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Dataify Google AI Mode

核心功能是处理用户明确要求的AI模式Google搜索请求,转换为Dataify Scraper API提交,支持配置输出格式、地区等参数。典型场景为用户需要AI生成的搜索结果而非普通SERP。输入为搜索需求及可选参数,输出默认返回精简的标题、链接等,也可按需返回原始JSON或HTML。需绑定API token,缺失时引导登录获取,新用户享50免费额度。

dataify-server
dataify-server
热度 53.37
最近更新 2026-09-01 17:19:30
分类:搜索依赖:代码执行 / 第三方 API应用场景:API 集成 / 研究 / 数据处理

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add dataify-google-ai-mode
Metadata
name
dataify-google-ai-mode
description
Search with Google AI Mode when the user explicitly requests AI Mode or AI-generated Google search results. Do not use for ordinary Google SERP results.

Dataify Google AI Mode

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

Workflow

  1. Parse the user's request into Google AI Mode fields. Use q as the query and set engine to google_ai_mode.
  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 with only the fields the user requested plus required defaults. Use json: "1" unless the user asks for another output format.
  4. Run the bundled Python script with python3. Run it from this skill directory, or use the absolute path to scripts/google_ai_mode.py.
python3 scripts/google_ai_mode.py --q "pizza" --json 1

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

python3 scripts/google_ai_mode.py --params-json '{"q":"pizza","json":"1","gl":"us","hl":"en"}'

Field Mapping

Use references/google_ai_mode_api.md when you need the exact field list or examples.

Core rules:

  • Always submit the API request as form data with Content-Type: application/x-www-form-urlencoded.
  • Always force engine to google_ai_mode.
  • Keep request values as strings unless the script accepts and normalizes a boolean.
  • Omit optional fields that the user did not request.
  • Ask a follow-up only when the required query q cannot be inferred.
  • If both location and uule are present, prefer the explicit uule and omit location.
  • 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"
  • search origin location -> location
  • Google encoded location -> uule
  • bypass cache / no cache -> no_cache: "true"
  • use cache -> no_cache: "false"
  • country or region for Google behavior -> gl
  • interface/search language -> hl

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_ai_mode.py --q "pizza" --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.