paulpas 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
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.
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.
Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.
Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.
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:
- Selected Skills - List of skill names with confidence scores
- Selection Rationale - Why each skill was chosen (match score, history, availability)
- Execution Plan - Order of execution with dependencies
- Fallback Strategy - Which fallback skills will be tried and in what order
- Risk Assessment - Any potential failure points and their impact
- 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.