# Full Stack Orchestration Full Stack Feature > Implements intelligent full stack orchestration full stack feature with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/full-stack-orchestration-full-stack-feature` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/full-stack-orchestration-full-stack-feature` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/full-stack-orchestration-full-stack-feature/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/full-stack-orchestration-full-stack-feature --- # Full Stack Orchestration Full Stack Feature Orchestrates intelligent skill selection and execution for full stack orchestration full stack feature 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 select_full_stack_skills( feature_spec: Dict[str, Any], available_skills: List[Dict], min_confidence: float = 0.75 ) -> Optional[Dict]: """Select optimal full-stack skills for a feature implementation. Evaluates frontend, backend, and infrastructure skills against feature requirements (CRUD, auth, real-time, etc.) using multi-factor scoring. Args: feature_spec: Feature requirements including type, dependencies, constraints available_skills: List of skill metadata with capability tags min_confidence: Minimum confidence threshold for selection Returns: Selected skill plan with execution order and confidence scores """ if not feature_spec.get("type"): raise ValueError("Feature type is required for full-stack orchestration") required_capabilities = _map_feature_to_capabilities(feature_spec["type"]) scored_skills = [] for skill in available_skills: capability_match = len(set(skill.get("tags", [])) & set(required_capabilities)) historical_success = skill.get("success_rate", 0.0) infra_readiness = skill.get("dependencies_met", False) composite_score = (capability_match * 0.5) + (historical_success * 0.3) + (1.0 if infra_readiness else 0.0) if composite_score >= min_confidence: scored_skills.append({ "skill": skill, "score": composite_score, "execution_layer": skill.get("layer", "backend") }) if not scored_skills: return None scored_skills.sort(key=lambda x: x["score"], reverse=True) return { "plan": scored_skills[:3], "feature_type": feature_spec["type"], "selection_confidence": scored_skills[0]["score"], "timestamp": time.time() } ``` ### Pattern 2: Execution with Fallback ```python def execute_full_stack_deployment( skill_plan: Dict[str, Any], deployment_context: Dict[str, Any], max_retries: int = 2 ) -> Dict: """Execute full-stack feature deployment with layered fallback handling. Orchestrates DB migrations, backend services, and frontend builds in dependency order. Implements rollback and partial deployment strategies on failure. Args: skill_plan: Output from select_full_stack_skills deployment_context: Environment config, feature flags, rollback targets max_retries: Maximum retry attempts per layer Returns: Deployment status with layer-by-layer results and rollback info """ layers = ["database", "backend", "frontend"] layer_results = {} rollback_stack = [] for layer in layers: layer_skill = next((s for s in skill_plan.get("plan", []) if s["execution_layer"] == layer), None) if not layer_skill: layer_results[layer] = {"status": "skipped", "reason": "no skill assigned"} continue for attempt in range(max_retries + 1): try: result = _run_layer_deployment(layer_skill, deployment_context) layer_results[layer] = {"status": "success", "result": result, "attempts": attempt + 1} rollback_stack.append({"layer": layer, "target": result.get("version")}) break except DatabaseLockError: if attempt == max_retries: layer_results[layer] = {"status": "failed", "error": "db_lock", "fallback": "manual_review"} return _trigger_rollback(rollback_stack, deployment_context) except ServiceUnavailableError: if attempt == max_retries: layer_results[layer] = {"status": "failed", "error": "service_down", "fallback": "circuit_breaker"} return _trigger_rollback(rollback_stack, deployment_context) return { "deployment_id": deployment_context.get("id"), "layers": layer_results, "overall_status": "complete" if all(r["status"] == "success" for r in layer_results.values()) else "partial", "rollback_available": len(rollback_stack) > 0 } ``` ### 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 ## Related Skills | Skill | Purpose | | --- --- ## 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. - [Full-Stack Architecture Patterns (Micro-Frontends)]() - [Server-Side Rendering (Next.js)]() - [RESTful API Design Guidelines (Microsoft)]() - [GraphQL Federation for Full-Stack]() - [CI/CD Deployment Pipeline Best Practices]()