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Found 2,040 Skills
MiniQMT Xuntou Quantitative Trading Interface, based on the XtQuant Python library, supports market data acquisition (K-line, tick data, financial data, etc.) and trading operations (order placement, order cancellation, querying assets/orders/positions) for A-shares, futures, and options. It is used when users need to obtain real-time/historical market data from MiniQMT, conduct quantitative trading, or perform backtesting.
Detect and fix SQL injection vulnerabilities in any framework. Covers Laravel (DB::raw, whereRaw), Node.js (template literals in queries), Python (f-strings in SQL), and Cloudflare D1. Enforces parameterized bindings everywhere. Use when writing database queries, reviewing code for injection, or fixing SQL injection findings.
Build AI scientist systems using ToolUniverse Python SDK for scientific research. Use when users need to access 1000++ scientific tools through Python code, create scientific workflows, perform drug discovery, protein analysis, genomics analysis, literature research, or any computational biology task. Triggers include requests to use scientific tools programmatically, build research pipelines, analyze biological data, search literature, predict drug properties, or create AI-powered scientific workflows.
Query, audit, and optimize Google Ads campaigns. Supports two modes: (1) API mode for bulk operations with google-ads Python SDK, (2) Browser automation mode for users without API access - just attach a browser tab to ads.google.com. Use when asked to check ad performance, pause campaigns/keywords, find wasted spend, audit conversion tracking, or optimize Google Ads accounts.
Finds all REFACTOR markers in codebase, validates associated ADRs exist, identifies stale markers (30+ days old), and detects orphaned markers (no ADR reference). Use during status checks, before feature completion, or for refactor health audits. Triggers on "check refactor status", "marker health", "what's the status", or PROACTIVELY before marking features complete. Works with Python (.py), TypeScript (.ts), and JavaScript (.js) files using grep patterns to locate markers and validate against ADR files in docs/adr/ directories.
Skill for creating custom lint rules by leveraging the existing linter ecosystems of various programming languages. This is a linter designed for AI Agents rather than humans, and its error messages function as correction instruction prompts for AI. Create custom rules in the `lints/` directory using standard methods for each language, including Rust (dylint), TypeScript/JavaScript (ESLint), Python (pylint), Go (golangci-lint), etc. Use this skill in the following scenarios: (1) When you want AI to enforce project-specific coding rules; (2) When you want to create lint rules that output AI-readable correction instructions when violations occur; (3) When you want to enforce naming conventions, structural patterns, and consistency rules through AI-driven linting. Triggers: "Create a linter rule", "Add a lint rule", "Enforce this pattern", "AI linter", "Custom lint", "Code rules", "Naming rules", "Structural rules", "create a linter rule", "add a lint rule", "enforce this pattern", "AI linter".
CLI-based image generation from text prompts using Google Gemini APIs via Python. Use when user needs "generate image", "create image with AI", "gemini image", "text to image", "create sprite", or "generate character art". Supports model selection, batch generation, watermark removal, and background transparency. Do NOT use for web app image features (use nano-banana-builder), video/audio generation, or non-Gemini models.
This skill should be used when users want to install, set up, or integrate ZeroEval into their AI application, agent, or pipeline. It covers SDK setup (Python and TypeScript), first-run tracing, ze.prompt migration, and judge recommendations. For non-SDK languages or direct API/OTLP ingestion it routes to the custom-tracing skill. Triggers on "install zeroeval", "set up zeroeval", "add tracing", "integrate zeroeval", "ze.prompt", "add judges", or "monitor my AI app".
JVM performance profiling with Java Flight Recorder (JFR), jcmd, and GC analysis. Use for identifying bottlenecks and memory issues. USE WHEN: user mentions "Java profiling", "JFR", "JVM performance", asks about "Java Flight Recorder", "jcmd", "heap dump", "GC tuning", "thread dump", "Java memory leak" DO NOT USE FOR: Node.js/Python profiling - use respective skills instead
Control interactive CLIs (python, gdb, etc.) via tmux sessions - send keystrokes and scrape output
Designs and builds ETL/ELT data pipelines. Takes data sources, destination, transformation requirements. Generates pipeline code (Python/SQL), scheduling config, error handling, monitoring setup, and data quality checks. Outputs data-pipeline-spec.md + implementation files.
Analyze lakehouse data interactively using Fabric Livy sessions and PySpark/Spark SQL for advanced analytics, DataFrames, cross-lakehouse joins, Delta time-travel, and unstructured/JSON data. Use when the user explicitly asks for PySpark, Spark DataFrames, Livy sessions, or Python-based analysis — NOT for simple SQL queries. Triggers: "PySpark", "Spark SQL", "analyze with PySpark", "Spark DataFrame", "Livy session", "lakehouse with Python", "PySpark analysis", "PySpark data quality", "Delta time-travel with Spark".