Behavioral Modes

Behavioral Modes

Orchestrates intelligent skill selection and execution for behavioral modes 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 resolve_behavioral_mode(
    task_intent: str,
    current_context: Dict[str, Any],
    available_modes: List[Dict[str, Any]],
    min_confidence: float = 0.75
) -> Optional[Dict[str, Any]]:
    """Resolve the optimal behavioral mode for a given task intent.
    
    Applies domain-specific scoring based on:
    - Intent-to-mode semantic alignment
    - Contextual state constraints (e.g., cannot debug without code)
    - Historical success rates for similar task patterns
    - Mode dependency chains (e.g., planning -> coding -> review)
    
    Args:
        task_intent: Parsed natural language intent
        current_context: Agent state including active files, recent actions, error logs
        available_modes: List of mode definitions with triggers and constraints
        min_confidence: Minimum threshold for mode activation
        
    Returns:
        Selected mode dict with confidence score and routing metadata
    """
    if not task_intent or not available_modes:
        raise ValueError("Task intent and available modes are required")
        
    # Extract contextual constraints from current state
    context_flags = _extract_context_flags(current_context)
    
    best_mode = None
    best_score = 0.0
    
    for mode in available_modes:
        # Domain-specific scoring: intent alignment + state compatibility
        intent_score = _calculate_intent_alignment(task_intent, mode["triggers"])
        state_score = _validate_state_compatibility(mode["constraints"], context_flags)
        history_score = mode.get("historical_success_rate", 0.5)
        
        # Weighted composite score with domain penalties
        composite = (intent_score * 0.5) + (state_score * 0.3) + (history_score * 0.2)
        
        # Apply mode dependency penalty if prerequisite mode isn't active
        if mode.get("requires_mode") and mode["requires_mode"] != current_context.get("active_mode"):
            composite *= 0.7
            
        if composite > best_score and composite >= min_confidence:
            best_score = composite
            best_mode = mode
            
    if best_mode is None:
        return None
        
    # Return immutable snapshot with routing metadata
    return {
        "mode": best_mode["name"],
        "confidence": round(best_score, 3),
        "routing_context": {
            "intent_match": intent_score,
            "state_valid": state_score > 0.5,
            "timestamp": time.time()
        }
    }

Pattern 2: Execution with Fallback

def execute_behavioral_mode(
    target_mode: Dict[str, Any],
    execution_context: Dict[str, Any],
    fallback_hierarchy: List[str] = None
) -> Dict[str, Any]:
    """Execute a behavioral mode with domain-aware fallback routing.
    
    Implements mode-specific execution with automatic degradation:
    1. Attempt primary mode execution
    2. If blocked by constraints, fallback to next compatible mode
    3. If critical failure, escalate to planning/review mode
    4. Log state transitions for audit and confidence updating
    
    Args:
        target_mode: Mode definition from resolve_behavioral_mode
        execution_context: Task payload, file references, and agent state
        fallback_hierarchy: Ordered list of mode names to try on failure
        
    Returns:
        Execution result with mode transition metadata and confidence update
    """
    fallback_hierarchy = fallback_hierarchy or target_mode.get("fallback_chain", [])
    current_mode = execution_context.get("active_mode", "default")
    
    try:
        # Validate mode transition constraints
        _validate_mode_transition(current_mode, target_mode["name"])
        
        # Execute mode-specific logic
        result = _run_mode_logic(target_mode["name"], execution_context)
        
        # Update confidence based on execution outcome
        confidence_delta = _calculate_confidence_delta(result["success"], target_mode["name"])
        
        return {
            "success": True,
            "mode_executed": target_mode["name"],
            "previous_mode": current_mode,
            "result": result,
            "confidence_adjustment": confidence_delta,
            "execution_time_ms": time.time() * 1000
        }
        
    except ModeConstraintError as e:
        # Domain-specific fallback: try next mode in hierarchy
        if fallback_hierarchy:
            next_mode_name = fallback_hierarchy[0]
            next_mode = _resolve_mode_by_name(next_mode_name, execution_context)
            if next_mode:
                return execute_behavioral_mode(next_mode, execution_context, fallback_hierarchy[1:])
        raise ModeExecutionError(f"Mode {target_mode['name']} failed constraint check: {e}")
        
    except CriticalFailureError as e:
        # Escalate to high-level oversight mode
        escalation_mode = _find_escalation_mode(target_mode["name"])
        if escalation_mode:
            return execute_behavioral_mode(escalation_mode, execution_context, [])
        raise ModeExecutionError(f"Critical failure in {target_mode['name']}: {e}")

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

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.

• What is Multi-Agent? (LangChain Blog)
• LLamaIndex — Multi-Agent Frameworks
• Microsoft AutoGen — Conversable Agents
• Research: Behavioral Specialization in Multi-Agent Systems (NeurIPS)
• Anthropic — Constitutional AI & Behavior Control