# Dynamic Replanner > Implements intelligent dynamic replanner with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/dynamic-replanner` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/dynamic-replanner` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/dynamic-replanner/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/dynamic-replanner --- # Dynamic Replanner Orchestrates intelligent skill selection and execution for dynamic replanner 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 evaluate_and_select_skill( request: Dict[str, Any], skill_registry: List[Dict[str, Any]], confidence_store: Dict[str, float], min_threshold: float = 0.75 ) -> Optional[Dict[str, Any]]: """Evaluate available skills against a dynamic request using multi-factor scoring. Implements Law 2 (Parse at boundary) by extracting features before scoring. Implements Law 1 (Early Exit) by validating registry and request upfront. """ if not request.get("intent") or not skill_registry: raise ValueError("Request intent and skill registry are required") intent_vector = _extract_intent_features(request["intent"]) best_match = None best_score = 0.0 for skill in skill_registry: # Multi-factor scoring: text similarity + historical confidence + availability text_sim = _cosine_similarity(intent_vector, skill.get("trigger_vectors", [])) hist_conf = confidence_store.get(skill["id"], 0.5) is_available = skill.get("status") == "active" and not skill.get("deprecated", False) if not is_available: continue weighted_score = (text_sim * 0.5) + (hist_conf * 0.3) + (0.2 if skill.get("low_latency", False) else 0.0) if weighted_score > best_score and weighted_score >= min_threshold: best_score = weighted_score best_match = { "skill_id": skill["id"], "score": weighted_score, "factors": {"text_sim": text_sim, "history": hist_conf, "available": True} } if best_match is None: return None # Law 3: Return new structure, never mutate registry return {**best_match, "evaluated_at": time.time()} ``` ### Pattern 2: Execution with Fallback ```python def execute_with_adaptive_fallback( selected_skill: Dict[str, Any], execution_context: Dict[str, Any], fallback_registry: List[Dict[str, Any]], confidence_store: Dict[str, float] ) -> Dict[str, Any]: """Execute a selected skill with adaptive fallback chains and confidence tracking. Implements Law 4 (Fail Fast/Loud) by raising on invalid states. Implements continuous learning by updating confidence_store post-execution. """ if not selected_skill or not execution_context.get("inputs"): raise ExecutionError("Missing skill selection or execution inputs") attempts = 0 max_attempts = 2 current_skill = selected_skill while attempts <= max_attempts: try: result = _invoke_skill_handler(current_skill["skill_id"], execution_context) # Update confidence based on success confidence_store[current_skill["skill_id"]] = min(1.0, confidence_store.get(current_skill["skill_id"], 0.5) + 0.1) return { "status": "success", "skill_used": current_skill["skill_id"], "result": result, "attempts": attempts + 1, "updated_confidence": confidence_store[current_skill["skill_id"]] } except TransientNetworkError: attempts += 1 if attempts > max_attempts: break continue except InvalidStateError as e: # Law 4: Fail loud on invalid state, do not retry raise ExecutionError(f"Invalid state in {current_skill['skill_id']}: {e}") from e # Fallback chain: try alternative skills from registry for alt_skill in fallback_registry: if alt_skill["id"] != selected_skill["skill_id"]: try: result = _invoke_skill_handler(alt_skill["id"], execution_context) confidence_store[alt_skill["id"]] = max(0.0, confidence_store.get(alt_skill["id"], 0.5) - 0.05) return {"status": "fallback_success", "skill_used": alt_skill["id"], "result": result} except Exception: continue # Final fallback: human escalation return {"status": "escalated", "reason": "all automated fallbacks exhausted", "context": execution_context} ``` ### 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 | | --- --- ## 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. - [HTN Planning in AI (Wikipedia)]() - [Task and Motion Planning Survey]() - [LLM Task Decomposition Methods]() - [ReAct: Reasoning and Acting]() - [Self-Refine: Iterative Refinement with Self-Feedback]()