Concise Planning

Concise Planning

Orchestrates intelligent skill selection and execution for concise planning 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 select_planning_strategy(
    task_request: Dict[str, Any],
    available_planners: List[Dict[str, Any]],
    complexity_threshold: float = 0.8
) -> Optional[Dict[str, Any]]:
    """Select optimal planning strategy based on task constraints and historical performance.
    
    Implements Law 1 (Early Exit) and Law 2 (Make Illegal States Unrepresentable):
    - Validates task structure before scoring
    - Filters out deprecated or incompatible planners
    """
    if not task_request.get("objective") or not task_request.get("constraints"):
        raise ValueError("Planning request requires 'objective' and 'constraints'")
        
    parsed_constraints = _normalize_constraints(task_request["constraints"])
    task_complexity = _estimate_complexity(task_request["objective"], parsed_constraints)
    
    best_strategy = None
    best_score = 0.0
    
    for planner in available_planners:
        if planner.get("status") != "active":
            continue
            
        match_score = _calculate_domain_match(task_request["objective"], planner["triggers"])
        history_score = planner.get("success_rate", 0.0) * 0.4
        complexity_fit = 1.0 - abs(task_complexity - planner.get("optimal_complexity", 0.5))
        
        total_score = (match_score * 0.5) + history_score + (complexity_fit * 0.3)
        
        if total_score > best_score and total_score >= complexity_threshold:
            best_score = total_score
            best_strategy = planner
            
    if best_strategy is None:
        return None
        
    return {
        "strategy": best_strategy["name"],
        "confidence": best_score,
        "estimated_steps": best_strategy.get("default_steps", 3),
        "fallback_level": best_strategy.get("fallback_depth", 2)
    }

Pattern 2: Execution with Fallback

def execute_planning_workflow(
    selected_strategy: Dict[str, Any],
    task_context: Dict[str, Any],
    max_fallback_depth: int = 2
) -> Dict[str, Any]:
    """Execute concise planning workflow with domain-specific fallback chains.
    
    Implements Law 4 (Fail Fast, Fail Loud) and Law 3 (Atomic Predictability):
    - Validates context before execution
    - Returns immutable plan structures
    - Applies structured fallbacks when planning constraints are violated
    """
    if not _validate_planning_context(task_context):
        raise PlanningValidationError("Missing required execution context")
        
    current_depth = 0
    plan_state = _initialize_plan_state(selected_strategy, task_context)
    
    while current_depth <= max_fallback_depth:
        try:
            plan_steps = _generate_steps(plan_state)
            validated_plan = _validate_plan_against_constraints(plan_steps, task_context["constraints"])
            
            return {
                "status": "success",
                "plan": validated_plan,
                "confidence": selected_strategy["confidence"],
                "depth_used": current_depth,
                "timestamp": time.time()
            }
            
        except ConstraintViolationError as e:
            if current_depth == max_fallback_depth:
                raise PlanningExecutionError(f"Plan failed after {max_fallback_depth} fallbacks: {e}") from e
            plan_state = _apply_fallback_adjustment(plan_state, e)
            current_depth += 1
            
        except ResourceExhaustionError:
            plan_state = _simplify_scope(plan_state)
            current_depth += 1
            
    return {
        "status": "deferred",
        "reason": "max_fallback_depth_exceeded",
        "pending_context": task_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

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.

• OKR Goal Setting Framework
• Agile Estimation Techniques (Planning Poker)
• Gantt Chart Methodology (PMI)
• WBS (Work Breakdown Structure) Guide
• Critical Path Method (CPM)

Related Skills

| Skill | Purpose | |