Dataify Google Scholar#

Use this skill to turn a user's Google Scholar request into a Dataify Scraper API call.

Workflow#

1. Parse the user's request into Dataify Google Scholar fields. Always set engine to google_scholar.
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 documented defaults. Defaults must come only from parameter descriptions in references/google_scholar_api.md; never use example values as defaults.
4. Run the bundled Python script with python3. Run it from this skill directory, or use the absolute path to scripts/google_scholar.py.

Preview the complete parameter table:

bash

python3 scripts/google_scholar.py --request "搜索 large language model，2020 到 2024，返回 20 条" --preview

Call the API after the user confirms:

bash

python3 scripts/google_scholar.py --q "large language model" --as_ylo 2020 --as_yhi 2024 --num 20

For many fields, pass one JSON object with shell-appropriate quoting:

bash

python3 scripts/google_scholar.py --params-json '{"q":"large language model","as_ylo":"2020","as_yhi":"2024","num":"20"}'

Field Mapping#

Use references/google_scholar_api.md when you need the exact field list, defaults, or accepted values.

Core rules:

• Always submit the API request as form data with Content-Type: application/x-www-form-urlencoded.
• Always force engine to google_scholar.
• Keep request values as strings unless the script accepts and normalizes a boolean.
• Include documented defaults when the user did not specify a field.
• Omit optional fields that have no documented default and no user value.
• Ask a follow-up only when no usable search condition can be inferred. A usable search condition is q, cites, or cluster.
• Do not combine cluster with q or cites; cluster must be used by itself.
• 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"
• interface/search language -> hl
• language-restricted results -> lr, formatted like lang_fr or lang_fr|lang_de
• page number N -> start: String((N - 1) * 10)
• result count -> num, range 1 to 20
• cited-by search -> cites
• all versions search -> cluster
• year range lower bound -> as_ylo
• year range upper bound -> as_yhi
• past-year/date sort -> scisbd: "1" for abstracts only or scisbd: "2" for all content
• include patents -> as_sdt: "7"
• exclude patents -> as_sdt: "0"
• US case law -> as_sdt: "4"
• safe search on/off -> safe: "active" or safe: "off"
• disable similar/omitted result filter -> filter: "0"
• exclude citations -> as_vis: "1"
• include citations -> as_vis: "0"
• review articles only -> as_rr: "1"
• bypass 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#

bash

python3 scripts/google_scholar.py --help

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.