Blueprint

Blueprint

Orchestrates intelligent skill selection and execution for blueprint 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: Blueprint Orchestration & Skill Routing

def orchestrate_blueprint_request(
    user_request: str,
    skill_registry: List[Dict],
    execution_history: List[Dict]
) -> Dict:
    """Orchestrate a blueprint workflow by routing to the optimal skill chain.
    
    Applies the 5 Laws of Elegant Defense:
    - Law 1: Early exit on malformed requests
    - Law 2: Parse inputs at boundary, make illegal states unrepresentable
    - Law 3: Return new routing plan, never mutate registry
    - Law 4: Fail fast on missing dependencies
    """
    if not user_request or not user_request.strip():
        raise ValueError("Blueprint request cannot be empty")
        
    # Law 2: Parse & validate at boundary
    parsed_request = _parse_blueprint_intent(user_request)
    if not parsed_request.get("intent") or not parsed_request.get("required_params"):
        raise ValueError("Missing required blueprint intent or parameters")
        
    # Law 4: Validate dependencies before scoring
    available_skills = [
        s for s in skill_registry 
        if _check_dependencies_met(s.get("dependencies", []), execution_history)
    ]
    
    # Multi-factor scoring: trigger match + historical success + availability
    scored_candidates = []
    for skill in available_skills:
        trigger_match = _calculate_trigger_similarity(parsed_request["intent"], skill.get("triggers", []))
        historical_success = _get_historical_success_rate(skill["name"], execution_history)
        availability_score = 1.0 if skill.get("status") == "healthy" else 0.5
        
        composite_score = (trigger_match * 0.5) + (historical_success * 0.3) + (availability_score * 0.2)
        if composite_score >= 0.6:
            scored_candidates.append({
                "skill": skill,
                "score": composite_score,
                "confidence": composite_score * historical_success
            })
            
    if not scored_candidates:
        return {"status": "no_match", "fallback": "human_handoff", "reason": "No skills met threshold"}
        
    # Law 3: Return new structure, don't mutate
    best_match = max(scored_candidates, key=lambda x: x["score"])
    return {
        "status": "routed",
        "selected_skill": best_match["skill"]["name"],
        "confidence": best_match["confidence"],
        "routing_plan": {
            "primary": best_match["skill"]["name"],
            "fallback_chain": best_match["skill"].get("fallback_skills", []),
            "retry_policy": best_match["skill"].get("retry_config", {"max": 2})
        }
    }

Pattern 2: Blueprint Execution & Adaptive Fallback

def execute_blueprint_step(
    step_config: Dict,
    context: Dict,
    skill_registry: Dict[str, Callable]
) -> Dict:
    """Execute a blueprint workflow step with adaptive fallback and confidence tracking.
    
    Implements Fail Fast, Fail Loud (Law 4) with structured fallback chains.
    Updates confidence scores post-execution for adaptive routing.
    """
    skill_name = step_config.get("primary_skill")
    fallback_chain = step_config.get("fallback_chain", [])
    max_retries = step_config.get("retry_policy", {}).get("max", 2)
    
    # Law 1: Early exit on missing skill
    if skill_name not in skill_registry:
        raise KeyError(f"Blueprint step references unknown skill: {skill_name}")
        
    execution_log = []
    last_error = None
    
    for attempt in range(max_retries + 1):
        try:
            # Execute with strict input validation
            result = skill_registry[skill_name](context)
            
            # Law 3: Return new result structure
            execution_log.append({
                "attempt": attempt + 1,
                "status": "success",
                "latency_ms": result.get("latency_ms", 0)
            })
            
            # Update confidence for next routing decisions
            _update_skill_confidence(skill_name, success=True)
            return {
                "status": "completed",
                "skill": skill_name,
                "result": result.get("data"),
                "execution_log": execution_log
            }
            
        except TransientError as e:
            last_error = e
            execution_log.append({"attempt": attempt + 1, "status": "retry", "error": str(e)})
            if attempt == max_retries:
                break
        except InvalidStateError as e:
            # Law 4: Fail immediately on invalid state
            _update_skill_confidence(skill_name, success=False)
            raise BlueprintExecutionError(f"Invalid state in {skill_name}: {e}") from e
            
    # Fallback chain execution
    for fallback_skill in fallback_chain:
        if fallback_skill in skill_registry:
            try:
                fallback_result = skill_registry[fallback_skill](context)
                _update_skill_confidence(fallback_skill, success=True)
                return {
                    "status": "fallback_success",
                    "original_skill": skill_name,
                    "fallback_skill": fallback_skill,
                    "result": fallback_result.get("data"),
                    "execution_log": execution_log
                }
            except Exception as fb_err:
                execution_log.append({"fallback": fallback_skill, "status": "failed", "error": str(fb_err)})
                
    # Law 4: Fail loud with full context
    _update_skill_confidence(skill_name, success=False)
    raise BlueprintExecutionError(
        f"All attempts and fallbacks exhausted for {skill_name}. "
        f"Last error: {last_error}. Requires human review."
    )

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.

• Architecture Decision Records (ADR Pattern)
• Software Blueprint Patterns (Martin Fowler)
• C4 Model for Software Architecture
• UML Use Case Modeling Guide
• Design Documentation Best Practices (NIST)

Related Skills

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