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Found 1,941 Skills
RSpec testing best practices for Ruby and Rails applications, covering test organization, data management, and isolation patterns.
Professional Skills and Methodologies for Mobile Application Security Testing
Statistical arbitrage tool for identifying and analyzing pair trading opportunities. Detects cointegrated stock pairs within sectors, analyzes spread behavior, calculates z-scores, and provides entry/exit recommendations for market-neutral strategies. Use when user requests pair trading opportunities, statistical arbitrage screening, mean-reversion strategies, or market-neutral portfolio construction. Supports correlation analysis, cointegration testing, and spread backtesting.
Amazon Bedrock AgentCore Evaluations for testing and monitoring AI agent quality. 13 built-in evaluators plus custom LLM-as-Judge patterns. Use when testing agents, monitoring production quality, setting up alerts, or validating agent behavior.
Test features before users find bugs. Use when feature is built, before deploying, or when bugs reported. Covers manual testing, edge cases, cross-browser testing, and testing checklists for non-technical founders.
Comprehensive toolkit for validating, linting, testing, and automating Jenkinsfile pipelines (both Declarative and Scripted). Use this skill when working with Jenkins pipeline files, validating pipeline syntax, checking best practices, debugging pipeline issues, or working with custom plugins.
Host simulated panel discussions and debates among AI-simulated domain experts. Supports roundtable, Oxford-style, and Socratic formats with heterogeneous expert personas, anti-groupthink mechanisms, and structured synthesis. Use when exploring complex topics from multiple expert perspectives, testing argument strength, academic brainstorming, or understanding trade-offs in decisions. NOT for one-on-one conversations, simple Q&A, or real-time debates.
Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations with extensive frameworks, testing methodologies, and educational content.
Use when needing point-in-time recovery, version control for object storage, or creating isolated bucket copies for testing/experimentation
Creates an integration testing plan for .NET data access artifacts during Oracle-to-PostgreSQL database migrations. Analyzes a single project to identify repositories, DAOs, and service layers that interact with the database, then produces a structured testing plan. Use when planning integration test coverage for a migrated project, identifying which data access methods need tests, or preparing for Oracle-to-PostgreSQL migration validation.
Instrument Python LLM apps, build golden datasets, write eval-based tests, run them, and root-cause failures — covering the full eval-driven development cycle. Make sure to use this skill whenever a user is developing, testing, QA-ing, evaluating, or benchmarking a Python project that calls an LLM, even if they don't say "evals" explicitly. Use for making sure an AI app works correctly, catching regressions after prompt changes, debugging why an agent started behaving differently, or validating output quality before shipping.
Explore any codebase from scratch and generate six quality artifacts: a quality constitution (QUALITY.md), spec-traced functional tests, a code review protocol, an integration testing protocol, a multi-model spec audit (Council of Three), and an AI bootstrap file (AGENTS.md). Works with any language (Python, Java, Scala, TypeScript, Go, Rust, etc.). Use this skill whenever the user asks to set up a quality playbook, generate functional tests from specifications, create a quality constitution, build testing protocols, audit code against specs, or establish a repeatable quality system for a project. Also trigger when the user mentions 'quality playbook', 'spec audit', 'Council of Three', 'fitness-to-purpose', 'coverage theater', or wants to go beyond basic test generation to build a full quality system grounded in their actual codebase.