Skill UI

This skill provides a comprehensive framework for simulating and testing disaster recovery plans against ransomware threats. It guides users through measuring Recovery Time Objective (RTO) and Recovery Point Objective (RPO), validating clean backup restores, and hardening security controls to ensure organizational resilience. It is designed for tabletop exercises and compliance auditing, not for active incident response.

This skill involves creating structured specifications before writing any code to serve as a shared source of truth between developers and engineers. It is used when starting new projects, features, or significant changes where requirements are unclear or ambiguous. The workflow includes a scope check, specifying objectives, planning, task breakdown, and implementation with human reviews at each phase. It helps decompose complex requirements into capability maps and ensures clarity before coding begins.

Python framework for single- and multi-objective optimization with NSGA-II/III, MOEA/D, and SPEA2. Supports constraints, ZDT/DTLZ benchmarks, Pareto front analysis, and visualization for engineering design.

This skill integrates LangChain 1.0 and LangGraph 1.0 into standard OpenTelemetry (OTEL) backends (e.g., Jaeger, Honeycomb, Tempo). It solves critical observability gaps by ensuring correct prompt content capture, propagating spans across complex subgraphs, and providing five LLM-specific Service Level Objectives (SLOs) like latency and cost. Ideal for multi-tenant, compliance-sensitive, or existing OTEL stack environments where LangSmith is unsuitable.

This tool provides comprehensive management capabilities for Objectives and Key Results (OKR) within Lark. Users can view and edit entire OKR cycles, manage objectives, define key results, track progress records, adjust weights, and establish alignment relationships. It is essential for teams needing structured goal setting, performance tracking, and corporate alignment management.

This skill provides a structured framework for capturing, organizing, and maintaining persistent facts about the user. By segmenting facts into topic-specific files (e.g., coding preferences, tech stack, goals), it creates a reliable, objective knowledge base. This structure is crucial for AI agents and applications to achieve highly personalized interactions and provide accurate context.

This tool performs a rigorous Level 2 semantic epistemic review of Agent-Native Research Artifacts (ARAs). It assesses six critical dimensions—including evidence relevance, falsifiability, and methodological rigor—to determine the content's intellectual soundness. It is used after initial structural validation to provide objective, constructive criticism, scoring the work from Strong Accept to Reject.

A comprehensive OKR tracker dashboard designed to visualize team objectives and key results (KRs). It includes a quarter banner, multiple objective cards with progress bars, owner details, and a dedicated sidebar for at-a-glance KPIs, ideal for quarterly performance reviews and strategic planning.

This skill analyzes the comprehensive commit history between two branches to generate a concise, objective, and highly structured Pull Request (PR) description. It automatically groups changes by domain, summarizing the purpose, technical notes, and user impact, ensuring consistency and minimizing manual documentation effort.

This powerful skill pauses deep technical discussions to conduct a comprehensive, multi-dimensional reassessment. It analyzes conceptual drift, identifies unverified assumptions, and detects potential cognitive biases (e.g., confirmation bias, sunk cost fallacy). Use it when the conversation is becoming overly detailed, or when a strategic check-in is needed to ensure the focus remains on the core objective and the correct problem is being solved.

This skill converts complex, narrative technical documents, research excerpts, or natural language problem descriptions into a highly structured Mathematical Problem Specification (MPS). It systematically extracts variables, constraints, objectives, and uncertainty parameters. By adhering to a strict Zero-Inference Protocol, it ensures all extracted elements are strictly grounded in the source text, making it invaluable for formalizing real-world optimization, data science, and engineering problems.

This skill is designed to prevent AI coding assistants from exhibiting sycophantic agreement bias. When active, it forces a structured, critical assessment of user claims. The process involves extracting the core claim, assessing it independently with evidence (for/against), and concluding solely based on that assessment, ensuring objective and evidence-based responses.