Skill UI

A universal infrastructure for automatically evolving and optimizing AI agents across any domain. Using LLM-driven mutation and iterative solve-observe-evolve cycles, it enhances critical components like prompts, skills, and memory by benchmarking performance against measurable datasets, enabling the creation of self-improving AI systems.

This harness provides comprehensive, reproducible evaluation pipelines for complex LLM chains and agents (LangChain/LangGraph 1.0). It integrates golden dataset management, LangSmith evaluation, RAGAS metrics, deepeval LLM-as-judge, and structured agent trajectory analysis. Use it when establishing quality benchmarks for new chains, diagnosing performance regressions after model switches, or implementing CI/CD gates to prevent quality drops.

Combines a curated Japanese NLP resource dataset with live web research to produce an up-to-date trend report on models, tooling, benchmarks, embeddings, speech, and related subfields.

A comprehensive reference guide providing quantitative benchmarks and formulas for system architects. It covers user-to-RPS conversion, storage estimation, bandwidth calculation, known scale limits of major technologies (databases, queues, caches), and capacity planning for various user loads (1K DAU to 10M DAU). Essential for designing scalable, high-availability applications.

This comprehensive skill assesses pre-launch readiness across all organizational functions, including Product, Marketing, Support, Sales, and Data. It systematically checks feature completeness, performance benchmarks, operational readiness, and communication assets. The output provides a clear Go/Conditional Go/No-Go recommendation, a prioritized blockers list with owners, a detailed risk register, and a rationale, ensuring a robust and controlled product release.

Transforms complex raw product metrics into a clear, actionable health narrative. This skill analyzes key performance indicators (KPIs) across the entire user lifecycle—including acquisition, activation, engagement, and retention—against set OKRs and benchmarks. The output is a comprehensive report providing overall health status, key observations, hypothesized root causes for performance dips, and prioritized, actionable next steps with assigned owners.