Skill UI

This skill exports cost-tracking telemetry data into formats suitable for external observability platforms like Prometheus, Grafana, and Datadog. It allows cost data, previously confined to the AgentDB namespace, to be visualized and monitored in real-time dashboards via Prometheus textfile collectors or structured JSON webhooks.

Automatically analyzes complex AI operations, including token usage patterns, model tier suitability, cache hit rates, and agent redundancy. It identifies potential waste and provides actionable, prioritized recommendations with estimated dollar savings to significantly reduce operational costs and improve efficiency across LLM pipelines.

This tool generates a detailed cost report, tracking token usage and associated USD costs across all AI agents and models. It provides crucial insights into budget utilization, breaking down spending by model (e.g., Opus, Sonnet), agent, and operational tier (Tier 1/2/3), helping teams manage spending and optimize resource allocation.

This guide outlines a comprehensive, agent-focused testing workflow for modern C++ (C++17/20) using GoogleTest and CMake/CTest. It teaches key concepts like the TDD cycle (Red-Green-Refactor), utilizing mocking (gmock) and fixtures for isolation, setting up robust CI/CD integration, and debugging complex issues using sanitizers (ASAN, TSAN) and coverage analysis. Use this when writing, updating, or ensuring the functional robustness of C++ codebases.

This tool scaffolds a new Claude Code plugin from scratch, generating a standardized directory structure that meets all architectural requirements. It automatically creates necessary components, including plugin.json, skills, commands, agents, ADR documentation, and smoke tests, ensuring a robust and compliant plugin development environment.

This tool enables the creation and management of Dynamic Agentic Architecture (DAA) agents. Unlike static agents, DAA agents possess the ability to learn, adapt their internal behavior based on real-time performance metrics, and continuously evolve through interactions. It facilitates knowledge sharing among multiple agents, ensuring systemic, continuous improvement in complex AI systems. Ideal for scenarios requiring high adaptability and evolving intelligence.

Build a production-ready, AI-powered data collection agent for any public source (job boards, prices, news, etc.). This agent automatically scrapes data, enriches it using free LLMs (like Gemini Flash), and stores the results in databases such as Notion, Sheets, or Supabase. It runs on a free schedule using GitHub Actions and learns from user feedback, making it ideal for continuous data monitoring and tracking.

A deterministic 'Stop hook' designed to enforce session hygiene for AI agents. It checks machine-verifiable facts such as file modification timestamps of learning logs, disk space, and predefined rationalization patterns. This gate ensures that complex tasks lead to captured knowledge, acting as a mechanical safeguard complementing AI self-auditing.

This comprehensive tool guides developers through the entire plugin ecosystem, helping them find and recommend the optimal plugins for any given workflow or task. It covers core coordination, multi-agent collaboration, advanced memory management (Vector DB, RAG), architectural design (ADR/DDD), security auditing, and modern development methodologies. Use it when starting a new project or optimizing existing development pipelines.

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 guide outlines Dynamic Workflow Mode, enabling coding agents to generate structured, task-local harnesses for complex, multi-step processes. It establishes a disciplined system for temporary workarounds, extracting reusable shared skills, and implementing control pane checkpoints. Use this when tasks require custom loops, multi-agent collaboration, or reliable handoff artifacts.

This guide helps users navigate and understand the complex components of the ECC (Everything Claude Code) ecosystem, including skills, commands, agents, hooks, and rules. It provides structured paths for new user onboarding, feature discovery, installation planning, and troubleshooting, ensuring answers are derived from the current repository state.