Skill UI

Diagnoses full-stack failure modes in agent and LLM applications, including wrapper regressions, memory contamination, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings and code-first fixes for production agent systems.

Design a persistent multi-agent operating system on Claude Code with kernel routing, specialist agents, slash commands, file-based memory, and scheduled automation for long-lived projects.

This tool serves as a persistent memory and state manager for complex coding projects within the Claude environment. It allows users to initialize new projects with structured metadata, track detailed session progress (including summaries, decisions, and next steps), and save the entire context reliably. By ensuring project continuity across disconnected sessions, it drastically improves the flow and consistency of the development workflow.

This utility provides garbage collection for Claude Code configurations. It periodically scans various directories—including skills, memory files, hooks, permissions, and caches—to identify orphaned, stale, or redundant configuration items. Crucially, the process is designed to be non-destructive, always requiring explicit human confirmation for every deletion to ensure a safe, audited cleanup process.

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.

A comprehensive audit worker that integrates Open Infrastructure Architecture (OIA) manifest analysis, threat modeling, and MCP security scanning. It computes a composite worst-case severity score and persists time-stamped audit records in a memory namespace. Primarily designed for scheduled CI/CD pipelines to detect architectural drift and security regressions over time.

This skill executes the metaharness score to provide a comprehensive, multi-dimensional readiness scorecard for a codebase. It assesses critical dimensions like compilation confidence, task coverage, tool safety, and memory usefulness. Use it to evaluate if a repository is prepared for harness adoption before initiating development tools or framework setup.

Uses the Monitor tool to stream real-time events from a swarm system instead of inefficient polling. It provides live observability for key actions like agent spawns, task completions, memory writes, and health status. This is ideal for continuous monitoring and debugging of complex, distributed processes.

This utility diagnoses and automatically repairs common issues within the Ruflo installation environment. It performs comprehensive health checks on core dependencies like Node.js, npm, and git, while also validating configurations, daemon status, memory databases, and API keys. It provides targeted commands to resolve issues related to memory initialization, daemon startup, and configuration resets, ensuring a stable operational environment.

This pattern demonstrates how to build a robust, thread-safe data persistence layer in Swift using the 'actor' model. It combines fast in-memory caching with reliable file-backed storage, automatically eliminating data races and the need for manual locks. Ideal for offline-first applications and managing shared mutable state in modern Swift concurrency.

Tree Ring Memory is a local-first, framework-agnostic layer for managing durable knowledge in AI agents. It provides structured methods for recalling past decisions, recording evidence, auditing project state, and deliberately forgetting information, ensuring the core project memory remains clean and actionable without relying on raw conversation transcripts.

This tool performs a comprehensive audit of the project's stored auto-memory. It analyzes learned knowledge to identify actionable patterns for promotion to core rules, flag stale or outdated entries, and suggest opportunities for consolidating redundant knowledge. Use it after major development phases or when the memory knowledge base becomes crowded to maintain system knowledge quality.