Skills

Designs traces, metrics, structured events, privacy controls, dashboards, and alerts for LLM applications so teams can diagnose quality, cost, latency, and tool-use behavior.

Turns merged work, pull requests, issues, and user-visible behavior changes into accurate, audience-specific release notes with breaking-change and upgrade guidance.

Converts a design specification or screenshot into accessible, responsive production UI while preserving component boundaries, design tokens, semantic structure, and states.

Diagnoses failing browser and end-to-end tests by correlating assertions, traces, screenshots, network activity, timing, and application state into reproducible fixes.

Designs reliable JSON and typed outputs for LLM features, including schemas, validation, recovery paths, versioning, examples, and contracts for downstream consumers.

Guides containment, evidence collection, root-cause analysis, customer communication, and remediation when an AI agent takes an unsafe, incorrect, or unauthorized action.

Plans safe database schema and data migrations with backwards compatibility, staged rollout, validation, rollback, performance safeguards, and application coordination.

Превращает незнакомый репозиторий в краткое инженерное руководство по архитектуре, соглашениям, локальной настройке, ключевым путям выполнения и безопасным первым изменениям.

Evaluates retrieval quality for search and RAG systems using grounded test sets, relevance metrics, failure taxonomy, chunking experiments, and actionable remediation.

Designs trustworthy product analytics events, properties, identity rules, validation, privacy boundaries, dashboards, and governance for product and growth decisions.

Converts a loosely defined opportunity into testable product requirements, user journeys, scope boundaries, assumptions, risks, success metrics, and discovery questions.

Produces clear, reviewable architecture decision records that capture context, alternatives, trade-offs, consequences, rollout steps, and reversal criteria.

Designs low-risk secret rotation for applications and agents, covering inventory, overlapping credentials, automation, audit evidence, emergency revocation, and rollback.

Designs durable API evolution plans with compatibility guarantees, deprecation policy, version negotiation, consumer communication, migration tooling, and operational telemetry.

Создаёт воспроизводимые оценки AI-агентов, измеряющие успешность задач, корректность использования инструментов, обоснованность, задержку, стоимость и безопасное восстановление в реалистичных сквозных сценариях.

Produces blameless, actionable incident postmortems with a precise timeline, contributing factors, impact, decisions, corrective actions, owners, and measurable follow-through.

Reviews Model Context Protocol servers for excessive permissions, unsafe tool design, prompt injection exposure, secret handling, authorization gaps, and auditability.

Builds regression suites for prompts and agent instructions so teams can detect quality, safety, format, and tool-selection regressions before deploying changes.

Designs and implements secure, ergonomic Model Context Protocol servers with precise tool schemas, transport selection, authentication boundaries, error contracts, and tests.

Optimizes prompts and agent workflows for finite context windows by prioritizing evidence, compressing history, managing retrieval budgets, and measuring token-cost trade-offs.

Проверяет интерфейсы продуктов на соответствие практическим требованиям доступности, включая семантическую структуру, управление с клавиатуры, поведение фокуса, цветовой контраст, экранные дикторы и формы.