Skill UI

A comprehensive, AI-assisted methodology for diagnosing complex software errors in production environments. This skill guides users through the entire debugging lifecycle, from initial triage and gathering observability data (Sentry, DataDog, Jaeger) to generating hypotheses, selecting appropriate debugging strategies (Time-Travel, Chaos Engineering), and performing AI-powered root cause analysis. It culminates in proposing validated fixes and prevention measures.

This comprehensive skill guides users through the entire debugging lifecycle, from reproducing elusive bugs to tracing the root cause, implementing permanent fixes, and preventing future regressions. It covers advanced techniques like binary search, time travel debugging, and handling common patterns such as race conditions and null values, ensuring professional and systematic troubleshooting for any production issue.

This skill generates a comprehensive, structured medical summary card for quick access during emergencies, travel, or hospital visits. It extracts critical user data, including severe allergies, active medications, chronic conditions, implants, and emergency contacts. Supports multi-format output (HTML, JSON, PDF) and prioritizes information by medical urgency (P0-P2) for rapid deployment in critical situations.

Analyze travel health risks for destinations, recommend vaccines and travel medicine, and generate multilingual emergency medical cards using WHO/CDC-style guidance. Useful for pre-trip planning, risk assessment, and post-travel monitoring.

This skill provides a structured guide to querying Azure Monitor activity and sign-in logs using KQL. It is crucial for proactive threat hunting and security incident investigation. Users can detect suspicious administrative actions, including unauthorized role assignments, impossible travel, privilege escalation, and resource modifications within Azure environments. Ideal for SOC analysts and cloud security engineers.

Detects anomalous authentication patterns across identity logs by combining UEBA, statistical baselines, geo intelligence, and machine learning to flag impossible travel, brute-force, credential stuffing, and other compromised account behaviors for SOC or IAM teams.

Detect compromised AWS, Azure, and GCP credentials by correlating GuardDuty, Defender for Identity, and SCC Event Threat Detection findings with abnormal API activity, impossible travel, and credential abuse indicators for triage and incident response.

Performs UEBA-driven anomaly detection in SIEM environments to flag impossible travel, privilege abuse, anomalous timing, and data access deviations so SOC teams can investigate compromised accounts or insider threats.

Creates a Google Sheets spreadsheet prepopulated with headers, a sample expense entry, and sharing permissions so teams can immediately start logging and reviewing travel costs.

A comprehensive guide to ensuring robust state management for LangGraph agents. This skill addresses common checkpointing pitfalls (like missing thread_id or non-JSON serializable state) and details best practices for implementing reliable state persistence using MemorySaver or PostgresSaver. It covers thread discipline, schema migration, and time-travel debugging for production-ready applications.

This skill provides comprehensive programmatic access to the Navan travel booking platform via REST API. It manages the entire trip lifecycle, including OAuth authentication, retrieving user and admin bookings, filtering by date/status, and handling pagination. Ideal for automating trip reporting, syncing data to internal systems, or building advanced travel dashboards.

This skill guides users through optimizing corporate travel spending using the Navan platform. It covers analyzing historical booking data via REST API, configuring granular travel policies (flight/hotel caps), enforcing negotiated rates, tracking unused travel credits, and utilizing the Rewards program to maximize savings and ensure compliance for corporate expense management.