Loading...
Loading...
Found 286 Skills
Sync retirement account data from Vanguard and Fidelity CSV exports to Google Sheets DataHub. Handles multiple accounts, aggregates holdings by ticker, and updates quantities in retirement section (rows 46-62). Triggers on sync retirement, update retirement, vanguard sync, 401k update, IRA sync, or working with notebooks/retirement-accounts/ files.
Analyze CSV files, generate summary statistics, and create visualizations using Python and pandas. Use when the user uploads, attaches, or references a CSV file, asks to summarize or analyze tabular data, requests insights from CSV data, or wants to understand data structure and quality.
Automate College Football Data tasks via Rube MCP (Composio). Always search tools first for current schemas.
Design data systems by understanding storage engines, replication, partitioning, transactions, and consistency models. Use when the user mentions "database choice", "replication lag", "partitioning strategy", "consistency vs availability", or "stream processing". Covers data models, batch/stream processing, and distributed consensus. For system design, see system-design. For resilience, see release-it.
Use when writing or running Nushell commands, scripts, or pipelines - via the Nushell MCP server (mcp__nushell__evaluate), via Bash (nu -c), or in .nu script files. Also use when working with structured data (JSON, YAML, TOML, CSV, Parquet, SQLite), doing ad-hoc data analysis or exploration, or when the user's shell is Nushell.
This skill should be used when the user asks to "use NumPy", "write NumPy code", "optimize NumPy arrays", "vectorize with NumPy", or needs guidance on NumPy best practices, array operations, broadcasting, memory management, or scientific computing with Python.
This Skill supports screening qualified stocks based on stock selection criteria (such as market indicators, financial indicators, etc.); it allows querying stocks, listed companies within specified industries/sectors, as well as component stocks of sector indices; it also supports related tasks such as stock, listed company, and sector/index recommendations, avoiding the use of outdated information by large models during stock selection.
Use when asked to generate multiple QR codes from CSV data, create bulk QR codes with tracking, or generate QR codes for events/products.
Collect validated Xiaohongshu image assets from normalized XHS datasets into local manifests and downloaded files. Use this when you need reproducible local media artifacts from note covers or other already-exposed remote asset URLs.
Reconcile general ledger to subledger for a trade date or period — match at the position or transaction level, surface breaks, and classify each break by likely cause. Use for daily or month-end recon runs across asset classes.
Server-side quantitative indicator runner via Longbridge Securities — execute Pine Script v6 syntax subset against historical K-line data on Longbridge servers without a local Python environment. Supports built-in indicators (MACD, RSI, Bollinger Bands, EMA, SMA, etc.) and custom calculation logic; results returned as JSON. Triggers: "量化指标", "Pine Script", "指标计算", "MACD计算", "RSI计算", "服务端指标", "指标脚本", "量化脚本", "技术指标运行", "量化指標", "指標計算", "MACD計算", "RSI計算", "服務端指標", "指標腳本", "quant indicator", "Pine Script", "indicator calculation", "run indicator", "server-side quant", "MACD script", "RSI calculation", "technical indicator runner", "quant run".
Assess construction data quality using completeness, accuracy, consistency, timeliness, and validity metrics. Automated validation with regex patterns, thresholds, and reporting.