Skill UI

This utility formats prompts for multiple Large Language Model providers (including Anthropic, OpenAI, Gemini, and Ollama) using standardized chat templates. Furthermore, it enables the creation and utilization of an HNSW index for advanced context retrieval, making it ideal for building robust Retrieval-Augmented Generation (RAG) pipelines.

This tool allows users to define and manage structured cognitive patterns for AI agents. It enables the codification of complex reasoning strategies, such as decision trees, Chain of Thought, and analogy mapping. Use it when agents need guided, systematic problem-solving approaches that can be executed, evaluated, and shared for continuous improvement and reliable decision-making.

This pattern defines a robust, continuous loop structure for autonomous agents. It integrates crucial components like quality gates, evaluation harnesses, and explicit recovery controls. It guides complex workflows by defining decision paths (e.g., PR control, RFC decomposition) and includes strategies to mitigate common failure modes such as infinite loop churn or cost drift, ensuring measurable progress in autonomous systems.

A comprehensive skill designed to systematically diagnose why an AI agent fails a specific task within the GAIA benchmark. It guides the user through loading the question trace, classifying the failure mode (e.g., Reasoning Miss, Tool Gap), and proposing targeted fixes. Use this tool when you need to root-cause a performance regression or an incorrect answer to resubmit a corrected agent.

Guides the user through the complete, multi-phase GAIA benchmark process. This workflow covers environment validation, cost estimation, running the benchmark, and generating a highly integrity-checked, HAL-compatible submission package required for leaderboards. It ensures both transport-integrity and earning-integrity.

This skill runs an automated evolution process designed to systematically optimize the seven core policy surfaces of an AI agent harness (e.g., planner, contextBuilder, reviewer). By mutating these policies and scoring the variants in a sandbox, it identifies crucial performance improvements without requiring expensive foundation model retraining. It is ideal for empirical configuration tuning and establishing optimized baselines.

This skill enables the cross-project sharing of learned AI patterns via IPFS. It allows developers to publish the intelligence learned by one project or machine and reliably fetch and apply those patterns to other disparate projects. This is crucial for institutionalizing knowledge and ensuring continuity when starting new, related projects.

This skill guides the architectural design of an Itô Data Atlas agent for advanced quantitative research. It facilitates market discovery, constructs candidate prediction-market baskets, drafts necessary parameter changes, and integrates human-in-the-loop review. It is strictly for workflow and architectural planning, and does not execute live trading orders.

This tool provides comprehensive configuration for running Large Language Models (LLMs) locally using RuVLLM. It supports initial model setup, specialized fine-tuning via MicroLoRA adapters for domain-specific tasks, and continuous, real-time adaptation using SONA. Use it when transitioning from base models to highly specialized, adaptive AI systems requiring precise parameter control.

This skill provides access to Anthropic's Claude Managed Agents, offering a robust cloud environment for executing complex, long-running AI workflows. It handles the entire agent lifecycle, from provisioning the container and environment (create), to executing multi-turn prompts, monitoring status, and ensuring proper cleanup (terminate). Ideal for critical, asynchronous tasks that require persistent state and advanced tools.

This memory bridge imports local auto-generated memories from Claude Code into a centralized AgentDB knowledge base. It generates ONNX embeddings, deduplicates entries using cosine similarity, and enables unified semantic search across all projects and sessions. It is essential for building comprehensive, context-aware AI applications.

This tool collects distributed trace spans to build a comprehensive, visual trace tree of an agent's task execution. It allows users to understand the exact flow, timing, and coordination between multiple agents involved in a task. Use it to identify performance bottlenecks, analyze the critical path, and synthesize a detailed narrative summary of complex multi-step workflows.