Skill UI

A comprehensive guide covering patterns and architectures for automating code execution using LLMs. It spans simple sequential pipelines (e.g., daily dev scripts) to advanced, RFC-driven multi-agent Directed Acyclic Graph (DAG) orchestration. Ideal for building CI/CD style continuous development pipelines, managing complex state, and automating quality gates without human intervention.

This advanced orchestration layer acts as the central point between founders and C-suite advisors. It analyzes complex strategic questions, scores decision complexity, and automatically routes queries to the necessary expert roles (e.g., CFO, CTO). It coordinates multi-agent 'board meetings' and synthesizes conflicting expert opinions into structured, actionable decision logs for major business decisions.

This guide demonstrates how to implement clean and robust asynchronous pipelines in TypeScript using `TaskEither` from `fp-ts`. It solves the common problem of deeply nested, unmanageable `try/catch` blocks, allowing developers to compose sequential API calls and operations while correctly handling success and failure paths in a functional, immutable manner. Ideal for complex data fetching and workflow orchestration.

Coordinates discovery, matching, and workflow orchestration across agent ecosystems, automatically scanning SKILL.md, ranking relevant skills, and executing multi-step plans for complex requests.

A deep expert system for Anthropic's Claude Code CLI. This skill maximizes productivity by managing advanced features like hooks, persistent context via CLAUDE.md, memory management, sub-agent orchestration, and integrating with local development ecosystems. Ideal for complex coding tasks, debugging, and advanced workflow automation.

A framework for building production-ready, low-latency, multimodal AI agents. It allows developers to orchestrate complex pipelines that seamlessly handle real-time audio, video, and text data. Key capabilities include integrating multiple AI services (LLMs, STT, TTS), managing conversation context, and enabling tool use and function calling for advanced, interactive AI applications.

Evaluates incoming requests, decides if specialized skills are needed, orchestrates optimal combinations, and logs successful pairings in agent-memory while guarding against overusing complex tools on simple tasks.

Manages and coordinates complex workflows involving multiple AI agents and tasks. It efficiently distributes work across multiple workers, handles runtime errors, and intelligently merges disparate results into a unified output, making it ideal for large-scale, distributed AI computations.

This skill orchestrates end-to-end autonomous AI research projects using a sophisticated two-loop architecture. It manages the entire research lifecycle, from initial literature review and hypothesis formation to running constrained experiments and synthesizing findings into publishable papers. It acts as a project manager, autonomously driving the process while structuring the workspace and presenting progress to human users.

A robust framework for orchestrating complex, multi-step coding tasks using multiple AI agents. It functions by first planning the entire project into a mission dependency graph (DAG), then dispatching specialized agents to isolated Git worktrees for parallel development. The system automatically monitors progress, handles merges, and generates structured, comprehensive reports detailing changes, completed tasks, and next steps.

This skill guides the implementation of Security Orchestration, Automation, and Response (SOAR) workflows using platforms like Splunk Phantom. It automates the entire incident lifecycle, including alert triage, IOC enrichment (via VirusTotal, CrowdStrike), containment actions, and ticket creation (ServiceNow). Ideal for SOC teams needing to reduce Mean Time To Respond (MTTR) and standardize complex security procedures across multiple tools.

This guide details how to implement sophisticated Security Orchestration, Automation, and Response (SOAR) playbooks using Palo Alto Cortex XSOAR. It demonstrates full lifecycle automation, such as phishing incident investigation, requiring enrichment of URLs, IPs, and files, followed by automated containment actions (e.g., blocking senders, purging mailboxes). Mastering this allows SOC teams to drastically reduce Mean Time To Respond (MTTR) by automating detection, analysis, and remediation steps across multiple security tools.