---

name: dynamic-replanner compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent dynamic replanner with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense license: MIT maturity: stable metadata: domain: agent output-format: analysis related-skills: agent-confidence-based-selector, agent-task-routing role: orchestration scope: orchestration triggers: dynamic-replanner, dynamic replanner, how do i dynamic-replanner, orchestrate dynamic-replanner, automate dynamic-replanner, agent dynamic-replanner archetypes:
  • orchestration
  • strategic anti_triggers:
  • brainstorming
  • vague ideation
  • single-agent monolith response_profile: verbosity: medium directive_strength: high abstraction_level: tactical version: "1.0.0"

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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

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

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 | |

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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