form-ux-architecture
Form UX Architecture
Coverage
Design form structure and validation behavior. Covers field grouping, labels as structure handoff, required vs optional decisions, progressive disclosure, defaults, input formats, client-side validation, server-side validation, validation timing, submit lifecycle, error recovery, multi-step forms, review steps, autosave, and high-risk data entry.
Philosophy
Forms are not data dumps. A form is a guided conversation that asks only for information the system truly needs, at the moment the user can answer it, with correction paths that preserve trust.
Client-side validation is a user-experience aid, not a security boundary. The server must validate every submitted field even when the client appears correct.
Method
More from jacob-balslev/skill-graph-skills
ai-native-development
Use when reasoning about agent autonomy levels, designing auto-improve loops, evaluating AI-generated code quality, or measuring agent productivity in an LLM-assisted codebase. Covers Karpathy's three eras of software (1.0 explicit / 2.0 learned / 3.0 natural-language), the vibe-coding-vs-agentic-engineering distinction, the 0–5 autonomy slider with task-type recommendations, the one-asset / one-metric / one-time-box AutoResearch loop, Software 3.0 productivity metrics, and the documented quality regressions of ungated AI-generated code (the 'vibe hangover'). Do NOT use for choosing a specific autonomy-loop topology (use `agent-engineering`), for the per-prompt authoring discipline (use `prompt-craft`), or for reviewing the AI-generated code that comes out of a Software 3.0 workflow (use `code-review`).
4ideation
Use when generating a wide range of solution concepts before converging on a direction, running structured idea-generation sessions, breaking out of solution fixation, or moving from divergent to convergent selection with explicit criteria. Do NOT use for collaborative engineering domain discovery (event-storming), solo deep technical design, or making final go/no-go investment decisions — those require different methods.
4frontend-architecture
Use when organizing a frontend codebase — module boundaries, component layering, state ownership, data-flow direction, and the separation between feature code and shared primitives. Do NOT use for visual design decisions, specific framework migration tactics, or backend API contract design.
4color-system-design
Use when designing a color system — palette construction, semantic color tokens, WCAG contrast ratios, perceptual uniformity in OKLCH/LCH, and light/dark mode parity. Do NOT use for single brand-color picks, runtime theme-switching mechanics, or non-color design tokens.
4agent-engineering
Use when designing or evaluating a production AI agent system, choosing a multi-agent coordination pattern (orchestrator/worker, fan-out, consensus, sequential chain, evaluator/optimizer), diagnosing coordination failures (claim races, silent stalls, context contamination, runaway loops), or auditing whether an agent loop is truly production-ready. Covers the four pillars (architecture and lifecycle, task decomposition, coordination patterns, production reliability), the six reliability requirements (observability, cost budgets, idempotency, failure recovery, safety caps, claim locks), the delegation decision framework with overhead crossover, and the most common anti-patterns. Do NOT use for prompt wording (use `prompt-craft`), per-call tool efficiency (use `tool-call-strategy`), context-stack design within a single agent (use `context-engineering`), or runtime debugging of a deployed system (use `debugging`).
4constraint-awareness
Use when prioritizing work in an AI-assisted codebase, designing agent autonomy levels, deciding what to automate vs keep manual, or evaluating whether a process/tool adds value. Covers Theory of Constraints for AI-era engineering: cheap code production, human review/validation/decision bottlenecks, Five Focusing Steps, constraint-aware process design, attention audits, and constraint-shift modeling. Do NOT use for task-effort estimation, backlog scoring with RICE/WSJF/ICE, or routing a task to a specific model.
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