Skill UI

This meta-skill is designed to guide and execute structured developmental workflows, enabling continuous skill improvement and knowledge transfer. It is applicable when a task requires following a defined developmental process. Users must ensure all necessary inputs, permissions, and success criteria are provided, and must treat the output as a guide, supplementing professional validation.

A sophisticated, two-stage system designed to capture and internalize user corrections and learnings during ongoing conversations. It automatically detects patterns (e.g., "use X instead of Y") and queues them. Users can manually review and apply these learnings to a persistent knowledge base (CLAUDE.md), ensuring the AI continuously improves its knowledge and adherence to specific user context for future sessions.

Automatically evaluates Claude Code sessions at the Stop hook, identifies reusable patterns, and stores them as learned skills for future reuse, helping you capture troubleshooting steps, debugging techniques, and project conventions without manual effort.

This advanced system transforms session interactions into reusable, atomic knowledge units called "Instincts." By hooking into tool usage and session activity, it observes patterns (like preferred coding styles, error fixes, or repeated workflows). These instincts are then clustered and "evolved" into structured Skills, Commands, or specialized Agents, capturing deep contextual knowledge with weighted confidence scoring to enhance development productivity.

This tool deploys advanced machine learning techniques to detect anomalies within Operational Technology (OT) and Industrial Control Systems (ICS) environments. It establishes behavioral baselines by analyzing network traffic (Modbus, DNP3, OPC UA) and communication timing. Use cases include continuous monitoring, detecting zero-day deviations, and enhancing security monitoring platforms beyond standard signature-based intrusion detection.

This comprehensive guide details how to design, deploy, and measure a robust anti-phishing security awareness program. By combining regular simulations, interactive role-based learning modules, and metric tracking, organizations can build a strong security-conscious culture. It covers initial baseline assessments, continuous difficulty scaling, and compliance alignment using industry-leading tools like KnowBe4 and Proofpoint.

Captures coding session learnings by extracting corrections, successes, and anti-patterns with confidence scoring so teams can track, reinforce, and reuse technical knowledge across projects.

This skill provides continuous, structured monitoring of deep learning model training metrics. It analyzes data from platforms like WandB and local logs to detect critical anomalies such as NaN values, divergence, plateaus, and sudden spikes. By providing a clear decision (CONTINUE, WAIT, or STOP) and detailed evidence, it ensures compute resources are only spent on viable training runs.

This skill enforces output quality and evidence verification across security audits like SAST, SCA, and code reviews. It prevents suppression of valid findings and ensures reports meet quality thresholds before delivery. Includes components for self-critique, anti-rationalization, and continuous learning to maintain intellectual honesty and accuracy in security analysis workflows.

A comprehensive coaching layer designed to elevate users into power users of AI models. Upon first activation, it provides a structured glossary of high-impact techniques tailored to user needs. In subsequent turns, it actively monitors prompts, surfacing single, actionable tips to optimize interactions and maximize the AI's capabilities, ensuring continuous, guided learning.

This skill transforms the current workspace into a comprehensive, stateful teaching environment. It guides the user through deep, multi-session learning by structuring content into core components: a defined mission, reference materials, curated resources, structured lessons, and continuous learning records. It employs educational best practices (spaced repetition, retrieval practice) to ensure long-term knowledge retention.

This powerful skill leverages learned patterns and the current operational state to predict the most efficient next action. It guides complex workflows by offering immediate, high-confidence predictions. If confidence is low, it intelligently falls back to task priority lists. After every completed task, it utilizes a robust learning loop to update success patterns, ensuring continuous process improvement and cross-session memory recall.