Total 55,338 skills, Data Processing has 2830 skills
Showing 12 of 2830 skills
Who is this wallet and what have they been doing? Identity labels, balance, PnL summary, recent transactions, perp positions, and counterparties.
Design KPI dashboards for executive monitoring. Use for performance tracking, strategic initiatives, and management reporting.
Use this skill when building data pipelines, ETL/ELT workflows, or data transformation layers. Triggers on Airflow DAG design, dbt model creation, Spark job optimization, streaming vs batch architecture decisions, data ingestion, data quality checks, pipeline orchestration, incremental loads, CDC (change data capture), schema evolution, and data warehouse modeling. Acts as a senior data engineer advisor for building reliable, scalable data infrastructure.
Run a comprehensive data quality assessment and produce a scorecard across 6 dimensions: completeness, uniqueness, consistency, timeliness, accuracy, validity. Use when the user asks about data quality, mentions data issues, wants to audit a table, is onboarding a new data source, or needs to validate pipeline output.
Aggregate and deduplicate recent news from multiple sources into concise topic summaries.
Salesforce Data Cloud Harmonize phase. TRIGGER when: user works with DMOs, mappings, relationships, identity resolution, unified profiles, data graphs, or universal IDs. DO NOT TRIGGER when: the task is only about streams/DLOs (use sf-datacloud-prepare), segments/insights (use sf-datacloud-segment), retrieval/search (use sf-datacloud-retrieve), or STDM/session tracing (use sf-ai-agentforce-observability).
Use this skill when the user wants to add or update metadata in DataHub: descriptions, tags, glossary terms, ownership, deprecation, domains, data products, structured properties, documents, or field-level metadata. Triggers on: "add tag to X", "update description for X", "set owner of X", "add glossary term", "deprecate X", "create a domain", "create a glossary term", "add a document", or any request to modify DataHub metadata.
Generates a Jupyter notebook that transforms datasets between ML schemas for model training or evaluation. Use when the user says "transform", "convert", "reformat", "change the format", or when a dataset's schema needs to change to match the target format — always use this skill for format changes rather than writing inline transformation code. Supports OpenAI chat, SageMaker SFT/DPO/RLVR, HuggingFace preference, Bedrock Nova, VERL, and custom JSONL formats from local files or S3.
Build and deploy new Goldsky Turbo pipelines from scratch. Triggers on: 'build a pipeline', 'index X on Y chain', 'set up a pipeline', 'track transfers to postgres', or any request describing data to move from a chain/contract to a destination (postgres, clickhouse, kafka, s3, webhook). Covers the full workflow: requirements → dataset selection → YAML generation → validation → deploy. Not for debugging (use /turbo-doctor) or syntax lookups (use /turbo-pipelines).
Transform, filter, reshape, join, and manipulate football data. Use when the user needs to clean data, merge datasets, convert between formats, handle missing values, work with large datasets, or do any data manipulation task on football data.
Calculate derived football metrics and models. Use when the user wants to compute xG, xGOT, PPDA, passing networks, expected threat, possession value, pressing intensity, or any derived football statistic from raw data.
Smart CSV importer with format auto-detection. Handles major banks in Canada and US, plus payment platforms (Stripe, PayPal, Wise, WeChat Pay, Alipay) and browser-assisted exports gathered through `/cfo-statement-export`. Use when importing bank or credit card CSV exports. CLEAR step: C (Capture)