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Found 286 Skills
Use this skill for ANY question about creating test or evaluation datasets for LangChain agents. Covers generating datasets from traces (final_response, single_step, trajectory, RAG types), uploading to LangSmith, and managing evaluation data.
Analyze historical downtrend durations and generate interactive HTML histograms showing typical correction lengths by sector and market cap.
Run SQL queries against the attached DuckDB database or ad-hoc against files. Accepts raw SQL or natural language questions. Uses DuckDB Friendly SQL idioms.
Analyze stock liquidity using bid-ask spreads, volume profiles, order book depth, market impact estimates, and turnover ratios via Yahoo Finance data. Use this skill whenever the user asks about liquidity, trading costs, bid-ask spread, market depth, volume analysis, slippage, market impact, turnover ratio, or how easy/hard it is to trade a stock without moving the price. Triggers: "how liquid is AAPL", "bid-ask spread", "volume analysis", "order book depth", "market impact of a large order", "turnover ratio", "slippage estimate", "can I trade 100k shares without moving the price", "liquidity comparison", "spread analysis", "ADTV", "Amihud illiquidity", "dollar volume", "execution cost estimate", "liquidity score", penny stocks, small caps, or thinly traded securities.
Databricks CLI operations: auth, profiles, data exploration, and bundles. Contains up-to-date guidelines for Databricks-related CLI tasks.
Python data processing with pandas, openpyxl, and lxml. Covers DataFrame operations, Excel I/O, XML parsing, bulk data transformation, and large-file handling. Use when processing tabular data, spreadsheets, or XML in Python. USE WHEN: user mentions "pandas", "DataFrame", "openpyxl", "read_excel", "lxml", "XPath", "CSV processing", "Excel parsing", "bulk data", "large file", "data transformation", "UTF-16", "codecs" DO NOT USE FOR: SQL databases (use sql-expert), NumPy-only math, ML/training
Generate professional company tear sheets using S&P Capital IQ data via the Kensho LLM-ready API MCP server. Use this skill whenever the user asks for a tear sheet, company one-pager, company profile, fact sheet, company snapshot, or company overview document — especially when they mention a specific company name or ticker. Also trigger when users ask for equity research summaries, M&A company profiles, corporate development target profiles, sales/BD meeting prep documents, or any concise single-company financial summary. This skill supports four audience types: equity research, investment banking/M&A, corporate development, and sales/business development. If the user doesn't specify an audience, ask. Works for both public and private companies.
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".
Expert-level algorithmic trading, market systems, quantitative analysis, and trading platforms
Convert JSON rows with latitude/longitude fields into a GeoJSON FeatureCollection using raw PostGIS SQL.
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.