Skill UI

This skill outlines a comprehensive, multi-agent orchestration workflow designed for sophisticated production issue resolution. It systematically combines AI code assistants, observability platforms (like Sentry/DataDog), and automated techniques (e.g., git bisect, distributed tracing) into a rigorous four-phase process: Issue Analysis, Root Cause Investigation, Fix Implementation, and Verification. It guides best practices for reducing Mean Time To Recovery (MTTR) and improving system resilience in complex, multi-system environments.

A comprehensive guide to setting up production-grade observability for LangChain applications. It integrates LangSmith tracing for zero-code debugging, custom callbacks for detailed metric collection (latency, token usage, error rate), and Prometheus exporters for robust alerting. This allows developers to monitor LLM pipelines, track performance metrics, and manage operational health in real-time.

A comprehensive diagnostic guide for resolving the 10 most common Langfuse integration errors. This resource covers root causes, specific error codes (like 401 Unauthorized or connection timeouts), and provides tested solutions for debugging missing traces, authentication failures, and general LLM observability issues.

A minimal example demonstrating how to implement basic tracing using the Langfuse SDK. It illustrates the hierarchy of traces, spans, and generations (LLM calls), providing code examples in TypeScript and Python for easy setup and integration testing.

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.

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 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 comprehensive skill for performing deep security audits and writing secure code in Go. It guides developers through identifying vulnerabilities like SQL injection, XSS, path traversal, and crypto misuse. The framework incorporates advanced techniques such as STRIDE/DREAD threat modeling, data flow tracing, and multi-stage review modes (Audit, Review, Coding), ensuring defense in depth for robust Go applications.

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.

A comprehensive methodology for pinpointing the root cause of complex bugs, regressions, or unexpected behavior in large codebases. Techniques include binary search debugging, Git bisect for tracking regressions across commits, strategic data/control flow logging, component isolation, and advanced breakpoint strategies (conditional breakpoints, watch expressions). Ideal for scenarios where the bug source is unclear or difficult to reproduce.

A comprehensive methodology for offensive security, red team intelligence gathering, and bug bounty reconnaissance. Covers domain recon, email harvesting, social media profiling, code leaks, infrastructure mapping, cryptocurrency tracing, and geospatial intelligence. Essential for building detailed attack-surface maps against organizations or individuals.