Bdistill Behavioral Xray

Bdistill Behavioral Xray

Orchestrates intelligent skill selection and execution for bdistill behavioral xray 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 analyze_behavioral_trace(
    trace_data: Dict[str, Any],
    skill_registry: List[Dict],
    confidence_threshold: float = 0.75
) -> Dict[str, Any]:
    """Analyze behavioral xray trace and route to optimal skill.
    
    Implements Law 2 (Parse at boundary) by validating trace schema.
    Implements Law 1 (Early Exit) for malformed or incomplete traces.
    """
    if not trace_data or "agent_actions" not in trace_data:
        raise ValueError("Trace must contain agent_actions array")
        
    # Parse & validate trace features (Law 2)
    parsed_trace = _normalize_trace(trace_data)
    behavioral_features = {
        "error_rate": sum(1 for a in parsed_trace if a.get("status") == "error") / max(len(parsed_trace), 1),
        "avg_latency_ms": sum(a.get("duration_ms", 0) for a in parsed_trace) / max(len(parsed_trace), 1),
        "confidence_drift": _calculate_confidence_drift(parsed_trace)
    }
    
    # Multi-factor scoring against skill registry
    routed_skill = None
    best_score = 0.0
    
    for skill in skill_registry:
        # Domain-specific scoring: match trace patterns to skill triggers
        pattern_match = _match_trace_patterns(parsed_trace, skill.get("triggers", []))
        historical_perf = skill.get("success_rate", 0.5)
        availability = 1.0 if skill.get("status") == "healthy" else 0.0
        
        composite_score = (pattern_match * 0.5) + (historical_perf * 0.3) + (availability * 0.2)
        
        if composite_score > best_score and composite_score >= confidence_threshold:
            best_score = composite_score
            routed_skill = {
                "name": skill["name"],
                "score": composite_score,
                "routing_reason": f"pattern_match={pattern_match:.2f}, perf={historical_perf:.2f}"
            }
            
    if not routed_skill:
        return {"status": "no_match", "trace_features": behavioral_features}
        
    # Law 3: Return new structure, never mutate trace
    return {
        "status": "routed",
        "selected_skill": routed_skill,
        "trace_features": behavioral_features,
        "timestamp": time.time()
    }

Pattern 2: Execution with Fallback

def orchestrate_xray_execution(
    routed_result: Dict[str, Any],
    execution_context: Dict[str, Any],
    fallback_chain: List[str] = None
) -> Dict[str, Any]:
    """Execute behavioral xray with adaptive fallback chain.
    
    Implements Law 4 (Fail Fast/Loud) for invalid execution states.
    Implements Law 1 (Early Exit) for critical trace corruption.
    """
    fallback_chain = fallback_chain or ["historical_batch_xray", "human_review"]
    
    if routed_result.get("status") != "routed":
        raise ValueError("Cannot execute without valid skill routing")
        
    target_skill = routed_result["selected_skill"]["name"]
    trace_data = execution_context.get("trace_data")
    
    for attempt, fallback_target in enumerate([target_skill] + fallback_chain):
        try:
            # Domain-specific execution: run xray analysis on behavioral trace
            if fallback_target == target_skill:
                analysis_result = _run_realtime_xray(trace_data, target_skill)
            elif fallback_target == "historical_batch_xray":
                analysis_result = _run_historical_batch_xray(trace_data)
            elif fallback_target == "human_review":
                analysis_result = _generate_human_review_ticket(trace_data)
            else:
                analysis_result = _run_generic_xray(trace_data, fallback_target)
                
            # Law 3: Atomic result construction
            return {
                "status": "success",
                "skill_used": fallback_target,
                "analysis": analysis_result,
                "attempts": attempt + 1,
                "confidence": analysis_result.get("confidence_score", 0.0)
            }
            
        except TraceCorruptionError as e:
            # Law 4: Fail immediately on invalid trace state
            raise SkillExecutionError(f"Trace corruption in {fallback_target}: {e}") from e
        except TransientAnalysisError:
            # Fallback to next strategy
            continue
            
    # Law 4: Fail loud if all fallbacks exhausted
    return {
        "status": "failed",
        "skill_used": target_skill,
        "error": "All fallback strategies exhausted",
        "trace_features": routed_result.get("trace_features")
    }

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.

• Behavioral Analysis Patterns in Psychology (APA)
• Computational Behavioral Science — Methods & Techniques
• X-Ray Testing in Security Analysis (OWASP)
• Cognitive Behavioral Frameworks for AI Systems (arXiv)
• Psychological Profiling in Human-Computer Interaction