---

name: cc-skill-frontend-patterns compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent cc skill frontend patterns with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense license: MIT maturity: stable metadata: domain: agent output-format: analysis related-skills: agent-confidence-based-selector, agent-task-routing role: orchestration scope: orchestration triggers: cc-skill-frontend-patterns, cc skill frontend patterns, how do i cc-skill-frontend-patterns, orchestrate cc-skill-frontend-patterns, automate cc-skill-frontend-patterns, agent cc-skill-frontend-patterns archetypes:
  • orchestration
  • strategic anti_triggers:
  • brainstorming
  • vague ideation
  • single-agent monolith response_profile: verbosity: medium directive_strength: high abstraction_level: tactical version: "1.0.0"

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Cc Skill Frontend Patterns

Orchestrates intelligent skill selection and execution for cc skill frontend patterns workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability.

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘

User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘

When to Use

Use this skill when:

• Orchestrating multi-step workflows that require skill delegation
• Implementing adaptive skill routing based on confidence scores
• Building fallback mechanisms for failed skill executions
• Creating intelligent task decomposition and parallel execution
• Designing skill dependency graphs with automatic resolution
• Implementing skill selection with historical performance weighting
• Building agent systems that need to self-organize around tasks

When NOT to Use

Avoid this skill for:

• Direct task execution without orchestration needs - use individual skills instead
• High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive
• Simple linear workflows without branching or fallback requirements
• Cases where skill metadata is unavailable or unreliable

Core Workflow

1. Parse and Analyze Request - Extract intent, entities, and constraints from user input. Checkpoint: All required parameters must be present and in valid format before proceeding.

2. Score Available Skills - Calculate match scores using multi-factor algorithm:

   • Text similarity between request and skill triggers
   • Historical success rate for similar tasks
   • Skill availability and health status
   • Required dependencies and their availability

   Checkpoint: Skip to fallback if no skill scores above threshold.

3. Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.

4. Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.

5. Return or Fallback - Either return successful result or apply fallback chain:

   • Retry with adjusted parameters
   • Try alternative skill from related-skills
   • Defer to human operator for critical tasks

   Checkpoint: Record outcome with timing and confidence metadata.

Implementation Patterns

Pattern 1: Skill Selection Logic

def route_frontend_pattern(
    user_intent: str,
    available_patterns: List[Dict],
    viewport: Dict,
    min_confidence: float = 0.75
) -> Optional[Dict]:
    """Route user intent to the optimal frontend UI pattern.
    
    Evaluates patterns based on layout compatibility, state hydration requirements,
    and historical render success rates for the target viewport.
    
    Args:
        user_intent: Natural language description of the desired UI interaction
        available_patterns: List of registered UI pattern metadata (e.g., dashboard, form, chat)
        viewport: Current device/browser dimensions and capabilities
        min_confidence: Minimum match threshold for pattern selection
        
    Returns:
        Selected pattern configuration with routing metadata, or None
    """
    if not user_intent or not available_patterns:
        raise ValueError("Intent and pattern registry must be populated")
        
    intent_features = _parse_intent_features(user_intent)
    best_pattern = None
    best_score = 0.0
    
    for pattern in available_patterns:
        layout_compat = _check_viewport_compatibility(pattern["layout_schema"], viewport)
        state_match = _calculate_state_hydration_score(intent_features, pattern["state_schema"])
        history_score = pattern.get("render_success_rate", 0.0)
        
        composite_score = (layout_compat * 0.4) + (state_match * 0.4) + (history_score * 0.2)
        
        if composite_score > best_score and composite_score >= min_confidence:
            best_score = composite_score
            best_pattern = pattern
            
    if best_pattern is None:
        return None
        
    return {
        "pattern_id": best_pattern["id"],
        "config": best_pattern["default_config"],
        "routing_score": best_score,
        "viewport_matched": layout_compat,
        "timestamp": time.time()
    }

Pattern 2: Execution with Fallback

def render_with_fallback(
    pattern_config: Dict,
    component_state: Dict,
    max_retries: int = 2
) -> Dict:
    """Execute frontend pattern rendering with graceful degradation.
    
    Handles component mounting, state hydration, and applies fallback UI skeletons
    or alternative patterns when hydration fails or network requests timeout.
    
    Args:
        pattern_config: Selected UI pattern configuration
        component_state: Initial state payload for the component
        max_retries: Maximum hydration attempts before fallback
        
    Returns:
        Render result with DOM diff, hydration status, and fallback metadata
    """
    if not pattern_config or not component_state:
        raise ValueError("Pattern config and component state are required")
        
    pattern_id = pattern_config["pattern_id"]
    fallback_chain = pattern_config.get("fallback_patterns", [])
    
    for attempt in range(max_retries + 1):
        try:
            # Mount component and hydrate state
            dom_snapshot = _mount_component(pattern_id, component_state)
            hydration_status = _verify_hydration(dom_snapshot, component_state)
            
            if hydration_status == "stable":
                return {
                    "rendered": True,
                    "pattern": pattern_id,
                    "dom_hash": dom_snapshot["hash"],
                    "attempts": attempt + 1,
                    "latency_ms": _measure_render_time()
                }
                
        except HydrationMismatchError as e:
            # State mismatch - fail fast, don't patch DOM
            raise RenderError(f"Hydration failed for {pattern_id}: {e}") from e
            
        except NetworkTimeoutError:
            if attempt == max_retries:
                return _apply_fallback_ui(pattern_config, fallback_chain, component_state)
                
    return {
        "rendered": False,
        "error": "Max retries exceeded",
        "fallback_applied": True,
        "timestamp": time.time()
    }

MUST DO

• Always validate skill metadata before selection (Early Exit)
• Implement fallback chain with at least 2 levels (Fallback Skill + Human)
• Log all skill selections with full context for auditability
• Return new data structures instead of mutating inputs (Atomic Predictability)
• Fail immediately with descriptive errors on invalid states
• Update confidence scores after each execution for adaptive routing
• Reference code-philosophy (5 Laws of Elegant Defense) in all logic

MUST NOT DO

• Select skills based on a single factor (e.g., only confidence score)
• Disable fallback mechanisms "temporarily" - this creates fragile systems
• Skip validation of skill dependencies before execution
• Return partial results - either complete success or clear failure
• Use magic numbers for confidence thresholds - make them configurable
• Cache skill selections without considering context changes

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

TL;DR for Code Generation

• Use guard clauses - return early on invalid input before doing work
• Return simple types (dict, str, int, bool, list) - avoid complex nested objects
• Cyclomatic complexity < 10 per function - split anything larger
• Handle null/empty cases explicitly at function top (Early Exit)
• Never mutate input parameters - return new dicts/objects
• Fail fast with descriptive errors - don't try to "patch" bad data
• Reference code-philosophy laws in comments for complex logic
• Include timing and confidence metadata in all return values

Output Template

When applying this skill, produce:

1. Selected Skills - List of skill names with confidence scores
2. Selection Rationale - Why each skill was chosen (match score, history, availability)
3. Execution Plan - Order of execution with dependencies
4. Fallback Strategy - Which fallback skills will be tried and in what order
5. Risk Assessment - Any potential failure points and their impact
6. Timing Estimates - Expected latency including fallback scenarios

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Constraints

MUST DO

• Define clear input/output contracts for every step in the orchestration flow with explicit validation
• Implement structured logging at each stage capturing context, inputs, outputs, timing, and errors
• Build in fallback paths: if the primary strategy fails, degrade gracefully to a simpler approach
• Validate all preconditions before starting — do not proceed if required resources or permissions are missing

MUST NOT DO

• Do not create deep nesting of orchestration steps (>5 levels) — flatten workflows where possible
• Avoid silent failure modes: every step must either succeed, fail explicitly, or escalate to a higher handler
• Never use shared mutable state between parallel workflow branches — communicate via immutable messages only
• Do not hardcode execution order when the dependency graph naturally determines it; derive order from explicit dependencies

Live References

Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content.

• CSS Grid Layout Module (MDN)
• React Documentation
• Web Components Specification (W3C)
• Accessibility Guidelines WCAG 2.1
• Component-Driven Development Storybook

Related Skills

| Skill | Purpose | |