Total 53,693 skills, Data Processing has 2765 skills
Showing 12 of 2765 skills
Design, build, and optimize dashboards for RIA practice management with AUM tracking, revenue analytics, and KPI frameworks. Use when the user asks about tracking firm-level metrics, monitoring advisor productivity, measuring organic growth rate, analyzing client retention and attrition, building executive or branch manager views, setting up exception alerts for NIGO or rebalancing drift, benchmarking against industry peers, or designing role-based dashboard access. Also trigger when users mention 'how is the practice doing', 'revenue per advisor', 'client attrition', 'net new assets', 'effective fee rate', 'practice benchmarking', 'AUM growth decomposition', 'advisor capacity', or 'referral tracking'.
Analyze equity securities, factor models, and equity portfolio construction. Use when the user asks about stocks, equity valuation ratios, index construction methods, or style analysis. Also trigger when users mention 'P/E ratio', 'growth vs value', 'market cap weighting', 'sector allocation', 'GICS classification', 'earnings per share', 'Fama-French factors', 'CAPM', 'dividend yield', 'PEG ratio', 'EV/EBITDA', or ask which factors explain equity returns.
Track and analyze e-commerce sales performance across platforms. Set up KPI dashboards, trend analysis, and performance alerts to catch issues and opportunities early.
Valuation and pricing framework focusing on valuation analysis / pricing logic / investment decisions. This Skill is mainly applied in scenarios such as answering user questions, writing reports, and creating financial articles. This report generates extensive content and is not suitable for simple conversation scenarios. Various information and data can be obtained via the wind.financial.data tool using appropriate keywords or keyword combinations. Users want to know how to value a company, the level of its current valuation, why the market is willing to assign this valuation, and whether there is room for revaluation.
Structure and organize Dagster code locations using dg. Use this skill when creating or migrating code locations, placing assets or sensors in the correct location, scaffolding new dg projects, or setting up the dg_projects/ workspace layout.
Systematic stock screening and investment idea sourcing. Combines quantitative screens, thematic research, and pattern recognition to surface new long and short ideas. Use when looking for new ideas, running screens, or conducting thematic sweeps. Triggers on "idea generation", "stock screen", "find ideas", "what looks interesting", "screen for", "new ideas", or "pitch me something".
Analyze portfolio allocation drift and generate rebalancing trade recommendations across accounts. Considers tax implications, transaction costs, and wash sale rules. Triggers on "rebalance", "portfolio drift", "allocation check", "rebalancing trades", or "my portfolio is out of balance".
Use Ibis for database-agnostic data access in Python. Use when writing data queries, connecting to databases (DuckDB, PostgreSQL, SQLite), or building portable data pipelines that should work across backends.
Structured data research: search sources, extract structured data, archive raw sources, maintain canonical tracker pages, deduplicate. Parameterized via YAML recipes for investor updates, donations, company updates, or any email-to-structured-data pipeline.
Search arXiv papers by keyword, author, category, or ID.
Complete FFmpeg + OpenCV + Python integration guide for video processing pipelines. PROACTIVELY activate for: (1) FFmpeg to OpenCV frame handoff, (2) cv2.VideoCapture vs ffmpeg subprocess, (3) BGR/RGB color format conversion gotchas, (4) Frame dimension order img[y,x] vs img[x,y], (5) ffmpegcv GPU-accelerated video I/O, (6) VidGear multi-threaded streaming, (7) Decord batch video loading for ML, (8) PyAV frame-level processing, (9) Audio stream preservation with video filters, (10) Memory-efficient frame generators, (11) OpenCV + FFmpeg + Modal parallel processing, (12) Pipe frames between FFmpeg and OpenCV. Provides: Color format conversion patterns, coordinate system gotchas, library selection guide, memory management, subprocess pipe patterns, GPU-accelerated alternatives to cv2.VideoCapture. Ensures: Correct integration between FFmpeg and OpenCV without color/coordinate bugs. See also: ffmpeg-python-integration-reference for type-safe parameter mappings.
Generates SQL validation notebooks for dbt PR changes with before/after comparison queries.