# Cc Skill Frontend Patterns > Implements intelligent cc skill frontend patterns with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/cc-skill-frontend-patterns` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/cc-skill-frontend-patterns` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/cc-skill-frontend-patterns/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Web & Frontend - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/cc-skill-frontend-patterns --- # 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 ```python 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 ```python 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 --- --- ## 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 | |