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Found 2,186 Skills
AWS S3 object storage for bucket management, object operations, and access control. Use when creating buckets, uploading files, configuring lifecycle policies, setting up static websites, managing permissions, or implementing cross-region replication.
Analyze code coverage and CRAP (Change Risk Anti-Patterns) scores to identify high-risk code. Use OpenCover format with ReportGenerator for Risk Hotspots showing cyclomatic complexity and untested code paths.
Retrieval-augmented generation (RAG) skill for the D&D 5e System Reference Document (SRD). Use when answering questions about D&D 5e core rules, spells, combat, equipment, conditions, monsters, and other SRD content. This skill provides agentic search-based access to the SRD split into page-range markdown files.
Enforces minimum quality thresholds in CI including code coverage, linting, type checking, and security scanning. Provides required checks, PR rules, and automated enforcement. Use for "quality gates", "CI checks", "code quality", or "PR requirements".
Builds LLM applications with LangChain including chains, agents, memory, tools, and RAG pipelines. Use when users request "LangChain setup", "LLM chain", "AI workflow", "conversational AI", or "RAG pipeline".
Use only when writing/updating/fixing C++ tests, configuring GoogleTest/CTest, diagnosing failing or flaky tests, or adding coverage/sanitizers.
Generates comprehensive, workable unit tests for any programming language using a multi-agent pipeline. Use when asked to generate tests, write unit tests, improve test coverage, add test coverage, create test files, or test a codebase. Supports C#, TypeScript, JavaScript, Python, Go, Rust, Java, and more. Orchestrates research, planning, and implementation phases to produce tests that compile, pass, and follow project conventions.
Execute and troubleshoot memory-cli commands for episode management, pattern analysis, and storage operations. Use this skill when running CLI commands, debugging CLI issues, explaining command usage, or guiding users through CLI workflows.
Create and run orq.ai experiments — compare configurations against datasets using evaluators, analyze results, and generate prioritized action plans. Use when evaluating LLM agents, deployments, conversations, or RAG pipelines end-to-end. Do NOT use without a dataset and evaluators. Do NOT use for cross-framework comparisons with external agents (use compare-agents).
Design, create, and configure orq.ai Agents with tools, instructions, knowledge bases, and memory stores. Use when building new agents, attaching KBs or memory, writing system instructions, selecting models, or setting up RAG pipelines. Do NOT use for debugging existing agents (use analyze-trace-failures) or comparing agents across frameworks (use compare-agents).
Use when feature flag tests fail, flags need updating, understanding @gate pragmas, debugging channel-specific test failures, or adding new flags to React.
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.