Total 54,373 skills, Data Processing has 2785 skills
Showing 12 of 2785 skills
Corporate event opportunity scanner for A-share companies via Longbridge — identifies and analyses events that may create pricing dislocations: M&A / restructuring (asset injection / reverse merger), major shareholder increases / buybacks (positive signal), equity incentive plans (management alignment), index inclusion / exclusion (forced passive flows), and lockup expiry (potential selling pressure). Provides historical statistical patterns and trading window recommendations per event type. Triggers: "捕捉机会", "事件机会", "并购重组机会", "增持机会", "回购信号", "指数调整机会", "解禁压力", "事件套利", "捕捉機會", "事件機會", "並購重組機會", "增持機會", "回購信號", "指數調整機會", "解禁壓力", "event opportunity", "corporate event", "M&A opportunity", "buyback signal", "index inclusion", "lockup expiry", "event catalyst", "special situation", "event-driven".
Undetectable, adaptive, high-performance Python web data extraction. Automatically survives website structure changes, bypasses anti-bot systems (Cloudflare, WAFs), and outperforms BeautifulSoup/Scrapy. Includes stealth browser fetching, CSS/XPath selectors, CLI, interactive shell, and MCP AI server integration.
Query the Reactome database (Analysis and Content Services). Use when the user asks about pathway analysis, gene list enrichment, retrieving results by token, finding unmapped or not-found identifiers, mapping identifiers, reaction participants (inputs, outputs), pathway hierarchy (including top-level pathways), diagram export, cross-reference mapping, or searching the knowledgebase.
Set up end-to-end Change Data Capture (CDC) pipelines on Confluent Cloud using Debezium source connectors, Flink for transformation, and Tableflow for data lake integration. Supports JSON_SR, Avro, and Protobuf formats. Handles schemaless topics (plain JSON without SR) and multi-event topics. This skill handles the complete workflow from database to Iceberg/Delta tables. Use this skill when users want to capture database changes and materialize them into Iceberg or Delta Lake tables via Confluent Cloud Tableflow. Trigger phrases include "CDC to Tableflow", "database to Iceberg", "database to Delta Lake", "stream database changes to data lake", "set up Tableflow pipeline", "schemaless topic to Tableflow", or "multi-event topic to Iceberg". Do NOT trigger for general CDC, Debezium, or database replication requests that do not involve Tableflow or Iceberg/Delta Lake as the destination.
Build ETL pipelines and analytics dashboards using the Harvard Art Museums API with Python, SQL, and Streamlit
End-to-end ETL pipeline and analytics application for Harvard Art Museums API with Streamlit dashboards
Update financial models with new data — quarterly earnings, management guidance, macro changes, or revised assumptions. Adjusts estimates, recalculates valuation, and flags material changes. Use after earnings, guidance updates, or when assumptions need refreshing. Triggers on "update model", "plug earnings", "refresh estimates", "update numbers for [company]", "new guidance", or "revise estimates".
Use when large data ingestion, backfill, export, ETL, warehouse loading, manifest catch-up, or table synchronization needs to become much faster while preserving data correctness.
10 data wrangling skills. Trigger: messy data, format conversion, missing values, data reshaping. Design: pipeline-oriented recipes for common data cleaning and transformation tasks.
Read and write large cuPyNumeric arrays to HDF5 with Legate's parallel, distributed HDF5 I/O (legate.io.hdf5: to_file, from_file, from_file_batched). Use when a developer needs to save a cuPyNumeric array to an .h5/.hdf5 file, load an HDF5 dataset into a distributed cuPyNumeric array, read a large HDF5 dataset in chunks, hand arrays to an HPC pipeline as a single file, or accelerate HDF5 disk I/O with GPUDirect Storage (GDS). Do not use it for Parquet/cuDF/raw-binary or other sharded/custom layouts (see the cupynumeric-parallel-data-load skill), Zarr or object-store/S3 output, .npz or pickled archives, plain h5py without cuPyNumeric, or pure array compute such as FFT, matmul, or reductions.
Query and aggregation-pipeline optimization rules for Azure DocumentDB — using `explain("executionStats")` to verify index usage and avoid `COLLSCAN`. Use when reviewing a specific query, diagnosing a slow query, or validating that an index is actually being used. For full index-design workflow, see the `documentdb-query-optimizer` skill.
Assess construction data quality using completeness, accuracy, consistency, timeliness, and validity metrics. Automated validation with regex patterns, thresholds, and reporting.