Results for “data-augmentation”

For analysis/task/review agents using a Professional Skill on batch, stream, warehouse, lineage, replay, or data quality; not for transactions without distributed-data impact.

Designs and implements big data architectures, processes large-scale datasets with distributed systems, and optimizes data pipelines for throughput using Hadoop, Spark, and cloud platforms.

Scale pandas and NumPy workflows to larger-than-memory datasets using parallel and distributed computing.

Builds a scheduled, AI-powered data collection agent that scrapes public sources, enriches results with Gemini Flash, and stores them in Notion, Sheets, or Supabase.

Cleans datasets, detects anomalies, generates reports, and creates visualizations using pandas, scikit-learn, and plotting libraries to turn raw data into client-ready deliverables.

Migrates data from MongoDB, PostgreSQL, MySQL, Neo4j, Kafka, and JSONL into SurrealDB with full and incremental CDC synchronization.

Your data-fill agent. Connect a contact-enrichment tool (contact-level) or a company-enrichment tool (company-level), or both for the full waterfall, to fill gaps on any list. Given a name plus a company, find email, phone, LinkedIn, and title. Given a domain, find headcount, funding, technographic profile, and hiring intent. Trigger on "enrich these contacts with emails", "find LinkedIn URLs for {list}", "get phone numbers for {contacts}", "who are the decision makers at {company}", "complete this prospect list", "enrich this CSV", "get the technographic stack for {company}", "fill in missing data on {list}", or any data-completion, list-enrichment, or contact-lookup question.

Automates multi-source enrichment of IPs, domains, URLs, and file hashes using VirusTotal, AbuseIPDB, Shodan, GreyNoise, URLScan.io, and MISP to provide contextual risk scoring and disposition recommendations for SOC analysts.

Analiza datasets con Pandas y NumPy: explora distribuciones, correlaciones y patrones, y aplica tests de hipótesis para extraer conocimiento no obvio.

Use this skill when the user uploads Excel (.xlsx/.xls) or CSV files and wants to perform data analysis, generate statistics, create summaries, pivot tables, SQL queries, or any form of structured data exploration. Supports multi-sheet Excel workbooks, aggregation, filtering, joins, and exporting results to CSV/JSON/Markdown.

dbt (data build tool) transforms data in your warehouse using SQL SELECT statements. Learn project setup, models, tests, documentation, incremental materializations, and integration with data warehouses like PostgreSQL, BigQuery, and Snowflake.

Provides a SQLite-backed persistence layer for skill execution metrics, feedback, improvement candidates, and version history, with query and maintenance workflows.

Converts CSV, Excel, or JSON data into a polished, interactive visual report page with KPI cards, charts, data tables, and insights.

Analyze datasets to answer defined questions through statistical methods, trend identification, hypothesis testing, and correlation analysis. Use when the user needs evidence-backed findings or decisions from data; use exploratory-data-analysis instead for open-ended first-pass profiling before questions are defined.