tschuehly

Create and operate a durable, task-shaped HTML surface when a human needs to understand, inspect, edit, annotate, compare, decide, approve, or triage generated material with an agent. Use for visual review loops, decision sessions, media review, structured editors, reports, plans, batches, and other work that is clearer in an interactive surface than chat.

Run a documented grilling session in claudex with Claude leading the interview and the GPT/Codex-family grill-advisor challenging the opening design tree and auditing the final decisions. Use when the user wants Claude and Codex to stress-test a plan or design together while recording ADRs and domain terminology.

Compact the current conversation into a handoff document for another agent to pick up.

Watch a Quality Run Chapter execute in another session, read-only, and produce an evidence-first evaluation of how the agents behaved against the rules they were given. Use when a human wants a skill change, routing change, or orchestration change tested on a live Chapter and needs an independent record of what actually happened — not the Driver's own account of it.

Run /quality-loop under claudex with quota-aware Claude-spine and Codex-spine profiles. The selected family coordinates and implements while the other family supplies a persistent design advisor and fresh Correctness-vs-Design reviewer. Use for a substantial autonomous system build that needs cross-family scrutiny, explicit session boundaries, and resumable handoffs.

Turn an outcome, optional discovery brief, or existing GitHub Issue into an approved system design, autonomously implemented and adversarially reviewed semantic Chapters, and a human-readable System Story. Use when the user wants agents to build a substantial change while the human reviews architecture and behavior rather than every code diff, invokes /quality-loop, or resumes an active Quality Run.

Coordinate a lead agent, a persistent specialist or adapter, and a durable shared-state surface while keeping the lead thread thin. Use when work spans multiple feedback rounds, must survive compaction or session boundaries, or combines an agent-owned task with a delegated surface, reviewer, researcher, or advisor. Defines agent interaction and continuity; pair with model-orchestration for model selection and atelier for review surfaces.

How to combine the available models (Fable 5, Opus 4.8, Sonnet 5, Haiku 4.5, and GPT-5.6) by cognitive role on multi-step coding, research, design, and intensive audit tasks. Use when planning non-trivial work in a claudex or Codex proxy session, especially when deciding who gathers evidence, comprehends a large system, makes judgments, advises, implements, or independently reviews.

Split this repository's SPEC.md into thin, ordered, TDD-ready Markdown task slices. Use when the user asks to split, slice, break down, or create implementation task files from SPEC.md for use with the tdd-task skill.

Implement a feature or fix a bug using TDD in this repository: write a failing test first, implement the smallest passing change, refactor, and verify with the relevant Maven and frontend commands.

Build, modify, or review Preact v10 UI code. Use when working with Preact components, JSX, Vite setup, TypeScript config, hooks from preact/hooks, @preact/signals state, forms and controlled inputs, refs, context providers/consumers, routing, testing, SSR, debugging, preact/compat, or Preact ecosystem library choices.

Comprehensive PostgreSQL-specific table design reference covering data types, indexing, constraints, performance patterns, and advanced features

Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, or applications. Generates creative, polished code that avoids generic AI aesthetics.

Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Outputs a spec with user stories.

Comprehensive jOOQ DSL best practices for Kotlin/Java. Use when writing jOOQ queries, mapping results, handling transactions, or designing repository layers.

Use only when the user explicitly invokes the embabel-agent-builder skill by name or path. When invoked, route the request to the right Embabel agent-building phase: planning from a prompt/product/feature spec, current-docs research for implementation questions, implementation from an approved high-level spec, or an end-to-end spec-first build.

Field-tested knowledgebase distilled from AI Engineer conference talks for building AI software and building with AI — agents, coding agents, RAG/retrieval, evals, inference, context engineering, MCP/tools, models, voice, vision, security, AI product strategy. Use when answering a question or making a design decision about building AI/LLM systems and you want grounded patterns, tradeoffs, and failure modes with source citations; also use when another agent needs grounded AI-engineering knowledge.