Skill UI

This expert skill reviews the architecture and design of the Bitcoin Lightning Network protocol. It analyzes complex Layer 2 scaling tradeoffs, compares various channel factory approaches, and evaluates critical technical aspects—including trust models, on-chain footprint, consensus requirements, HTLC/PTLC compatibility, liveness guarantees, and watchtower support. Use this when assessing the cryptographic and systemic soundness of L2 blockchain protocols.

Guides product managers from stakeholder requests to structured Opportunity Solution Trees by extracting outcomes, enumerating opportunities, mapping solutions, and evaluating experiments to select the best proof of concept before building.

A comprehensive guide to building production-grade evaluation systems for Large Language Models (LLMs). This skill covers advanced methodologies, including Direct Scoring and Pairwise Comparison, alongside critical techniques for mitigating systematic biases (e.g., Position Bias, Length Bias). Learn how to select appropriate metrics and structure prompts to ensure reliable, consistent, and objective quality assessment across various AI applications.

This comprehensive guide outlines a rigorous, human-in-the-loop engineering workflow for leveraging advanced AI agents to complete complex implementation tasks. It mandates defining clear completion criteria, decomposing work into manageable units, and strategically routing models (Haiku, Sonnet, Opus) based on task complexity. The workflow emphasizes continuous evaluation, regression testing, security review of invariants and edge cases, and strict cost discipline to ensure robust and reliable software development using AI assistance.

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.

This pattern addresses the fundamental limitation of context window size by treating the filesystem as a persistent, external memory. Agents can write large outputs, plans, or findings to files, offloading them from the active context. Instead of carrying all information, agents use targeted search and retrieval tools to load only the necessary context on demand, drastically reducing token bloat and improving performance over long, complex tasks.

Guides growth engineers through evaluating, designing, and launching free marketing tools that drive traffic, leads, and backlinks without paid ads, covering idea scoring, UX, lead capture, and launch tactics.

A structured decision-making framework for high-stakes, painful business choices—such as layoffs or pivots—where there are no perfect options. It guides you through evaluating reversibility, long-term impact, and stakeholder communication to find the least wrong path.

This skill provides comprehensive tools for documenting and managing model performance on Hugging Face. It supports multiple methods for adding structured evaluation data, including extracting tables from README content, importing benchmark scores via APIs (like Artificial Analysis), and executing high-performance custom evaluations using advanced backends such as vLLM or lighteval. It ensures that all metadata is structured and compatible with model leaderboards and industry standards.

Use this command to set up a structured multi-agent collaboration environment within AgentHub. It allows you to define the core task, specify the required number of agents, and configure detailed evaluation criteria (metrics, directions, and commands) for both quantitative benchmarking and qualitative LLM judging. This is the starting point for any agent competition or complex AI workflow.

A comprehensive framework for global expansion, guiding businesses through market selection, evaluating entry modes (export, partnership, subsidiary), and executing localization strategies. Covers product adaptation, go-to-market planning, regulatory compliance, and operational setup for new international regions.

Osint Evals provides a structured way to assess open-source intelligence collection and analysis workflows, helping security teams benchmark their OSINT tooling and methodologies against predefined scenarios.