Dataify Google Lens

Use this skill to turn a user's Google Lens or reverse-image-search request into a Dataify Scraper API form submission.

Workflow

Parse the user's request into Google Lens fields. Use url for the image URL, set engine to google_lens, and infer optional fields only when the user asks for them.
Build request parameters from the user's request. If the user did not specify a field, use only the documented default from the parameter description:
- engine: google_lens
- json: 1
- type: all
- no_cache: false Fields with no documented default stay unset. Do not treat examples such as us, en, active, or true as defaults.

python3 scripts/google_lens.py --url "https://example.com/image.jpg" --json 1 --type all --country us --preview

Ask the user: 请确认是否需要修改这些参数；确认无误后我再调用接口。

If the token is missing, stop and tell the user to sign in at Dataify Dashboard to obtain DATAIFY_API_TOKEN.
Run the bundled Python script with python3. Run it from this skill directory, or use the absolute path to scripts/google_lens.py. The script submits form data to the hardcoded API endpoint; it does not send a JSON body.

python3 scripts/google_lens.py --url "https://example.com/image.jpg" --json 1 --type all --country us

Natural-language fallback is available when useful:

python3 scripts/google_lens.py --request "Search Google Lens for https://example.com/image.jpg, products, country US, safe on, no cache"

Use references/google_lens_api.md when you need the exact field list, defaults, constraints, or examples.

Core rules:

Always submit the API request as form data with Content-Type: application/x-www-form-urlencoded.
Always encode request data as UTF-8.
Always force engine to google_lens.
Keep request values as strings unless the script accepts and normalizes a boolean.
Use documented defaults when the user does not specify a value. Omit fields that have no documented default and were not requested.
Ask a follow-up only when the required image url cannot be inferred from the user's request.
Normalize token values in the script. A token without Bearer is accepted and prefixed automatically.
Never include Authorization in the preview table, and never print the token value in the final explanation.

Common mappings:

Image URL, picture URL, reverse image search target -> url
"JSON" -> json: "1"
"JSON+HTML" -> json: "2"
"HTML" -> json: "3"
"Light JSON" -> json: "4"
interface/search language -> hl
country or region for Lens behavior -> country
all results -> type: "all"
product results -> type: "products"
about this image -> type: "about_this_image"
exact matches -> type: "exact_matches"
visual matches or similar images -> type: "visual_matches"
extra query/keyword/refinement used with all, visual_matches, or products -> q
safe search on/off -> safe: "active" or safe: "off"
bypass cache -> no_cache: "true"

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.

python3 scripts/google_lens.py --help

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.

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.