Dataify Google Hotels

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

Workflow

1. Parse the user's request into Dataify Google Hotels fields. Read references/google_hotels_api.md when the exact field list, accepted values, defaults, or mapping notes are needed.
2. Resolve relative dates from the conversation date, then pass dates as YYYY-MM-DD.

python3 scripts/google_hotels.py --params-json '{"q":"Tokyo hotels","check_in_date":"2026-06-01","check_out_date":"2026-06-03","gl":"us","hl":"en"}' --dry-run

5. If the token is missing, stop and tell the user to sign in at Dataify Dashboard to obtain DATAIFY_API_TOKEN.
6. After the user confirms the table, run the bundled Python script with python3. Run it from this skill directory, or use the absolute path to scripts/google_hotels.py.

Mapping Rules

• Always submit the API request as form data with UTF-8 encoding and Content-Type: application/x-www-form-urlencoded; charset=utf-8.
• Always force engine to google_hotels.
• Use documented defaults only. Do not treat examples, placeholders, or blank values in the API docs as defaults.
• Use json: "1" unless the user asks for another output format.
• Use q, check_in_date, and check_out_date for normal hotel searches when they can be inferred. Ask a follow-up if a normal search is missing any of those fields.
• Use property_token for hotel detail requests when the user provides a property token.
• Use next_page_token for next-page requests when the user provides a pagination token.
• Keep request values as strings unless the script accepts and normalizes a boolean.
• Normalize token values in the script. A token without Bearer is accepted and prefixed automatically.

Common mappings:

• "JSON" -> json: "1"
• "JSON+HTML" -> json: "2"
• "HTML" -> json: "3"
• "Light JSON" -> json: "4"
• country or region for Google behavior -> gl
• interface/search language -> hl
• lowest price / cheapest -> sort_by: "3"
• highest rating -> sort_by: "8"
• most reviewed -> sort_by: "13"
• rating 3.5+ / 4.0+ / 4.5+ -> rating: "7" / "8" / "9"
• free cancellation -> free_cancellation: "true"
• special offers -> special_offers: "true"
• eco certified -> eco_certified: "true"
• vacation rentals -> vacation_rentals: "true"
• bypass cache / no cache -> no_cache: "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

python3 scripts/google_hotels.py --params-json '{"q":"Tokyo hotels","check_in_date":"2026-06-01","check_out_date":"2026-06-03","gl":"us","hl":"en"}' --dry-run

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.