Skill UI

A comprehensive tool for designing, generating schemas, and evaluating complex multi-agent systems. It guides users through defining system requirements, selecting optimal orchestration patterns (Supervisor, Swarm, Pipeline), generating provider-ready tool schemas (Anthropic/OpenAI), and analyzing execution logs for performance bottlenecks, cost, and latency. This ensures robust, production-grade autonomous workflows.

This framework provides a comprehensive, structured approach for making critical architectural decisions. It guides users through requirements analysis, detailed trade-off evaluation, and documenting decisions using Architecture Decision Records (ADRs). It is essential when analyzing or designing complex system architectures to ensure simplicity, robustness, and maintainability.

A detailed methodology for identifying Cross-Site Scripting (XSS) vulnerabilities across web applications. It guides systematic testing for Reflected, Stored, and DOM-based XSS, covering payload bypass, output encoding evaluation, and assessing Content Security Policy (CSP) protection. Essential for OWASP WSTG testing and client-side security audits.

A lightweight CLI tool for systematically comparing various coding AI agents (such as Claude Code, Aider, and Codex) head-to-head. It measures crucial metrics—pass rate, API cost, execution time, and consistency—on reproducible tasks defined via YAML manifests. Use this to objectively select the best agent for your codebase or to validate model updates before production deployment.

An automated verification system designed for critical healthcare application deployments (EMR/EHR). This harness enforces stringent patient safety standards by running multiple mandatory test gates, including Clinical Decision Support System (CDSS) accuracy, Protected Health Information (PHI) leakage prevention, and data integrity checks. A single failure in critical gates automatically blocks deployment, ensuring regulatory compliance and optimal patient safety in the CI/CD pipeline.

This skill provides expert guidance on treating prompts as first-class infrastructure. It covers prompt registry design, systematic versioning, A/B testing, and building comprehensive evaluation pipelines. It ensures that prompt changes are rigorously tested and deployed safely, preventing quality regressions in production AI features at scale.

This tool generates a comprehensive hiring rubric and technical interview scorecard for evaluating software engineers at specific levels and roles. It defines clear behavioral anchors, structured technical questions, and system design criteria, ensuring consistent, unbiased evaluation across all interviewers, and providing a full debrief agenda.

A lightweight CLI tool for systematically comparing various coding AI agents (such as Claude Code, Aider, and Codex) head-to-head. It measures crucial metrics—pass rate, API cost, execution time, and consistency—on reproducible tasks defined via YAML manifests. Use this to objectively select the best agent for your codebase or to validate model updates before production deployment.

A formal framework designed for evaluating AI-assisted code generation sessions. It implements Development Oriented Evals (EDD) principles, allowing users to define capability and regression criteria. The system supports multiple evaluation types—code-based, model-based, and human review—and tracks reliability using advanced metrics like pass@k, ensuring robust quality control for AI agents.

An automated verification system designed for critical healthcare application deployments (EMR/EHR). This harness enforces stringent patient safety standards by running multiple mandatory test gates, including Clinical Decision Support System (CDSS) accuracy, Protected Health Information (PHI) leakage prevention, and data integrity checks. A single failure in critical gates automatically blocks deployment, ensuring regulatory compliance and optimal patient safety in the CI/CD pipeline.

A comprehensive guide for researchers and reviewers designing or auditing Automated Software Engineering (ASE) evaluations. It outlines the necessary rigor, including the use of real subject systems, establishing fair and runnable tool baselines, performing targeted ablations (especially for LLMs), defining explicit oracles, and ensuring full data and model provenance to guarantee reproducible and scientifically sound results.

A comprehensive guide for authors developing systems papers for major conferences (like ATC). It details how to build a reproducible story by pinning all environments (hardware, OS, toolchain, workload, code), providing turnkey one-command execution paths, and preparing a runnable, anonymized artifact package for academic review and evaluation.