Skill UI

Expert skill for building Model Context Protocol (MCP) servers and tools from scratch. Covers the full development lifecycle—from defining tool schemas and resources to implementation, testing, and publishing to registries. Supports robust patterns using TypeScript and Python for integrating custom capabilities into AI agents.

This skill detects persistent instruction injection vulnerabilities in AI agents. It verifies if the agent stores malicious instructions in long-term memory or context to execute later. The process involves attempting to write a persistent rule and verifying if the agent follows it in subsequent interactions. It focuses on behavioral persistence rather than data leakage.

This skill provides methodologies for designing sophisticated, layered memory architectures for AI agents. It moves beyond simple context windows and basic vector stores by incorporating short-term, long-term, and graph-based memory systems. Implementing this is crucial for agents that require persistence across sessions, maintaining complex entity consistency, and performing advanced, relationship-driven reasoning over accumulated knowledge bases, including time-aware fact tracking.

A self-hosted, semantic memory service designed for AI agents. It allows agents to save worklogs, decisions, and notes, and recall this knowledge across sessions based on meaning rather than exact keywords. Using pgvector and structured workspaces, it maintains persistent, context-aware knowledge bases, making it ideal for collaborative AI projects.

Routes ambiguous data-observability requests to the appropriate Monte Carlo skill or workflow. Activates reactively for unclear alerts, incidents, coverage gaps, or general data quality issues, asking clarifying questions first to avoid premature tool calls.

Surfaces Monte Carlo data observability insights—health, lineage, alerts, and monitors-as-code—before developers edit dbt models or SQL pipelines, ensuring risk-aware code changes.

Investigate and resolve data quality alerts using Monte Carlo MCP tools. Performs root cause analysis, assesses impact, discovers remediation tools, and executes fixes or escalates issues with full context.

This skill covers designing multi-agent systems to overcome single-agent context limits. It includes patterns like supervisor/orchestrator, swarm, and hierarchical structures. Key concepts involve context isolation, parallel execution, token economics, and specialization for complex tasks.

This skill combines a senior software engineering workflow with product design expertise. It ensures full planning, architecture, test-driven development, clean code, and pixel-perfect UX validation. Ideal for complex projects requiring both technical robustness and visual precision in Portuguese contexts.

This skill provides effective patterns for transitioning on-call responsibilities between shifts. It ensures continuity, context transfer, and reliable incident response by documenting active incidents, ongoing investigations, recent changes, and known issues. Includes templates for shift handoff documents and quick async handoffs to improve onboarding and operational stability.

Coordinates multiple specialized agents via Claude Code's native Agent Tool for complex tasks requiring diverse expertise, such as comprehensive code analysis, feature reviews, and security audits. Enables sequential chains, context passing, and resumable work, with a synthesis protocol for unified recommendations.

A comprehensive suite designed for AI agents to perform safe, deep, and multi-format web searches. It allows agents to retrieve current web information, analyze PDFs, extract YouTube transcripts, and access GitHub code files. Instructions include guidelines for varying search depths and using specialized tools for code context retrieval.