메뉴
이 저장소의 skills
SkillsMP는 zjunlp/DataMind에서 104개의 skill을 수집했습니다. skill을 열어 소스와 세부 정보를 확인하세요.
zjunlp/DataMind수집된 skill 104개 중 40개를 표시합니다.
Identifies the highest-cost scenario in payment processing fee analysis for the dabstep dataset. Use this skill when asked to find the most expensive MCC (Merchant Category Code) for a given transaction amount, or the most expensive ACI (Authorization…
원문 언어: 영어
Solve payment routing and cost optimization problems in the dabstep dataset. Use this skill for questions about which card scheme to steer merchant traffic to (for minimum or maximum fees), or which Authorization Characteristics Indicator (ACI) to incentivize…
원문 언어: 영어
Use this skill to calculate total payment processing fees for a merchant over a specified time period (e.g., a specific day, month, or year) in the dabstep dataset. Apply when asked: "What are the total fees that [merchant] paid in [period]?", "What is the…
원문 언어: 영어
Solve dabstep dataset questions about identifying applicable fee IDs from fees.json. Use this skill for any question asking which fee IDs apply to given conditions, a specific merchant on a specific day/month/year, or which merchants are affected by a…
원문 언어: 영어
Solve questions about estimating the average fee a card scheme would charge for a transaction. Use this skill for questions asking about average fees per card scheme (e.g., "what would be the average fee that GlobalCard would charge for a transaction value of…
원문 언어: 영어
Solve dabstep Average_Transaction_Value_Stats questions that ask for average transaction value (eur_amount) grouped by a dimension (shopper_interaction, issuing_country, acquirer_country, aci) for a specific merchant and card scheme over a date range. Use…
원문 언어: 영어
Answer questions about the dabstep payment processing dataset's metadata, schema, column definitions, and business rules governing fees. Use this skill whenever questions ask about column names/meanings, fee rule factors and their directions (e.g., "which…
원문 언어: 영어
Use this skill for dabstep payment-processing fee questions that involve simulating a change to a fee rule and computing the resulting impact. Trigger whenever a question asks: "what delta would <merchant> pay if the relative fee of fee ID=<X> changed to…
원문 언어: 영어
Skill for analyzing payment transaction fraud patterns and general macro-level statistics in the dabstep dataset. Use when questions involve: fraud rates by card scheme/merchant/year, percentage of fraudulent transactions, correlation between transaction…
원문 언어: 영어
Solves "highest cost scenario identification" questions in the dabstep payment processing dataset. Use this skill when the question asks about the most expensive MCC (Merchant Category Code) for a given transaction amount, or the most expensive ACI…
원문 언어: 영어
Solve payment routing and cost optimization problems on the dabstep dataset. Use this skill for questions like "which card scheme should merchant X steer traffic to for min/max fees?" or "which ACI should fraudulent transactions be moved to for lowest fees?"…
원문 언어: 영어
Calculates total payment processing fees for a merchant over a specified period (day, month, or full year) in the dabstep dataset. Use this skill whenever the question asks for "total fees" a merchant "paid" or "should pay," covering any time window. Involves…
원문 언어: 영어
Solve "Applicable Fee IDs" questions in the dabstep payment dataset. Use this skill whenever a question asks which fee IDs apply to a merchant, transaction, or combination of payment attributes (account_type, aci, card_scheme, capture_delay, intracountry,…
원문 언어: 영어
Solve dabstep questions that ask for average payment processing fees. Use this skill for: (1) computing the average fee a specific card scheme charges for a given transaction value, filtered by credit/debit type, account type, or MCC description; (2)…
원문 언어: 영어
Solve dabstep questions that ask for average transaction value (eur_amount) grouped by a categorical dimension (e.g., issuing_country, acquirer_country, aci, shopper_interaction, email_address), with optional filters for merchant, card_scheme, and date range…
원문 언어: 영어
Solve questions about the dabstep payment dataset's metadata, schema, column definitions, and business rules. Use this skill when answering questions about column names/meanings, fee structures, how factors affect fees (is_credit, intracountry,…
원문 언어: 영어
Solve dabstep questions about fee deltas from rate changes and fee impact simulations. Use for: (1) computing the monetary delta a merchant would pay if a fee's relative rate changed to a new value in a specific time period; (2) determining which merchants…
원문 언어: 영어
Solve fraud detection and macro-level transaction analysis questions on the dabstep payment dataset. Use this skill for any question involving the dabstep payments.csv data, including: fraud rate calculation, identifying highest/lowest transaction counts by…
원문 언어: 영어
Identify the most expensive MCC (Merchant Category Code) or ACI (Authorization Characteristics Indicator) for a given transaction scenario in the dabstep payment processing dataset. Use this skill when asked which MCC or ACI results in the highest fee for a…
원문 언어: 영어
Solve payment routing and cost optimization problems in the dabstep dataset. Use this skill when asked to determine the optimal card scheme or Authorization Characteristics Indicator (ACI) for a merchant to minimize or maximize fees, or when questions involve…
원문 언어: 영어
Solve dabstep questions that ask for the total payment processing fees a merchant should pay over a specific time period (a day, month, or year). Use this skill whenever the question involves computing total fees for a named merchant over a date range using…
원문 언어: 영어
Solve "Applicable Fee IDs" questions in the dabstep payment dataset. Use this skill whenever a question asks which fee IDs apply — whether to a merchant, transaction, or a combination of payment attributes (account_type, aci, card_scheme, capture_delay,…
원문 언어: 영어
Solve dabstep questions about fee deltas from rate changes and fee impact simulations. Use for: (1) computing the monetary delta a merchant would pay if a fee's relative rate changed to a new value in a specific time period; (2) determining which merchants…
원문 언어: 영어
Solve fraud detection and macro-level transaction analysis questions on the dabstep payment dataset. Use this skill for any question involving the dabstep payments.csv data, including: fraud rate calculation, identifying highest/lowest transaction counts by…
원문 언어: 영어
Solve payment routing and cost optimization problems in the dabstep dataset. Use this skill when asked to determine the optimal card scheme or Authorization Characteristics Indicator (ACI) for a merchant to minimize or maximize fees, or when questions involve…
원문 언어: 영어
Solve dabstep questions that ask for the total payment processing fees a merchant should pay over a specific time period (a day, month, or year). Use this skill whenever the question involves computing total fees for a named merchant over a date range using…
원문 언어: 영어
Analyze a company in an SEC 10-K SQLite database and produce high-quality evidence-grounded financial QA pairs. Use this whenever the user asks to analyze a company by CIK/ticker, inspect 10-K financial trends, generate finance QA datasets, or work with…
원문 언어: 영어
Analyze a company in an SEC 10-K SQLite database and produce high-quality evidence-grounded financial QA pairs. Use this whenever the user asks to analyze a company by CIK/ticker, inspect 10-K financial trends, generate finance QA datasets, or work with…
원문 언어: 영어
Analyze a company in an SEC 10-K SQLite database and produce high-quality evidence-grounded financial QA pairs. Use this whenever the user asks to analyze a company by CIK/ticker, inspect 10-K financial trends, generate finance QA datasets, or work with…
원문 언어: 영어
Analyze a company in an SEC 10-K SQLite database and produce high-quality evidence-grounded financial QA pairs. Use this whenever the user asks to analyze a company by CIK/ticker, inspect 10-K financial trends, generate finance QA datasets, or work with…
원문 언어: 영어
Analyze a company in an SEC 10-K SQLite database and produce high-quality evidence-grounded financial QA pairs. Use this whenever the user asks to analyze a company by CIK/ticker, inspect 10-K financial trends, generate finance QA datasets, or work with…
원문 언어: 영어
Analyze a company in an SEC 10-K SQLite database and produce high-quality evidence-grounded financial QA pairs. Use this whenever the user asks to analyze a company by CIK/ticker, inspect 10-K financial trends, generate finance QA datasets, or work with…
원문 언어: 영어
Analyze a company in an SEC 10-K SQLite database and produce high-quality evidence-grounded financial QA pairs. Use this whenever the user asks to analyze a company by CIK/ticker, inspect 10-K financial trends, generate finance QA datasets, or work with…
원문 언어: 영어
Analyze a specific participant's longitudinal passive-sensing and psychological data in the GLOBEM digital depression research dataset. Use this skill whenever the task involves: analyzing a user's mental health or behavioral data from wearables/smartphones,…
원문 언어: 영어
Analyze a specific participant's longitudinal passive-sensing and psychological data in the GLOBEM digital depression research dataset. Use this skill whenever the task involves: analyzing a user's mental health or behavioral data from wearables/smartphones,…
원문 언어: 영어
Analyze a specific participant's longitudinal passive-sensing and psychological data in the GLOBEM digital depression research dataset. Use this skill whenever the task involves: analyzing a user's mental health or behavioral data from wearables/smartphones,…
원문 언어: 영어
Analyze a specific participant's longitudinal passive-sensing and psychological data in the GLOBEM digital depression research dataset. Use this skill whenever the task involves: analyzing a user's mental health or behavioral data from wearables/smartphones,…
원문 언어: 영어
Analyze a specific participant's longitudinal passive-sensing and psychological data in the GLOBEM digital depression research dataset. Use this skill whenever the task involves: analyzing a user's mental health or behavioral data from wearables/smartphones,…
원문 언어: 영어
Analyze a specific participant's longitudinal passive-sensing and psychological data in the GLOBEM digital depression research dataset. Use this skill whenever the task involves: analyzing a user's mental health or behavioral data from wearables/smartphones,…
원문 언어: 영어
Analyze a specific participant's longitudinal passive-sensing and psychological data in the GLOBEM digital depression research dataset. Use this skill whenever the task involves: analyzing a user's mental health or behavioral data from wearables/smartphones,…
원문 언어: 영어