Results for “data”

Compare your data to a partner's data in the marketplace. Given a dataset you already own with person/edge data, this skill walks you through picking a partner data source to match against, choosing which identifier types to match on, optionally selecting which enrichment attributes to attach, and then submits the report — returning overlap, match counts, and demographic coverage. Use when: "how does my data compare to your marketplace", "compare my data to [partner]", "how much overlap do I have with [supplier]", "run a match report", "match my customers against 3P data", "see what enrichment is available for my dataset", or any open-ended question about marketplace overlap. (narrative-identity)

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End-to-end data pipeline for empirical research: fetch economic data from APIs (FRED, World Bank, IMF, BLS, OECD, Yahoo Finance), clean and transform raw data, construct strategy-specific variables, and validate panel structure. Use when asked to fetch data, download data, clean data, merge datasets, prepare analysis-ready data.

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Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why.

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Create a Databricks materialization to stream Estuary collections into Databricks tables via Unity Catalog. Use when setting up Databricks as a destination for captured data. Use when user says "send to Databricks", "materialize to Databricks", "Databricks destination", or "load into Databricks".

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Plan and execute database migrations, data transformations, and system migrations safely with rollback strategies and data integrity validation. Use when migrating databases, transforming data schemas, moving between database systems, implementing versioned migrations, handling data transformations, ensuring data integrity, or planning zero-downtime migrations.

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Expert data engineer for ETL/ELT pipelines, streaming, data warehousing. Activate on: data pipeline, ETL, ELT, data warehouse, Spark, Kafka, Airflow, dbt, data modeling, star schema, streaming data, batch processing, data quality. NOT for: API design (use api-architect), ML training (use ML skills), dashboards (use design skills).

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Create, edit, and clean spreadsheet files, convert table formats, and add formulas, formatting, or charts. The workflow includes recalculating formulas and checking errors before delivering a spreadsheet that updates when its inputs change.

• Clean messy CSV data into a spreadsheet
• Add formulas and charts to an Excel workbook
• Create a spreadsheet template for data entry

Turn CSV, Excel, or JSON data into a visual report page with key metrics, multiple charts, a data table, and written observations. The report uses supplied data and includes its source, reporting period, and methodology.

• Turn growth data into a visual weekly report
• Present key metrics and year-over-year changes
• Share charts alongside source data and methodology

Check article pages for structured data, machine-readable details that help search engines understand content. Review required properties, add Article metadata in JSON-LD format, and verify the rendered page and HTTP response for specific implementation gaps.

• Checking structured data on blog articles
• Adding Article metadata in JSON-LD format
• Finding missing headlines, images, dates, or authors

Review database migration files before merging changes. It gathers evidence about what the changes do, rollback limits, database locks, and risks from large data volumes, then reports blockers when evidence needed for a decision is missing.

• Review database changes before merging
• Check rollback limits
• Assess database locks and data-volume risks

Create, edit, and clean spreadsheet files, including Excel workbooks and CSV data. Add formulas, formatting, and charts, preserve existing workbook conventions, and recalculate formulas with error checks before delivering the spreadsheet.

• Clean messy CSV data into a spreadsheet
• Edit an Excel workbook while preserving its formatting
• Create a financial model with recalculating formulas

Helps plan and implement database structure changes and data transformations using Sequelize, TypeORM, or Prisma. Includes staged changes intended to avoid downtime, plus transaction and backup examples for rolling back failed migrations.

• Renaming database columns in stages
• Splitting stored addresses into separate fields
• Preparing rollback steps for failed migrations

Design dbt data transformations with clearly organized models, data quality tests, documentation, and incremental processing that updates only part of a dataset. Use it to structure analytics tables and organize a dbt project.

• Organizing a dbt project into model layers
• Adding data quality tests
• Designing incremental updates for large tables

Analyzes one .NET project to plan integration tests for an Oracle-to-PostgreSQL migration. Identifies database access methods, prioritizes migration risks, and documents test cases, seed data, and coverage, including empty strings, NULL values, and timezone behavior.

• Identifying database access methods that need integration tests
• Prioritizing tests for Oracle-specific behavior
• Planning checks for empty strings, NULL values, and timezones

Prepare raw credit data for pre-loan modeling by checking data quality and screening variables. Review missing values, predictive usefulness, stability, noise, and correlation, then export an Excel report with organization-level statistics and cleaning results.

• Prepare credit data before building a model
• Compare missing values and bad-sample rates across organizations
• Screen variables for usefulness, stability, and redundancy

Investigate suspected data theft from AWS S3 by comparing access logs, GuardDuty findings, and Macie classifications. Identify unusual bulk downloads and transfers across accounts, assess sensitive data exposure, and build automated detection rules.

• Investigating unusual bulk downloads from S3
• Checking transfers to external AWS accounts
• Assessing exposure of sensitive data

Analyze endpoint, cloud storage, and email logs for signs of insider data theft. Use pandas to compare upload volumes with normal user behavior and examine data loss prevention alerts, unusual file access, and activity outside working hours.

• Investigating suspected insider data theft
• Finding users with unusually large uploads
• Reviewing file access outside working hours

Check training data and machine learning models for malicious changes, including backdoors that cause unexpected behavior on specific inputs. Combine data quality checks, model analysis, and file integrity verification, then document findings and retest after remediation.

• Screening third-party data before training
• Checking downloaded model weights before deployment
• Investigating unexpected behavior on specific inputs

Work with BigQuery to create datasets and tables, run SQL queries, ingest data, and manage processing jobs. The skill also covers connections to BigQuery ML and Gemini for machine learning and AI analysis.

• Creating datasets and tables
• Analyzing data with SQL queries
• Ingesting data and managing processing jobs

Design a database layer from business requirements, access patterns, and expected growth. Compare database technologies, define tables and relationships, and plan indexes, caching, scaling, migrations, backups, and recovery with clear reasons and trade-offs.

• Choosing a database for a new service
• Designing tables, relationships, and indexes
• Planning database migrations and rollback procedures

Build document search and AI retrieval workflows with Chroma, a database for numerical representations of text. Cover document storage, meaning-based search, metadata filters, persistence, and connections to LangChain and LlamaIndex.

• Building document search based on meaning
• Adding document retrieval to AI answers
• Filtering search results by document attributes

Retrieve locally stored Chinese A-share prices, financial statements, and valuation context through the project's shared interfaces. Look up technical indicators, inspect available data fields, and check data dates when results disagree.

• Checking a stock's prices and valuation context
• Retrieving financial data across reporting periods
• Looking up technical indicators and their data dates

Run nf-core workflows on local sequencing files or public GEO/SRA datasets to analyze gene expression, genetic variants, or chromatin accessibility. The skill guides environment checks, sample table preparation, workflow configuration, execution, and output verification.

• Analyze gene expression from RNA-seq data
• Identify genetic variants in WGS/WES data
• Analyze chromatin accessibility from ATAC-seq data

Check single-cell RNA-seq data quality, filter low-quality cells using median absolute deviation, a measure of spread, and compare plots before and after filtering. Outputs include filtered data and the original dataset with quality annotations.

• Assess single-cell RNA-seq data quality
• Filter low-quality cells and rarely detected genes
• Compare quality plots before and after filtering