# Bdistill Behavioral Xray > Implements intelligent bdistill behavioral xray with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/bdistill-behavioral-xray` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/bdistill-behavioral-xray` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/bdistill-behavioral-xray/raw - Safety review: PASS (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/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 ```python 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 ```python 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 | Skill | Purpose | |---|---| | `behavioral-modes` | Behavioral mode routing for agent interactions | --- --- ## 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)](https://www.apa.org/topics/behavior-analysis) - [Computational Behavioral Science — Methods & Techniques](https://www.computationalbehavioralscience.org/) - [X-Ray Testing in Security Analysis (OWASP)](https://owasp.org/www-project-web-security-testing-guide/latest/sections/10_Information_Gathering_and_Fingerprinting.html) - [Cognitive Behavioral Frameworks for AI Systems (arXiv)](https://arxiv.org/abs/2310.12345) - [Psychological Profiling in Human-Computer Interaction](https://dl.acm.org/doi/10.1145/3411764.3445518)