Skill UI

This skill provides a comprehensive suite for executing load testing, stress testing, and performance benchmarking. Use it to simulate peak traffic, identify system bottlenecks, and verify compliance with Service Level Agreements (SLAs). It supports industry-standard tools like k6, Artillery, Locust, and JMeter for robust performance analysis.

Automates spike test setup workflows by offering guided steps, best practices, code snippets, and validations across load/stress benchmarking and monitoring scenarios.

Automates throughput calculator tasks for Performance Testing by offering step-by-step guidance, industry best practices, and production-ready code for load, stress, benchmarking, and monitoring scenarios.

Query BindingDB for measured drug-target affinities (Ki, Kd, IC50, EC50) via REST endpoints or downloaded TSVs to support target-centric drug discovery, SAR analysis, lead profiling, and benchmarking datasets.

Use this command to set up a structured multi-agent collaboration environment within AgentHub. It allows you to define the core task, specify the required number of agents, and configure detailed evaluation criteria (metrics, directions, and commands) for both quantitative benchmarking and qualitative LLM judging. This is the starting point for any agent competition or complex AI workflow.

This skill initializes a new autoresearch experiment. It guides the user through defining all necessary parameters, including the domain, target files, evaluation commands, metrics, and optimization direction. It is the essential starting point for automated optimization and performance benchmarking cycles.

Evaluates pitch decks slide by slide, benchmarking against the 11-slide VC template, scoring every section, flagging common killer patterns, and benchmarking versus famous decks for actionable fundraising feedback.

Executes a structured VC valuation workflow that ingests SaaS or startup metrics, runs DCF, comparable revenue multiples, and SaaS health models via Python, and synthesizes a defensible valuation, runway, and capital-efficiency narrative for users planning fundraising or benchmarking.

A comprehensive 7-stage workflow designed to manage, audit, and optimize GitHub repositories. This tool automates critical tasks including identifying hardcoded secrets, cleaning up junk files, performing deep documentation reviews, benchmarking against competitors, and validating the final state, transforming messy codebases into production-ready assets.

A comprehensive tool for establishing performance baselines across multiple dimensions. It measures Core Web Vitals (LCP, CLS, INP), API latency (p50, p95, p99), and development build efficiency. Essential for detecting performance regressions before merging code, ensuring stability in CI/CD pipelines, and comparing alternative stack implementations.

This skill is designed for running comprehensive evaluations of Hugging Face Hub models on local hardware. It supports multiple evaluation frameworks, including `inspect-ai` and `lighteval`, and provides flexibility in backend selection (e.g., `vllm`, Transformers, or `accelerate`) to optimize for performance and compatibility. Use this for local GPU benchmarking, smoke testing, and backend selection, but note that it does not handle remote job orchestration or result publishing.

A comprehensive guide and toolkit for measuring Go application performance. Covers the entire workflow: writing benchmarks, running tests with statistical rigor, profiling hot paths using pprof, comparing results with `benchstat`, and tracking regressions in CI. Helps engineers ensure reliable and measurable performance improvements.