Skill UI

Automatically reverse-engineers any codebase (frontend, backend, or fullstack) into a comprehensive Product Requirements Document (PRD). It analyzes complex elements like routes, components, API integrations, state management, and business logic across major stacks (React, Vue, Next.js, Django, FastAPI). The resulting PRD is detailed enough for both product managers (understanding 'what') and engineers/AI agents (understanding 'how' to reconstruct the system).

This tool allows users to systematically evaluate and rank the outputs of multiple agents within an AgentHub session. It supports metric-based evaluation (running custom commands for measurable metrics like speed or resource usage), or an advanced LLM Judge Mode. The LLM Judge compares results based on criteria like correctness, simplicity, and overall quality, helping users determine the best-performing agent for the task.

This command merges the results of the highest-ranked agent from a specified or latest session into the base branch. It automates the finalization of a development cycle by ensuring a clean and traceable history. Functionality includes merging the winner's branch, archiving all losing agents' commits using Git tags, and cleaning up temporary worktrees.

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.

This workflow bridges the gap between an abstract experiment plan and concrete, executable code. It reads structured experiment roadmaps (e.g., EXPERIMENT_PLAN.md), automatically generates or modifies necessary code scripts, handles prerequisite setup (like cloning base repositories), deploys the experiments to compute resources (GPU), and meticulously collects initial results. It includes optional steps for cross-model code review, ensuring robustness and correctness before execution. Ideal for moving from research hypotheses to measurable, running results.

This skill guides the process of evaluating research results against intended scientific claims. After running experiments, it systematically collects data from various sources (W&B, logs, etc.), performs a deterministic evidence pre-check to detect hallucinated evidence, and finally uses a Codex judgment to determine if the results support the claims, requiring next actions like pivoting or supplementing the findings.

This comprehensive guide covers idiomatic C# and modern .NET best practices for building robust, high-performance, and maintainable applications. Topics include immutability, dependency injection, explicit coding, proper async/await usage, the Options pattern, and the Result pattern. Essential reading for developers designing services with ASP.NET Core.

Generates implementation-ready JSON-LD schema markup for 10 types (e.g., FAQPage, Product, Article). It validates all fields against Google rich result eligibility rules, flags missing data with fix instructions, and provides clean, ready-to-paste script blocks to optimize search engine visibility and achieve rich results.

A robust, production-tested pattern for coordinating multiple specialized AI agents. The orchestrator decomposes complex tasks, routes them to the most suitable specialist, prevents redundant or conflicting work (anti-duplication), and enforces mandatory quality gates (e.g., running tests, checking file diffs) to verify results before marking a task as complete. It also includes heartbeat monitoring for task progress.

Analyzes Excel and CSV uploads with DuckDB, inspecting schemas, running SQL, summarizing statistics, and exporting results so users can explore structured data efficiently.

This skill provides a comprehensive framework for measuring Go application performance. It guides users through writing robust benchmarks using `testing.B`, running tests with various flags (`-benchmem`, `-cpuprofile`), interpreting complex profiling data (pprof), statistically comparing results using tools like `benchstat`, and setting up CI regression detection. It is essential for deep performance analysis and making statistically sound optimization decisions.

A powerful Go library providing type-safe monadic types like Option, Result, and Either. It helps developers adopt functional programming patterns, ensuring that impossible states (like nil pointers or unhandled errors) are unrepresentable at the type level. This is essential for building robust, reliable, and highly composable Go services.