Skill UI

A specialized reverse-engineering skill designed to extract comprehensive functional and non-functional specifications from existing, undocumented, or legacy codebases. It operates by analyzing code structure, tracing data flows, and identifying observable behaviors. The output is structured using EARS (Easy Approach to Requirements Syntax), providing clearer requirements documentation, architectural diagrams, and dependency maps for onboarding or system modernization.

A comprehensive checklist designed to ensure TwinMind integrations are deployed securely, stably, and compliantly in a production environment. It covers critical best practices across multiple domains, including authentication, data encryption (PII, GDPR), robust infrastructure setup (auto-scaling, load balancing), advanced monitoring (metrics, tracing, alerting), and full CI/CD deployment workflows. Use this when preparing for go-live or conducting production audits.

This guide provides a comprehensive setup for Vercel observability. It covers enabling built-in analytics, collecting detailed runtime logs, implementing structured logging, configuring external log drains (e.g., Datadog, Sentry), and integrating OpenTelemetry tracing. Use this when monitoring production deployments and setting up robust error tracking.

Audits GitHub Actions workflows that invoke AI coding agents to surface prompt injection and sandbox risks, tracing attacker-controlled inputs through env vars, reusable actions, and workflow references before deployment or code review.

A powerful skill for conducting structured, systematic code reviews for PRs and MRs. It goes beyond basic style checks, performing deep analysis such as blast radius tracing, comprehensive security scanning (SQLi, XSS, secrets), detecting breaking changes in APIs/DB schemas, and calculating test coverage delta. Ideal for reviewing large, complex, or security-sensitive code changes before merging.

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.

A comprehensive cybersecurity tool designed to analyze malicious VBA and XLM macros embedded within Microsoft Office documents (Word, Excel, PowerPoint). It facilitates deep forensic investigation by extracting, deobfuscating, and tracing the full attack chain, including identifying auto-execution triggers, payload download URLs, and file writing techniques. Useful for malware analysts investigating phishing campaigns and maldocs.

This methodology systematically deobfuscates multi-layered PowerShell malware. It utilizes Abstract Syntax Tree (AST) analysis, dynamic execution tracing, and specialized tools (like PSDecode) to recover hidden payloads, original commands, and C2 infrastructure from scripts obfuscated with Base64, string concatenation, or complex invocation tricks. Essential for incident response and deep malware analysis.

A systematic protocol for repairing broken features by scoping the module, tracing every dependency, diagnosing issues, fixing them, and verifying results—ideal when a feature needs end-to-end debugging rather than a quick patch.

A comprehensive guide on implementing advanced security observability using eBPF and Cilium Tetragon. This skill covers real-time tracking of process execution, network connections, and file system access directly in the kernel. Learn how to author TracingPolicy CRDs, utilize kprobe/tracepoint hooks for in-kernel filtering, and enforce runtime security policies (e.g., process killing) on Linux hosts or Kubernetes clusters.

This skill provides comprehensive observability tooling for Go services, covering the five essential signals: structured logging (slog), Prometheus metrics, OpenTelemetry tracing, continuous profiling, and RUM. Use it for proactive production monitoring, correlating logs with traces, setting up SLOs, and ensuring all new features are fully instrumented for robust diagnosis.

This skill guides the integration of Arize AX tracing into existing applications, especially complex LLM applications. It follows a structured, two-phase, agent-assisted flow: first analyzing the codebase (read-only) to understand dependencies, and then implementing instrumentation. It supports multi-language environments (Python, TypeScript, Java, Go) and integrates deeply with OpenTelemetry and various LLM providers, ensuring comprehensive observability without altering core business logic.