Dataify Google Trends

Search Google Trends for trend data

Use this skill to turn a user's Google Trends request into a Dataify Scraper API form submission.

Workflow

1. Parse the user's request into Google Trends fields. Use q as the query and set engine to google_trends.
2. If the token is missing, stop and tell the user to sign in at Dataify Dashboard to obtain DATAIFY_API_TOKEN.
3. Build request parameters from the user's request plus required API defaults. Do not use example values as defaults. In particular, do not default q to pizza; ask the user for a query if it cannot be inferred.

bash

python3 scripts/google_trends.py --q "AI" --json 1

For many fields, pass one JSON object with shell-appropriate quoting. The script still submits form data to the API:

bash

python3 scripts/google_trends.py --params-json '{"q":"AI","json":"1","hl":"en","geo":"United+States","data_type":"TIMESERIES"}'

To generate the required pre-call parameter table from the normalized request without calling the API:

bash

python3 scripts/google_trends.py --request "search Google Trends for AI in the United States, English, timeseries" --preview-table

Return the final script output directly to the user. Do not summarize, extract, clean, translate, or reshape the API response.

Field Mapping

Use references/google_trends_api.md when you need the exact field list, defaults, values, or parameter descriptions.

Core rules:

• Always submit the API request as form data with Content-Type: application/x-www-form-urlencoded.
• Always force engine to google_trends.
• Keep request values as strings unless the script accepts and normalizes a boolean.
• Ask a follow-up only when required q cannot be inferred.
• Normalize token values in the script. A token without Bearer is accepted and prefixed automatically.
• Use UTF-8 for all files, script output, and request encoding.

Common mappings:

• "JSON" -> json: "1"
• "JSON+HTML" -> json: "2"
• "HTML" -> json: "3"
• "Light JSON" -> json: "4"
• bypass cache / no cache / skip cache -> no_cache: "true"
• use cache -> no_cache: "false"
• language / interface language -> hl
• country, region, or Google Trends location -> geo
• subregion level / city / DMA / country-region breakdown -> region
• time trend / interest over time -> data_type: "TIMESERIES"
• regional comparison -> data_type: "GEO_MAP"
• regional interest distribution -> data_type: "GEO_MAP_0"
• related topics -> data_type: "RELATED_TOPICS"
• related queries -> data_type: "RELATED_QUERIES"
• timezone offset in minutes -> tz
• category -> cat
• image search -> gprop: "images"
• news search -> gprop: "news"
• Google Shopping -> gprop: "froogle"
• YouTube Search -> gprop: "youtube"
• date range, "past 12 months", "today 5-y", or other Google Trends date expression -> date
• CSV results -> csv: "true"
• include low search volume regions -> include_low_search_volume: "true"

Result presentation

• Return a compact, user-facing result by default: the most relevant titles, links, and vertical-specific fields, plus a count or truncation note when useful.
• Do not expose transport details, fixed engine fields, task plumbing, or the full response envelope in the ordinary flow.
• Return raw JSON or HTML only when the user explicitly requests raw output.
• Preserve source links and distinguish missing fields from empty values; do not invent data.

Quick Start

bash

python3 scripts/google_trends.py --q "AI" --json 1

Parameter interaction policy

• For a clear, low-risk, read-only, and low-cost request, apply safe defaults and execute immediately. A short execution summary is optional; do not pause for confirmation.
• Ask only for a missing required input, a material ambiguity, a high-volume or multi-page scope, a media download, a choice that materially changes credit usage, an irreversible action, or an explicit user request to review parameters.
• When confirmation is required, show only user-facing values that affect the target, scope, output, or cost. Prefer one concise sentence; use a compact table only when three or more consequential values are easier to compare.
• Never show fixed fields, empty optional fields, unchanged defaults, credentials, or internal implementation parameters such as engine selectors, response-format flags, offsets, spider IDs, and file-name templates.
• Keep advanced filters hidden unless the user asks for them or they are needed to resolve ambiguity. Never substitute documentation example values for missing required user input.
• After returning results, offer relevant refinements instead of forcing all optional decisions before the first result.

Account CTA policy

• Show a prominent Dataify account CTA only when the API token is missing, rejected/invalid, or the account has insufficient credits.
• For a missing token, offer https://dashboard.dataify.com/login?utm_source=skill and state: New accounts receive 50 free credits. Never ask the user to paste the token into chat.
• Detect the current operating system and shell. Show only the matching session-scoped setup command first (export for macOS/Linux shells, $env: for Windows PowerShell, or set for Windows Command Prompt). Show other platforms or persistent setup only when detection is ambiguous or the user asks.
• After the user says the token is configured, verify only whether DATAIFY_API_TOKEN is present; never print its value. If verification succeeds, continue the original task without asking the user to repeat it.
• Explain that persistent shell changes may require a new terminal or restarting the agent application. Do not recommend a project .env unless the execution path explicitly loads it, and ensure .env is ignored by version control.
• For an invalid token, direct the user to API-key management without implying that a new registration is required. For insufficient credits, direct the user to balance or recharge management.
• During normal submission, processing, and successful completion, do not promote registration or the Dashboard. Never expose the token or include it in CTA attribution parameters.