본문으로 건너뛰기
cccskills
무료GitHub에 공개

providing-feedback

Implements feedback and notification systems including toasts, alerts, modals, progress indicators, and error states. Use when communicating system state, displaying messages, confirming actions, or showing errors.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

설치 방법 보기

포함된 파일(19)

  • SKILL.md8.2 KB
  • assets/error-catalog.json9.6 KB
  • assets/message-templates.json6.7 KB
  • assets/timing-config.json5.2 KB
  • examples/confirmation-modal.tsx13.8 KB
  • examples/inline-validation.tsx16.1 KB
  • examples/progress-upload.tsx16.5 KB
  • examples/success-toast.tsx9.9 KB
  • outputs.yaml9.5 KB
  • references/accessibility-feedback.md16.8 KB
  • references/alert-patterns.md13.5 KB
  • references/empty-states.md15.0 KB
  • references/library-comparison.md14.0 KB
  • references/modal-patterns.md16.2 KB
  • references/progress-indicators.md18.0 KB
  • references/toast-patterns.md11.2 KB
  • scripts/calculate_timing.js10.0 KB
  • scripts/format_messages.py12.4 KB
  • scripts/generate_toast_manager.js10.5 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Providing User Feedback and Notifications

This skill implements comprehensive feedback and notification systems that enhance all other component skills by providing consistent patterns for communicating system state, displaying messages, and handling user confirmations.

When to Use This Skill

Activate this skill when:

  • Implementing toast notifications or snackbars
  • Displaying success, error, warning, or info messages
  • Creating modal dialogs or confirmation dialogs
  • Implementing progress indicators (spinners, progress bars, skeleton screens)
  • Designing empty states or zero-result displays
  • Adding tooltips or contextual help
  • Determining notification timing, stacking, or positioning
  • Implementing accessible feedback patterns with ARIA
  • Communicating any system state to users

Feedback Type Decision Matrix

Choose the appropriate feedback mechanism based on urgency and attention requirements:

Critical + Blocking       → Modal Dialog
Important + Non-blocking  → Alert Banner
Success/Info + Temporary  → Toast/Snackbar
Contextual Help          → Tooltip/Popover
In-progress              → Progress Indicator
No Data                  → Empty State

Quick Reference by Urgency

Urgency LevelComponentDurationBlocks Interaction
CriticalModal DialogUntil actionYes
ImportantAlert BannerUntil dismissedNo
StandardToast3-7 secondsNo
ContextualInline MessagePersistentNo
HelpTooltipOn hoverNo
ProgressSpinner/BarDuring operationOptional

Implementation Approach

Step 1: Determine Feedback Type

Assess the situation using these criteria:

  1. Urgency: How critical is the information?
  2. Duration: How long should it persist?
  3. Action Required: Does user need to respond?
  4. Context: Is it related to specific UI element?

Step 2: Choose Implementation Pattern

For Toasts/Snackbars:

  • Position: Bottom-right (recommended)
  • Duration: 3-4s (success), 5-7s (warning), 7-10s (error)
  • Stack limit: 3-5 maximum
  • See references/toast-patterns.md for detailed patterns

For Modal Dialogs:

  • Focus management: Trap focus within modal
  • Accessibility: ESC to close, proper ARIA labels
  • Backdrop: Click outside to close (optional)
  • See references/modal-patterns.md for implementation

For Progress Indicators:

  • <100ms: No indicator needed
  • 100ms-5s: Spinner with message
  • 5s-30s: Progress bar (determinate if possible)
  • 30s: Progress bar + time estimate + cancel

  • See references/progress-indicators.md for patterns

For Empty States:

  • Include: Illustration, headline, body text, CTA
  • Types: First use, zero results, error, permission denied
  • See references/empty-states.md for designs

Step 3: Implement with Recommended Libraries

Modern React Stack (Recommended):

npm install sonner @radix-ui/react-dialog

For Toasts - Use Sonner:

import { Toaster, toast } from 'sonner';

// In your app root
<Toaster position="bottom-right" />

// Trigger notifications
toast.success('Changes saved successfully');
toast.promise(saveData(), {
  loading: 'Saving...',
  success: 'Saved!',
  error: 'Failed to save'
});

For Modals - Use Radix UI:

import * as Dialog from '@radix-ui/react-dialog';

<Dialog.Root>
  <Dialog.Trigger>Open</Dialog.Trigger>
  <Dialog.Portal>
    <Dialog.Overlay />
    <Dialog.Content>
      <Dialog.Title>Confirm Action</Dialog.Title>
      <Dialog.Description>Are you sure?</Dialog.Description>
      <Dialog.Close>Cancel</Dialog.Close>
    </Dialog.Content>
  </Dialog.Portal>
</Dialog.Root>

See references/library-comparison.md for alternative libraries and selection criteria.

Step 4: Apply Accessibility Patterns

ARIA Live Regions for Announcements:

<!-- For non-critical notifications -->
<div role="status" aria-live="polite">
  File uploaded successfully
</div>

<!-- For critical alerts -->
<div role="alert" aria-live="assertive">
  Error: Failed to save
</div>

Focus Management for Modals:

  1. Save current focus before opening
  2. Move focus to first interactive element in modal
  3. Trap focus within modal (Tab cycles)
  4. Restore focus to trigger on close

See references/accessibility-feedback.md for complete patterns.

Step 5: Integrate Design Tokens

All feedback components use the design-tokens skill for consistent theming:

/* Example token usage */
.toast {
  background: var(--toast-bg);
  color: var(--toast-text);
  padding: var(--toast-padding);
  border-radius: var(--toast-border-radius);
  box-shadow: var(--toast-shadow);
  animation-duration: var(--toast-enter-duration);
}

Token categories used:

  • Colors: Toast, alert, modal, tooltip backgrounds
  • Spacing: Internal padding for all components
  • Typography: Font sizes for titles and messages
  • Shadows: Elevation for floating elements
  • Motion: Animation durations and easing

Notification Timing Guidelines

Auto-dismiss durations:

  • Success: 3-4 seconds
  • Info: 4-5 seconds
  • Warning: 5-7 seconds
  • Error: 7-10 seconds or manual dismiss
  • With action button: 10+ seconds or no auto-dismiss

Progress indicator thresholds:

  • <100ms: No indicator
  • 100ms-5s: Spinner
  • 5s-30s: Progress bar
  • 30s: Progress bar + cancel option

Resources

Scripts (Token-Free Execution)

  • scripts/generate_toast_manager.js - Generate toast configurations with timing and stacking
  • scripts/format_messages.py - Format user-facing messages based on context
  • scripts/calculate_timing.js - Calculate auto-dismiss timings

References (Detailed Documentation)

  • references/toast-patterns.md - Toast positioning, stacking, animations
  • references/alert-patterns.md - Alert banner implementations
  • references/modal-patterns.md - Modal dialogs with focus management
  • references/progress-indicators.md - Loading states and progress
  • references/empty-states.md - No-data and zero-result patterns
  • references/accessibility-feedback.md - ARIA patterns and focus management
  • references/library-comparison.md - Detailed library analysis

Examples (Implementation Code)

  • examples/success-toast.tsx - Success notification with Sonner
  • examples/confirmation-modal.tsx - Delete confirmation with Radix UI
  • examples/progress-upload.tsx - File upload with progress bar
  • examples/inline-validation.tsx - Form validation errors

Assets (Templates and Configs)

  • assets/message-templates.json - Reusable message templates
  • assets/error-catalog.json - Error code to message mappings
  • assets/timing-config.json - Timing recommendations

Cross-Skill Integration

This skill enhances all other component skills:

  • Forms: Validation feedback, success confirmations
  • Data Visualization: Loading states, error messages
  • Tables: Bulk operation feedback, action confirmations
  • AI Chat: Streaming indicators, rate limit warnings
  • Dashboards: Widget loading, system status
  • Search/Filter: Zero results, search progress
  • Media: Upload progress, processing status
  • Design Tokens: All visual styling via token system

Library Quick Comparison

LibraryTypeSizeBest For
SonnerToastSmallModern React 18+, accessibility
react-hot-toastToast<5KBMinimal bundle size
react-toastifyToast~16KBRTL support, mobile
Radix UIModalSmallDesign systems, headless
Headless UIModalSmallTailwind projects

Choose based on project requirements. See references/library-comparison.md for detailed analysis.

Key Principles

  1. Match urgency to attention: Don't use modals for non-critical info
  2. Be consistent: Same feedback type for similar actions
  3. Provide context: Explain what happened and what to do
  4. Enable recovery: Include undo, retry, or help options
  5. Respect preferences: Honor reduced motion settings
  6. Test accessibility: Verify with screen readers and keyboard

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Manage Linux systems covering systemd services, process management, filesystems, networking, performance tuning, and troubleshooting. Use when deploying applications, optimizing server performance, diagnosing production issues, or managing users and security on Linux servers.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

ancoleman/ai-design-components5252025년 12월 11일 업데이트

Data pipelines, feature stores, and embedding generation for AI/ML systems. Use when building RAG pipelines, ML feature serving, or data transformations. Covers feature stores (Feast, Tecton), embedding pipelines, chunking strategies, orchestration (Dagster, Prefect, Airflow), dbt transformations, data versioning (LakeFS), and experiment tracking (MLflow, W&B).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

ancoleman/ai-design-components5252025년 12월 11일 업데이트

Strategic guidance for designing modern data platforms, covering storage paradigms (data lake, warehouse, lakehouse), modeling approaches (dimensional, normalized, data vault, wide tables), data mesh principles, and medallion architecture patterns. Use when architecting data platforms, choosing between centralized vs decentralized patterns, selecting table formats (Iceberg, Delta Lake), or designing data governance frameworks.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

ancoleman/ai-design-components5252025년 12월 11일 업데이트

Design cloud network architectures with VPC patterns, subnet strategies, zero trust principles, and hybrid connectivity. Use when planning VPC topology, implementing multi-cloud networking, or establishing secure network segmentation for cloud workloads.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

ancoleman/ai-design-components5252025년 12월 11일 업데이트

Design comprehensive security architectures using defense-in-depth, zero trust principles, threat modeling (STRIDE, PASTA), and control frameworks (NIST CSF, CIS Controls, ISO 27001). Use when designing security for new systems, auditing existing architectures, or establishing security governance programs.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

ancoleman/ai-design-components5252025년 12월 11일 업데이트

Assembles component outputs from AI Design Components skills into unified, production-ready component systems with validated token integration, proper import chains, and framework-specific scaffolding. Use as the capstone skill after running theming, layout, dashboard, data-viz, or feedback skills to wire components into working React/Next.js, Python, or Rust projects.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

ancoleman/ai-design-components5252025년 12월 11일 업데이트

ancoleman의 스킬 모두 보기

이 스킬의 문제 신고