_age string? ISO datetime of publication (e.g., "2025-04-12T14:22:41")
web.results[].extra_snippets list[str]? Up to 5 additional excerpts
web.results[].deep_results object? Additional links (buttons, links) from the page
web.results[].schemas list? Raw schema.org structured data
web.results[].product object? Product info and reviews
web.results[].recipe object? Recipe details (ingredients, time, ratings)
web.results[].article object? Article metadata (author, publisher, date)
web.results[].book object? Book info (author, ISBN, rating)
web.results[].software object? Software product info
web.results[].rating object? Aggregate ratings
web.results[].faq object? FAQ found on the page
web.results[].movie object? Movie info (directors, actors, genre)
web.results[].video object? Video metadata (duration, views, creator)
web.results[].location object? Location/restaurant details
web.results[].qa object? Question/answer info
web.results[].creative_work object? Creative work data
web.results[].music_recording object? Music/song data
web.results[].organization object? Organization info
web.results[].review object? Review data
web.results[].content_type string? Content type classification
web.results[].fetched_content_timestamp int? Fetch timestamp (with include_fetch_metadata=true)
web.mutated_by_goggles bool Whether results were re-ranked by Goggles
web.family_friendly bool Whether results are family-friendly
mixed object? Preferred display order (see Mixed Response below)
discussions.results[] array? Forum discussion clusters
discussions.results[].data.forum_name string? Forum/community name
discussions.results[].data.num_answers int? Number of answers/replies
discussions.results[].data.question string? Discussion question
discussions.results[].data.top_comment string? Top-voted comment excerpt
faq.results[] array? FAQ entries
news.results[] array? News articles
videos.results[] array? Video results
infobox.results[] array? Knowledge graph entries
locations.results[] array? Local POI results
rich.hint.vertical string? Rich result type
rich.hint.callback_key string? Callback key for rich data

JSON Example

{
  "type": "search",
  "query": {
    "original": "python frameworks",
    "altered": "python web frameworks",
    "spellcheck_off": false,
    "more_results_available": true
  },
  "web": {
    "type": "search",
    "results": [
      {
        "title": "Top Python Web Frameworks",
        "url": "https://example.com/python-frameworks",
        "description": "A comprehensive guide to Python web frameworks...",
        "age": "2 days ago",
        "language": "en",
        "meta_url": {
          "scheme": "https",
          "netloc": "example.com",
          "hostname": "example.com",
          "path": "/python-frameworks"
        },
        "thumbnail": {
          "src": "https://...",
          "original": "https://original-image-url.com/img.jpg"
        },
        "extra_snippets": ["Additional excerpt 1...", "Additional excerpt 2..."]
      }
    ],
    "family_friendly": true
  },
  "mixed": {
    "type": "mixed",
    "main": [
      {"type": "web", "index": 0, "all": false},
      {"type": "web", "index": 1, "all": false},
      {"type"
how to use web-search

How to use web-search on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add web-search
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/brave/brave-search-skills --skill web-search

The skills CLI fetches web-search from GitHub repository brave/brave-search-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/web-search

Reload or restart Cursor to activate web-search. Access the skill through slash commands (e.g., /web-search) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.664 reviews
  • Zara Jain· Dec 28, 2024

    Registry listing for web-search matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Kaira Harris· Dec 16, 2024

    Solid pick for teams standardizing on skills: web-search is focused, and the summary matches what you get after install.

  • Pratham Ware· Dec 12, 2024

    Solid pick for teams standardizing on skills: web-search is focused, and the summary matches what you get after install.

  • Mei Wang· Dec 12, 2024

    We added web-search from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Advait Nasser· Dec 8, 2024

    web-search reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Mei Li· Dec 4, 2024

    web-search has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Luis Smith· Dec 4, 2024

    Keeps context tight: web-search is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Chen Abebe· Nov 27, 2024

    I recommend web-search for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Arjun Khan· Nov 23, 2024

    web-search fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Chinedu Gonzalez· Nov 19, 2024

    Useful defaults in web-search — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

showing 1-10 of 64

1 / 7