Skill UI

This comprehensive guide outlines best practices for building reliable and robust AI agent harnesses. It covers optimizing action spaces, defining structured tools, managing observation formats, and implementing essential error recovery contracts. Use these principles to dramatically improve agent completion rates, manage context budgets effectively, and transition AI prototypes into stable, production-grade autonomous systems.

This harness transforms standard code environments into fully persistent, autonomous agent systems. It leverages native features like scheduled tasks (Crons), remote dispatching, and structured memory storage (MCP) to enable continuous, self-directing automation. Ideal for building AI assistants that require long-term context, recurring workflows, and complex, multi-step operations, eliminating the need for external agent frameworks like AutoGPT.

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 audits the token consumption across all components (agents, skills, rules, MCP servers) within a Claude Code session. It analyzes token overhead, identifies bloat (e.g., overly long descriptions, redundant components), and generates a detailed context budget report. Use it to maintain optimal context window usage, improve performance, and prevent context saturation.

This skill analyzes completed code sessions to automatically extract reusable patterns, transforming raw interaction data into structured, learned skills. It allows the AI to continuously improve its domain knowledge by capturing techniques like error resolution, debugging methods, and workarounds, making it effective for advanced coding assistance and knowledge curation.

This skill runs comprehensive benchmarks against structural and adversarial corpora. It measures the performance and cost efficiency of a booster model compared to leading LLM baselines (Gemini, Anthropic). Results are persisted in verifiable JSON reports, crucial for auditing claims and verifying booster win rates before publishing.

This utility facilitates the creation and management of Dynamic Agentic Architecture (DAA) agents. Unlike static systems, DAA agents are designed to continuously learn from interactions, adapt their behavior based on performance metrics, and propagate knowledge across multiple agents. It provides comprehensive tools for agent creation, monitoring learning progress, checking performance, manual adaptation, and knowledge sharing, ensuring robust and evolving AI workflows.

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.

This advanced workflow is designed to generate comprehensive, fact-checked research reports on any complex topic. It systematically performs fan-out web searches across multiple defined angles, fetches multiple sources, and employs a rigorous adversarial verification system (3-vote) to validate claims. The final output is a highly cited, synthesized report, ideal for academic or critical business research.

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 tool executes a GEPA learning cycle, automating the evolution and optimization of policy prompts against a scored task corpus. Instead of relying on manual prompt iteration, it measures performance improvements, identifying the best working 'harness genome.' It is ideal for measuring sustainable improvements in task performance and supports a free, cost-estimating dry-run mode before committing to actual model calls.

This tool provides state-of-the-art semantic memory search across various knowledge domains. It supports multiple advanced retrieval strategies, including hybrid (sparse+dense) searching and Graph RAG for multi-hop reasoning. Key features include Mean Margin Ranking (MMR) for result diversity, recency weighting for timely information, and structured namespace filtering, ensuring highly relevant and context-aware results.