Total 50,510 skills, Data Processing has 2560 skills
Showing 12 of 2560 skills
Screen and deeply analyze potential NSE multibagger stocks using a Peter Lynch-style quality, growth, valuation, and technical framework. Use when the user asks for multibagger stocks, undervalued high-growth NSE stocks, Peter Lynch-style analysis, top NSE multibagger candidates, smallcap/midcap compounders, value traps, or worst-case scenarios for high-potential stocks.
Primary entry point for building, managing, and orchestrating data pipelines on Google Cloud. Guides users to the appropriate skill for dbt, Dataflow (Apache Beam), Dataform, Spark (Dataproc Serverless), BigQuery Data Transfer Service (DTS) or orchestration pipeline using Cloud Composer. Clarify requirements and resolve ambiguity for creating, updating and running data pipelines.
Skill for BigQuery AI and Machine Learning queries using standard SQL and `AI.*` functions (preferred over dedicated tools).
Create publication figures with matplotlib/seaborn/plotly. Multi-panel layouts, error bars, significance markers, colorblind-safe, export PDF/EPS/TIFF, for journal-ready scientific plots.
Reference documentation for live music data APIs and ID mapping between services. Use when integrating MusicBrainz, Setlist.fm, JamBase, Bandsintown, Ticketmaster, or other concert/artist APIs.
Expert in migrating Apache Solr collections to OpenSearch indexes. Translates Solr XML/JSON schemas to OpenSearch mappings and converts Solr syntax (Standard, DisMax, eDisMax) into OpenSearch DSL. Provides sizing for nodes, shards, and JVM heap. Provides guidance auf authentication migration from Solr to OpenSearch. Uses the AWS Knowledge MCP Server for accurate, up-to-date OpenSearch and AWS service information.
InfluxDB Cloud integration. Manage data, records, and automate workflows. Use when the user wants to interact with InfluxDB Cloud data.
Specialist in self-healing data pipelines — uses air-gapped local SLMs and semantic clustering to automatically detect, classify, and fix data anomalies at scale. Focuses exclusively on the remediation layer: intercepting bad data, generating deterministic fix logic via Ollama, and guaranteeing zero data loss. Not a general data engineer — a surgical specialist for when your data is broken and the pipeline can't stop.
Builds data infrastructure — ETL/ELT pipelines, data warehousing, stream processing, data quality, orchestration (Airflow/Dagster), and analytics engineering (dbt). Use when the user asks to build data pipelines, set up ETL/ELT workflows, design a data warehouse, configure stream processing, or implement analytics engineering with dbt, Airflow, or Dagster.
Guides exploration of $autocapture events captured by posthog-js to understand user interactions, find CSS selectors (especially data-attr attributes), evaluate selector uniqueness, query matching clicks ad-hoc, and create actions. Use when the user asks about autocapture data, wants to find what users are clicking, needs to build actions from click events, asks about elements_chain, wants to build a trend or funnel filtered by clicks or other autocapture interactions, asks which properties autocapture sends, or asks how to filter $autocapture events. Only applies to projects using posthog-js autocapture.
Manage PostHog subscriptions — scheduled email, Slack, or webhook deliveries of insight or dashboard snapshots. Use when the user wants to subscribe to an insight or dashboard, check existing subscriptions, change delivery frequency, add or remove recipients, or stop receiving updates.
Airbyte integration. Manage data, records, and automate workflows. Use when the user wants to interact with Airbyte data.