paulpas 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
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.
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.
Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.
Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.
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:
- Selected Skills - List of skill names with confidence scores
- Selection Rationale - Why each skill was chosen (match score, history, availability)
- Execution Plan - Order of execution with dependencies
- Fallback Strategy - Which fallback skills will be tried and in what order
- Risk Assessment - Any potential failure points and their impact
- 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.