Skill UI

This is a comprehensive lookup reference for the Mercury MCP server tools. It guides AI agents on how to interact with a virtual team workspace, enabling structured communication for messaging teammates, managing threads and tasks, scheduling complex automations, and performing administrative graph edits. Use this when your agent needs to execute specific team workflows.

Design and optimize production-grade multi-agent systems using LangGraph, LangChain, and DeepAgents. Provides structured workflows for creating supervisor, planner, researcher, coder, and validator agents with memory and tool-calling pipelines.

Design n8n AI agents, chains, classifiers, extractors, tool calling, memory, RAG, structured output, and human-review flows. Covers sub-node patterns, connection types, and best practices for production workflows.

Build reusable n8n sub-workflows with typed inputs, all-vs-each execution, discoverable naming, and agent-tool exposure. Covers when to extract logic, input/output contracts, and stateless vs stateful design.

Proven architectural patterns for building n8n workflows, including webhook processing, API integration, database operations, AI agent workflows, and scheduled tasks. Guides pattern selection and implementation.

Plans first, then orchestrates dynamic multi-agent workflows with adversarial verification via a local odw daemon or native fallback. Supports 16-100 parallel agents, sandboxed scripts, SQLite checkpoints, and crash-resume for tasks spanning many files or items.

Open Dynamic Workflows (ODW) is an advanced, open-source engine designed to manage complex coding tasks using multiple AI agents. It operates in three stages: planning the task into parallel subtasks, orchestrating various coding agents (like Codex or Antigravity) to work concurrently, and finally, performing an adversarial verification pass to challenge and validate the combined output before merging. It supports multiple model providers and is ideal for structured, high-reliability software development.

Set up OpenClaw locally or on a Mac mini with Docker Compose, macOS hardening, launchd services, networking, monitoring, and production-grade always-on agent workflows.

API-first control plane for reply-aware cold outbound email workflows, managing inboxes, contacts, templates, sequences, approvals, pacing, webhooks, and delivery metrics for AI agents.

Polis Protocol transforms static AI agent workflows into a self-improving, dynamic team environment. It uses a learning router to assign tasks based on an agent's historical track record and effectiveness on specific domains, rather than fixed roles. It is vendor-agnostic, allowing diverse agents (e.g., Claude, Gemini) to collaborate under a unified, auditable governance structure where agents can propose and vote on amending the protocol's core rules.

This skill systematically audits a given project by analyzing its historical memory, rollout summaries, and local skill definitions. It identifies repeated complex workflows, recurring operational patterns, or areas where existing skills are stale. The audit process guides the user to recommend the highest-value skills to either add (if a new pattern emerges) or update (if current skills are outdated or incomplete), ensuring the AI agent operates with the most accurate and context-specific knowledge base for optimal project execution.

Protect AI systems from prompt injection attacks by implementing input sanitization, tool permissioning, and output validation. Essential for securing LLM applications, RAG pipelines, and agentic workflows against indirect and direct attacks.