---

name: full-stack-orchestration-full-stack-feature compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent full stack orchestration full stack feature 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: full-stack-orchestration-full-stack-feature, full stack orchestration full stack feature, how do i full-stack-orchestration-full-stack-feature, orchestrate full-stack-orchestration-full-stack-feature, automate full-stack-orchestration-full-stack-feature, agent full-stack-orchestration-full-stack-feature 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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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

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

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 | |

---

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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.

• 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