ork-assess
Assess a code change, design, architecture, workflow, or competing options against explicit criteria and evidence. Use when a request asks to assess, rate, compare, identify trade-offs, evaluate readiness, or decide whether an approach is good enough. Do not use for a full pull-request review or to implement a chosen solution.
git clone --depth 1 https://github.com/yonatangross/orchestkit /tmp/ork-assess && cp -r /tmp/ork-assess/plugins/ork-codex/skills/ork-assess ~/.claude/skills/ork-assessSKILL.md
# Ork Assess State the decision, scope, and criteria before scoring. Choose criteria appropriate to the task—normally correctness, maintainability, security, operability, testability, cost, and reversibility—and omit criteria that have no bearing on the decision. Collect evidence from the code, repository guidance, tests, or authoritative documentation. For a third-party library, framework, or service, the authoritative source is the context7 MCP server: call `resolve-library-id`, then `query-docs`. Separate facts, assumptions, and unknowns. Do not invent numeric precision: use a score only when it makes the comparison clearer, and attach the evidence and confidence to it. For high-impact choices, ask an independent read-only `ork_reviewer` or built-in reviewer to challenge the leading conclusion. End with a recommendation, conditions for approval, and concrete follow-up actions. Do not edit the target.
Accessibility patterns for WCAG 2.2 compliance, keyboard focus management, React Aria component patterns, cognitive inclusion, native HTML-first philosophy, and user preference honoring. Use when implementing screen reader support, keyboard navigation, ARIA patterns, focus traps, accessible component libraries, reduced motion, or cognitive accessibility.
Agent orchestration patterns for agentic loops, multi-agent coordination, alternative frameworks, and multi-scenario workflows. Use when building autonomous agent loops, coordinating multiple agents, evaluating CrewAI/AutoGen/Swarm, or orchestrating complex multi-step scenarios.
AI-assisted UI generation patterns for json-render, v0.app, Google Stitch, Bolt Cloud, and Cursor workflows. Covers prompt engineering for component and full-stack app generation, review checklists for AI-generated code, design token injection, refactoring for design system conformance, and CI gates for quality assurance. Use when generating UI components with AI tools, rendering multi-surface MCP visual output, reviewing AI-generated code, or integrating AI output into design systems.
Queries local analytics across OrchestKit projects for agent usage, skill frequency, hook timing, team activity, session replay, cost estimation, and model delegation trends. Privacy-safe with hashed project IDs. Supports time-range filtering and comparative analysis. Use when reviewing performance, estimating costs, or understanding usage patterns.
Animation and motion design patterns using Motion library (formerly Framer Motion) and View Transitions API. Use when implementing component animations, page transitions, micro-interactions, gesture-driven UIs, or ensuring motion accessibility with prefers-reduced-motion.
API contract design for REST and GraphQL, covering resource shape, URL and header versioning with deprecation windows, RFC 9457 Problem Details error handling, and OpenAPI specs. Use when specifying the wire contract an endpoint exposes, choosing a versioning scheme, or standardizing error response bodies across services. Framework-agnostic protocol layer, not runtime implementation.
ADR templates in the Nygard format with context, decision, consequences, and alternatives. Use when writing ADRs, recording an architectural decision, or evaluating options.
Architecture validation and patterns for clean architecture, backend structure enforcement, project structure validation, test standards, and context-aware sizing. Use when designing system boundaries, enforcing layered architecture, validating project structure, defining test standards, or choosing the right architecture tier for project scope.