Skills
96,981 packaged capabilities and playbooks for agents. Install any of them with one command.
Agent workflows
Use when verifying, using, or troubleshooting the anonymous Parallel Search MCP server bundled with this plugin — when the user installs the plugin, says "set up Parallel Search", "Parallel search isn't working", hits 429 rate limits from Parallel, or asks how to get higher limits. Also covers how to use the web_search and web_fetch tools the bundled server provides. For an authenticated gateway deployment use parallel-mcp-setup; for the CLI-backed skills use parallel-cli-setup.
Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast execution
Review a development pipeline where AI coding agents write, commit and deploy — permission boundaries, approval gates on irreversible actions, credential scope, and what must never be delegated. Use when agents have write access to a repository or an environment.
Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.
Use when the user asks to optimize prompts, design prompt templates, evaluate LLM outputs with an eval set, measure RAG retrieval quality, validate agent/tool configurations, analyze token usage, or design structured-output contracts. Covers eval-driven prompt iteration, RAG metrics (relevance, faithfulness, coverage), agent workflow validation, and token/cost budgeting — all model-agnostic, with three stdlib Python tools.
Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.
Analyzes an MLflow session — a sequence of traces from a multi-turn chat conversation or interaction. Use when the user asks to debug a chat conversation, review session or chat history, find where a multi-turn chat went wrong, or analyze patterns across turns. Triggers on "analyze this session", "what happened in this conversation", "debug session", "review chat history", "where did this chat go wrong", "session traces", "analyze chat", "debug this chat".
NVIDIA's runtime safety framework for LLM applications. Features jailbreak detection, input/output validation, fact-checking, hallucination detection, PII filtering, toxicity detection. Uses Colang 2.0 DSL for programmable rails. Production-ready, runs on T4 GPU.
Fetches aggregated trace metrics (token usage, latency, trace counts, quality evaluations) from MLflow tracking servers. Triggers on requests to show metrics, analyze token usage, view LLM costs, check usage trends, or query trace statistics.
Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Claude ('No, that's wrong...', 'Actually...'), (3) User requests a capability that doesn't exist, (4) An external API or tool fails, (5) Claude realizes its knowledge is outdated or incorrect, (6) A better approach is discovered for a recurring task. Also review learnings before major tasks.
Configure which models pstack uses per role. Detects available models and writes the current runtime's override sheet. Use for /setup-pstack, "configure pstack models", changing pstack's model choices, or turning the SessionStart hook on or off.
Writes educational Teams channel posts about Claude Code features and best practices, using templates emphasizing concrete examples and underlying principles (e.g. context engineering). Use when writing a Teams post or announcement to teach colleagues Claude Code usage, announce a feature, share a tip, or document a lesson learned.
Run agency-orchestrator YAML workflows directly in Claude Code / OpenClaw / Cursor — no API key required; uses the current session’s LLM as the execution engine. Trigger when the user provides a .yaml workflow file or asks for a task to be completed collaboratively by multiple roles.
Generate a project-local verification skill that drives your app the way a user does — any language, framework, or platform. Use for /create-verification-skill, \"make a control skill for this repo\", \"make a driver skill for this repo\", or when a project has no scripted way to prove UI/CLI/service behavior.
Select, define, create, inspect, update, attach, detach, and verify reusable Vapi tools through the public API. Use for native call-control tools, supported provider integrations, API Request tools, custom function tools, MCP tools, tool messages, credentials, or configuration-preserving assistant attachment changes
Diagnose and resolve Claude Code plugin and skill issues. This skill should be used when plugins are installed but not showing in available skills list, skills are not activating as expected, or when troubleshooting enabledPlugins configuration in settings.json. Triggers include "plugin not working", "skill not showing", "installed but disabled", or "enabledPlugins" issues.
Connect AI agents (Claude Code, Claude Desktop, Cursor, VS Code, Cline, Codex) to Capacitor and Ionic tooling over Model Context Protocol. Covers the awesome-ionic-mcp server (Ionic component APIs, Capacitor plugin docs, Ionic/Capacitor CLI tools), the Capgo hosted MCP at https://api.capgo.app/mcp, the local Capgo CLI MCP (npx @capgo/cli@latest mcp), and Xcode's MCP bridge (xcrun mcpbridge) for building the iOS app from an agent. Use when the user wants to add, configure, verify, or troubleshoot an MCP server for a Capacitor project, or sees errors like "GitHub API rate limit exceeded", "unable to find utility \"mcpbridge\"", or an MCP server that connects with no tools. Do not use for Capgo account operations themselves (capgo-cloud, capgo-cli-usage), app debugging (debugging-capacitor), device logs (ios-android-logs), choosing plugins (capacitor-plugins), or Ionic UI work (ionic-design).
Guide for creating AI subagents with isolated context for complex multi-step workflows. Use when users want to create a subagent, specialized agent, verifier, debugger, or orchestrator that requires isolated context and deep specialization. Works with any agent that supports subagent delegation. Triggers on "create subagent", "new agent", "specialized assistant", "create verifier". Do NOT use for Cursor-specific subagents (use cursor-subagent-creator instead).
Step-by-step agent workflow to configure the OpenAI Codex CLI on any machine (Linux, macOS, Windows) to use OmniRoute as an OpenAI-compatible backend. Detects OS and shell, writes config.toml and 7 named profiles, sets environment variables, and verifies the setup.
Periodic pass that keeps a project's verification skill and feature map honest: parallel source readers per feature, one live session driving every feature, at most one PR of proven corrections. Use for /maintain-verification-skill or "audit the verify skill".
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.