Skill UI

Pilot Protocol is an open-source overlay network providing AI agents with first-class network citizenship. It offers permanent virtual addresses, encrypted P2P communication, NAT traversal, and a structured app store. Use this skill when agents require stable networking across cloud changes, need direct, secure messaging without shared accounts, or must integrate structured external data services (e.g., live crypto prices) via local, typed capabilities.

This skill runs a single, simulated tick of a defined business pod (Sales). It is designed for dry-run testing, allowing users to validate inputs, resolve agents, and reserve budgeted resources without invoking real LLM workers. It handles template loading, schema validation, budget management, and emits structured status reports for subsequent workflow consumption.

This skill assists in researching prediction markets, treating them as potential data sources or oracle signals for corporate decision intelligence, product development, and agent inputs. It provides source-grounded analysis of implied probabilities, signal quality, and integration patterns, while strictly avoiding investment advice.

This skill reviews prediction market, basket, oracle, and trading-agent workflows to ensure compliance, safety, data quality, and privacy. It must be run before any workflow handles user portfolio data, venue authentication, API keys, or trade planning to mitigate execution and regulatory risks across multiple domains.

This tool manages RVF (Ruflo Vector Format) files, providing comprehensive functions for handling, transferring, and backing up agent memories. It supports listing, exporting, importing, migrating, and generating statistics for complex vector embeddings and associated metadata, ensuring knowledge portability across different projects and platforms.

The Safety Guard skill is designed to prevent destructive actions when working in production systems or when autonomous agents are executing tasks. It offers three protection modes (Careful, Freeze, and Guard) to monitor dangerous commands, lock specific directories, and ensure code changes are confined to intended areas, enhancing system stability and security.

The Santa Method is a multi-agent adversarial verification framework designed to improve output quality by requiring two independent reviewers (Agent B and Agent C) to pass the content before publication. It eliminates single-agent bias and systematically improves outputs through iterative refinement loops until convergence or manual escalation.

A comprehensive security auditing tool designed for Claude Code environments. It scans the entire configuration directory for security vulnerabilities, misconfigurations, injection risks, hardcoded secrets, and overly permissive settings. Ideal for initial project setup, pre-commit checks, or routine security hygiene.

This toolset allows users to save and restore complete agent sessions, ensuring context continuity across multiple conversations. It captures the agent's entire state, including memory, configuration, task progress, and learning metrics. Use it when pausing work and needing to resume later with full context, or for checkpointing long-running tasks.

This tool assesses whether coding agents adhere to defined skills, rules, or agent definitions. It automatically generates scenarios with varying prompt strictness, executes the agent, and generates a comprehensive report detailing the compliance rate of tool calls against predefined workflow specifications using advanced LLM classification.

A comprehensive, agent-driven tool for scheduling and publishing content across multiple social media platforms. It centralizes publishing workflows for 13 major providers, including X, LinkedIn, Instagram, TikTok, and more. Users can manage multi-platform campaigns, upload media assets, validate post schedules, and monitor real-time delivery status from a single workspace.

This tool initializes a structured multi-agent swarm with built-in anti-drift configuration. It is designed for orchestrating complex, multi-file tasks that require coordinated efforts from three or more specialized AI agents, such as feature implementation, cross-module refactoring, or comprehensive security audits. It supports various topologies (hierarchical, mesh, etc.) to ensure robust and manageable parallel work execution across large teams.