Skill UI

This skill provides a detailed, side-by-side comparison of advanced LLM agent evaluation frameworks, such as ruflo and HAL. It helps researchers and developers identify capability gaps—including real code execution, full web browsing, and structured file parsing—and prioritize architectural improvements for robust agent benchmarking and performance tracking.

A sophisticated multi-agent development harness inspired by GANs. It separates product planning, code generation, and rigorous evaluation into three distinct agents (Planner, Generator, Evaluator). This creates an adversarial feedback loop, allowing projects to iteratively reach professional, production-grade quality far beyond what a single-agent workflow can achieve. Ideal for complex, full-stack applications.

A comprehensive 7-section report providing a deep assessment of a repository's health and readiness level. It calculates key metrics including risk score, agent topology recommendations, MCP surface area, test confidence, and publish readiness. This tool is vital for pre-mint reviews, detecting architectural drift, and enforcing strict CI/CD quality gates.

A powerful tool designed to scaffold sophisticated, custom AI agent harnesses using the metaharness CLI. It supports various templates (e.g., coding, devops, marketing) and AI hosts (e.g., Claude, Codex). Crucially, it operates with maximum safety, defaulting to a dry-run mode and refusing to write to the calling repository root, ensuring controlled and predictable development workflows.

Prevents AI style drift in legacy projects by scanning codebases for implicit conventions, resolving conflicts with users, and generating enforceable .ai-style-rules.md files with golden files, naming rules, and DONTs. Ideal for onboarding AI agents to hand-written legacy codebases or extracting unwritten coding rules.

This utility initializes a brand-new Ruflo project environment. It sets up all necessary underlying components, including MCP tools, hooks, and agent configurations. It is essential for developers starting a project in a fresh repository, allowing selection between standard, minimal, or full presets to bootstrap the entire system.

A sophisticated system designed to automatically select the optimal AI agent and model tier (e.g., Haiku, Sonnet, Opus) for any non-trivial task. It replaces manual decision-making by integrating learned patterns, predictive modeling, and a robust 3-tier routing mechanism. The process is fully auditable, providing a detailed routing rationale to ensure transparency and reliable execution.

This tool is designed to register a Cognitum Seed IoT device, establishing a secure agent bridge and fetching the device's identity. It resolves the specified or default endpoint for connectivity and handles initial trust level assignment. It is crucial for the initial setup, pairing, and status checking of IoT hardware deployments.

This pattern is designed to solve context limitations in complex multi-agent workflows. Instead of relying on a single, massive context dump, it implements a structured, multi-cycle loop (Dispatch -> Evaluate -> Refine -> Loop). This process iteratively refines the search query by scoring content relevance and identifying missing context gaps, ensuring sub-agents gather the most targeted and highly relevant information needed for tasks like debugging or feature implementation.

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 is designed to analyze source code by extracting core entities (classes, functions, modules, types) and mapping their complex relationships (imports, inheritance, calls, dependencies). It constructs a comprehensive Knowledge Graph (KG) in AgentDB. This is essential for detailed dependency analysis, understanding large codebase structures, and performing accurate impact assessments.

This skill helps optimize and verify latency-sensitive systems such as real-time dashboards, market data feeds, and streaming agents. It involves tracking p50/p95/p99 latency, freshness age, and queue depth. You will map hot paths and run live readbacks to ensure performance. Use this when throughput or data freshness matters in engineering contexts.