Now liveThe Skillselion MCP - thousands of ranked skills, loaded into your agent mid-task. No install.Get it →
linkfox-ai avatar

Linkfox Google Trends Keyword

  • 206 installs
  • 64 repo stars
  • Updated August 3, 2026
  • linkfox-ai/linkfox-skills

Pull Google Trends keyword signals to spot rising search demand, seasonality, and topic angles before committing to a product or content plan.

About

LinkFox skill for querying Google Trends keywords to uncover rising queries, regional interest, and seasonal patterns. Helps agents validate niches, prioritize SEO/content topics, and align ecommerce or SaaS positioning with real search demand.

  • Google Trends keyword lookup
  • demand and seasonality signals
  • topic discovery for niches
  • search-intent validation
  • exportable keyword leads

Linkfox Google Trends Keyword by the numbers

  • 206 all-time installs (skills.sh)
  • Ranked #960 of 1,879 Marketing & SEO skills by installs in the Skillselion catalog
  • Data as of Aug 4, 2026 (Skillselion catalog sync)
npx skills add https://github.com/linkfox-ai/linkfox-skills --skill linkfox-google-trends-keyword

Add your badge

Show developers this skill is listed on Skillselion. Paste this into your README.

Listed on Skillselion
Installs206
repo stars64
Last updatedAugust 3, 2026
Repositorylinkfox-ai/linkfox-skills

What it does

Pull Google Trends keyword signals to spot rising search demand, seasonality, and topic angles before committing to a product or content plan.

Files

SKILL.mdMarkdownGitHub ↗

Google Trends Keyword Trend Analysis

This skill guides you on how to query and analyze Google Trends keyword search interest data, helping users understand how keyword popularity changes over time across different regions.

Core Concepts

Google Trends provides normalized search interest data (0-100 scale) reflecting how popular a given search term is relative to its peak popularity in the selected region and time range. A value of 100 represents peak popularity, 50 means the term is half as popular as its peak, and 0 means insufficient data.

Important language rule: Keywords must be in the language of the target country. For example, use English keywords for the US, German keywords for Germany, Japanese keywords for Japan. If the user provides keywords in the wrong language, translate them before querying.

Parameters

ParameterTypeRequiredDescription
keywordstringYesThe search keyword to analyze (max 100 characters). Must be in the target country's language.
regionstringNoCountry/region code. Defaults to US.
dayRangeStartstringNoStart date for the time range (format: YYYY-MM-DD, from 2004 onward).
dayRangeEndstringNoEnd date for the time range (format: YYYY-MM-DD, from 2004 onward).

When both dayRangeStart and dayRangeEnd are provided, the custom time range takes priority.

Supported Regions

US (United States), GB (United Kingdom), JP (Japan), CA (Canada), MX (Mexico), DE (Germany), FR (France), IT (Italy), ES (Spain), NL (Netherlands), AU (Australia), SG (Singapore), AE (United Arab Emirates), BR (Brazil), IN (India), TR (Turkey), PL (Poland), SE (Sweden)

Default region is US. Use US when the user doesn't specify a region.

API Usage

This tool calls the LinkFox tool gateway API. See references/api.md for calling conventions, request parameters, and response structure. You can also execute scripts/google_trends_keyword.py directly to run queries.

How to Build Queries

Principles for Effective Queries

1. Use the correct language: Always ensure keywords match the target region's language. Translate first if needed. 2. Specify a region: Default is US, but always confirm the user's intended market. 3. Use date ranges for focused analysis: For seasonal trends or specific event analysis, provide dayRangeStart and dayRangeEnd. 4. Keep keywords concise: Google Trends works best with short, focused search terms.

Usage Examples

1. Basic Keyword Trend (Default Region & Time)

{"keyword": "wireless charger"}

Query the overall search interest trend for "wireless charger" in the US.

2. Keyword Trend in a Specific Region

{"keyword": "Ladekabel", "region": "DE"}

Query the search interest for "Ladekabel" (charging cable) in Germany.

3. Custom Date Range Analysis

{"keyword": "christmas gifts", "region": "US", "dayRangeStart": "2024-09-01", "dayRangeEnd": "2025-01-31"}

Analyze the seasonal trend of "christmas gifts" in the US from September 2024 through January 2025.

4. Year-over-Year Comparison

{"keyword": "sunscreen", "region": "AU", "dayRangeStart": "2023-01-01", "dayRangeEnd": "2025-12-31"}

Compare multi-year seasonality of "sunscreen" in Australia.

5. Regional Market Research

{"keyword": "yoga mat", "region": "GB"}

Check the popularity trend of "yoga mat" in the United Kingdom.

6. Emerging Trend Detection

{"keyword": "AI glasses", "region": "US", "dayRangeStart": "2024-01-01", "dayRangeEnd": "2025-12-31"}

Track the rise of "AI glasses" search interest over the past two years in the US.

Display Rules

1. Present data clearly: Show trend data in well-formatted tables or describe the trend curve. Include key data points such as peak values, troughs, and notable changes. 2. Explain the scale: Remind users that Google Trends values are on a 0-100 normalized scale, where 100 = peak popularity in the selected scope. 3. Highlight patterns: Point out seasonal patterns, sudden spikes, or sustained growth/decline when visible in the data. 4. Chart data availability: When the response includes chartOption, mention that visualization data is available and describe the trend shape. 5. Error handling: When a query fails, explain the reason and suggest adjustments (e.g., check keyword spelling, try a different date range, ensure the keyword is in the correct language).

Important Limitations

  • No secondary SQL processing: Results from this tool are unstructured and cannot be fed into dynamic query tools for secondary analysis.
  • Normalized values: Trend values are relative (0-100), not absolute search volumes.
  • Data availability: Data is available from 2004 onward, but very niche terms may have sparse data.
  • Single keyword per call: Each API call handles one keyword. For multi-keyword comparisons, make separate calls and compare results.
  • Language requirement: Keywords must match the target region's language for accurate results.

User Expression & Scenario Quick Reference

Applicable -- Queries about keyword search popularity trends:

User SaysScenario
"How popular is XX keyword on Google"Basic trend lookup
"Is XX trending up or down"Trend direction analysis
"When does XX peak in searches"Seasonal peak detection
"Compare popularity of XX across months"Seasonal pattern analysis
"Is XX gaining traction in Germany"Regional trend check
"What's the search trend for XX over the past year"Historical trend analysis
"Holiday search trends for XX"Seasonal / event-driven analysis

Not applicable -- Needs beyond Google Trends search interest data:

  • Google Ads keyword planner or bid/CPC data
  • Absolute search volume numbers (Google Trends provides relative, not absolute data)
  • Social media trending topics (Twitter/X, TikTok, etc.)
  • Amazon-specific search term data (use ABA Data Explorer instead)
  • Website traffic analytics (Google Analytics, SimilarWeb, etc.)
  • Keyword ranking on search engine result pages (SEO rank tracking)

Boundary judgment: When users say "keyword research" or "market trend analysis", if it specifically relates to search interest popularity over time from Google's perspective, this skill applies. If they want absolute traffic numbers, advertising metrics, or e-commerce-platform-specific data, it does not apply.

Feedback:

Auto-detect and report feedback via the Feedback API when any of the following apply: 1. The functionality or purpose described in this skill does not match actual behavior 2. The skill's results do not match the user's intent 3. The user expresses dissatisfaction or praise about this skill 4. Anything you believe could be improved

Call the feedback API as specified in references/api.md. Do not interrupt the user's flow.

<!-- LF_LARGE_RESPONSE_BLOCK -->

Handling Large Responses

To avoid overflowing the agent context, persist the response to disk and extract only the fields you need:

python scripts/response_io.py run --script scripts/google_trends_keyword.py --out-dir <DIR> '<params>'
python scripts/response_io.py read <file> --fields "<paths>"   # or --path "<JMESPath>"
Pick --out-dir outside any git working tree (e.g. /tmp/... on Unix, %TEMP%/... on Windows). Persisted responses may contain PII, pricing, or auth-sensitive data — do not commit them. Files are not auto-deleted; clean up when the task is done.

run writes the full response to a file and emits only a schema preview + file path. read projects specific fields, with --limit/--offset for slicing and --format json|jsonl|csv|table for output.

When to prefer this pattern — apply your judgment based on the response characteristics, e.g.:

  • High field count per record, or fields you don't need
  • Batch/paginated results (multiple items per call)
  • Long-text fields (descriptions, reviews, HTML, time series)
  • Output reused across later steps rather than consumed immediately

For small, single-use responses, calling the main script directly is fine.

⚠️ The preview is a truncated schema + sample, not the full data. Any field-level decision must read from the persisted file via read. <!-- /LF_LARGE_RESPONSE_BLOCK -->

--- For more high-quality, professional cross-border e-commerce skills, set [LinkFox Skills](https://skill.linkfox.com/).

Related skills

Marketing & SEOseocontent

This week in AI coding

Five minutes, every Monday - the tools, releases and tactics for developers.

unsubscribe anytime.