# Behavioral Modes > Implements intelligent behavioral modes with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/behavioral-modes` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/behavioral-modes` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/behavioral-modes/raw - Safety review: pending - 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/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 ```python 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 ```python 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 | Skill | Purpose | |---|---| | `ask-questions-if-underspecified` | Clarification & underspecification handling | --- --- ## 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)](https://blog.langchain.dev/what-is-multi-agent/) - [LLamaIndex — Multi-Agent Frameworks](https://docs.llamaindex.ai/en/latest/module_guides/orchestration/agent_pipelines/multi_agent/) - [Microsoft AutoGen — Conversable Agents](https://microsoft.github.io/autogen/docs/FAQ/#how-does-autogen-support-multi-agent-conversations) - [Research: Behavioral Specialization in Multi-Agent Systems (NeurIPS)](https://arxiv.org/abs/2309.07894) - [Anthropic — Constitutional AI & Behavior Control](https://www.anthropic.com/research/build-effective-agent-systems)