Total 54,149 skills, Data Processing has 2770 skills
Showing 12 of 2770 skills
Track workouts, stats, progress over time. Identify improvement areas, plateaus, rest/recovery needs, peak performance timing, injury risk.
Deep dive into LookML includes, refinements (layering), and project structure best practices. Essential for mastering Looker's object-oriented capabilities.
All-in-one Assistant for Data Analysis and Office Productivity. Covers end-to-end workflows including data processing, analytical insights, report writing, PPT creation, and data visualization. Always approach from an expert perspective and think one step ahead for users. Proactively confirm with users when encountering uncertain issues. Supported features: Excel data analysis, campaign data review, ROI calculation, data visualization, report generation, PPT creation, formula generation. Use this skill when users mention terms like "analyze data", "create report", "make PPT", "Excel", "campaign analysis", "ROI", "review", "weekly report", "monthly report", "data processing", "chart", "visualization", "presentation", "spreadsheet", "formula".
Fetch recent posts from one or more X/Twitter accounts through twitterapi.io, output structured JSON/CSV records, optionally sync records to Feishu/Lark Bitable through feishu-cli, and optionally guide recurring execution through OpenClaw, Codex automations, cron, or launchd. Use when the user wants to monitor X bloggers, collect recent tweets, export tweet metrics, append tweets to Feishu Bitable, or set up a scheduled Twitter/X account tracking workflow.
Piwik Pro integration. Manage data, records, and automate workflows. Use when the user wants to interact with Piwik Pro data.
Use SNOMED CT's semantic attribute relationships to answer clinical questions. Finds concepts by relationship attribute (finding site, causative agent, associated morphology, procedure site), navigates the IS-A hierarchy, and composes property-filtered ValueSets. Use when the user asks things like "all disorders of the heart", "all procedures on the kidney", "all conditions caused by bacteria", "subtypes of hypertension", "symptoms of X", "complications of X", or any query that involves clinical relationships between concepts rather than simple text search.
Map a code from any clinical terminology (ICD-10-CM, LOINC, RxNorm) to its SNOMED CT equivalent in order to unlock SNOMED's rich semantic attribute relationships. Use when the user has a code in a non-SNOMED system and wants to explore related concepts, find clinically adjacent codes, understand the semantic meaning, or build a SNOMED-based ValueSet from a non-SNOMED starting point. Always propose this when a user asks about relationships or "what is related to X" and the code is not already in SNOMED.
Retrieve ESG ratings and scores using Octagon MCP. Use when analyzing Environmental, Social, and Governance ratings, MSCI ESG ratings, Sustainalytics risk ratings, industry ESG rankings, and sustainability metrics for any public company.
Retrieve sector P/E ratios using Octagon MCP. Use when comparing company valuations to sector benchmarks, analyzing sector valuations across exchanges, and understanding market-wide valuation trends.
Use when working with Lightdash YAML files, dbt models with Lightdash metadata, the lightdash CLI (deploy, upload, download, preview, lint, warehouse-catalog, sql, set-warehouse), or managing charts, dashboards, spaces and access, AI agents, scheduled content, users, groups, custom roles, metrics, and dimensions as code
Guides an end-to-end data-warehouse migration to Amazon Redshift — discovery, schema/SQL/stored-procedure/macro/script conversion, data migration, validation, performance comparison, and reporting. Source-routed via `references/<source>/`; Teradata (Vantage) is the supported source; additional sources are added as their own `references/<source>/` sets. Text-only knowledge (no executable code) — the AI generates all execution at runtime. Applies when a user wants to migrate Teradata to Amazon Redshift, convert Teradata DDL/SQL/stored procedures/macros/BTEQ to Redshift/RSQL, or assess Teradata-to-Redshift migration complexity. Applies only to migrations targeting Amazon Redshift; migrations to other platforms (Snowflake, BigQuery, Databricks, etc.) are out of scope regardless of source. Does not cover general Redshift administration, performance tuning, or troubleshooting of existing Redshift clusters (no migration involved), or sources not listed under references/.
Use this skill when spreadsheet files are the primary input or output. This means the user wants to: open, read, edit, or repair existing .xlsx, .xlsm, .csv, or .tsv files (e.g., add columns, calculate formulas, format, create charts, clean messy data); create new spreadsheets from scratch or from other data sources; or convert between spreadsheet file formats. Trigger this especially when the user references a spreadsheet file by name or path—even casually (such as "the xlsx in my downloads")—and wants to process it or generate content from it. It's also used to clean or reorganize messy tabular data files (rows with incorrect formatting, misaligned headers, garbage data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do not trigger this when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.