cc-skill-backend-patterns

# Cc Skill Backend Patterns Orchestrates intelligent skill selection and execution for cc skill backend 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 score_and_select_backend_skill( task_context: Dict[str, Any], skill_registry: List[SkillMetadata], min_confidence: float = 0.75 ) -> Optional[SkillExecutionPlan]: """Select optimal backend skill using multi-factor scoring aligned with Elegant Defense. Applies Law 1 (Early Exit) for invalid contexts. Applies Law 2 (Immutable State) by returning fresh plan objects. """ if not task_context.get("intent") or not skill_registry: raise ValueError("Missing intent or empty skill registry") parsed_intent = _normalize_intent(task_context["intent"]) best_plan = None best_score = 0.0 for skill in skill_registry: if not _is_skill_available(skill): continue text_match = _cosine_similarity(parsed_intent, skill.triggers) history_score = skill.success_rate * 0.4 availability_score = _calculate_load_penalty(skill.current_load) composite_score = (text_match * 0.5) + history_score + availability_score if composite_score > best_score and composite_score >= min_confidence: best_score = composite_score best_plan = SkillExecutionPlan( skill_id=skill.id, confidence=composite_score, parameters=_prepare_execution_params(task_context, skill) ) if best_plan is None: return None return best_plan ``` ### Pattern 2: Execution with Fallback ```python def execute_with_elegant_defense_fallback( plan: SkillExecutionPlan, execution_context: Dict[str, Any], fallback_chain: List[SkillId] ) -> ExecutionResult: """Execute skill with structured fallback chain per Law 4 (Fail Fast/Loud). Implements retry -> alternative skill -> human escalation. Returns immutable ExecutionResult with full audit metadata. """ attempt = 0 max_retries = 2 while attempt <= max_retries: try: raw_result = await _invoke_backend_skill(plan.skill_id, execution_context) validated = _validate_output_schema(raw_result, plan.skill_id) return ExecutionResult( success=True, skill_id=plan.skill_id, data=validated, confidence=plan.confidence, attempts=attempt + 1, latency_ms=_measure_latency() ) except SchemaValidationError as e: raise ExecutionError(f"Invalid output from {plan.skill_id}: {e}") from e except TransientBackendError as e: attempt += 1 if attempt > max_retries: break await _backoff_delay(attempt) # Fallback chain execution for alt_skill_id in fallback_chain: try: alt_result = await _invoke_backend_skill(alt_skill_id, execution_context) return ExecutionResult( success=True, skill_id=alt_skill_id, data=alt_result, confidence=0.6, attempts=attempt + 1, latency_ms=_measure_latency(), fallback_triggered=True ) except Exception as e: continue return ExecutionResult( success=False, skill_id=plan.skill_id, error="Fallback chain exhausted", confidence=0.0, attempts=attempt + 1, latency_ms=_measure_latency(), requires_human_review=True ) ``` ### 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. - [RESTful API Design Guide (Microsoft)](<https://learn.microsoft.com/en-us/styleguide/api-design-guide/>) - [GraphQL Specification (graphql.org)](<https://spec.graphql.org/October2021/>) - [gRPC Documentation](<https://grpc.io/docs/>) - [Microservices Patterns (Chris Richardson)](<https://microservices.io/patterns/microservices.html>) - [CQRS Pattern Reference](<https://docs.microsoft.com/en-us/azure/architecture/patterns/cqrs>) ## Related Skills | Skill | Purpose | |