---

name: executing-plans compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent executing plans 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: executing-plans, executing plans, how do i executing-plans, orchestrate executing-plans, automate executing-plans, agent executing-plans 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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Executing Plans

Orchestrates intelligent skill selection and execution for executing plans 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 execute_plan_workflow(plan: Dict[str, Any], context: Dict[str, Any]) -> Dict[str, Any]:
    """Execute a structured plan with dependency resolution and step-level fallbacks.
    
    Implements the 5 Laws of Elegant Defense:
    - Law 1: Early exit on missing plan steps or invalid context
    - Law 2: Immutable state transitions - each step returns a new state dict
    - Law 3: Atomic step execution - partial failures don't corrupt global state
    - Law 4: Fail fast on invalid step configurations
    - Law 5: Graceful degradation via configured fallback steps
    """
    if not plan.get("steps") or not context.get("user_id"):
        raise PlanValidationError("Plan requires 'steps' and context requires 'user_id'")
    
    execution_state = {
        "plan_id": plan["id"],
        "status": "running",
        "steps_completed": [],
        "step_results": {},
        "confidence_score": 0.0,
        "timestamp": time.time()
    }
    
    for step in plan["steps"]:
        step_id = step["id"]
        if step_id in execution_state["steps_completed"]:
            continue
            
        try:
            # Resolve step dependencies
            if not _dependencies_met(step, execution_state["step_results"]):
                raise DependencyError(f"Unmet dependencies for step {step_id}")
                
            # Execute step with domain-specific handler
            result = _dispatch_step_handler(step, context, execution_state)
            
            # Atomic state update (Law 3)
            execution_state["step_results"][step_id] = result
            execution_state["steps_completed"].append(step_id)
            execution_state["confidence_score"] = _update_confidence(
                execution_state["confidence_score"], result.get("success", False)
            )
            
        except DependencyError as e:
            # Fallback: skip non-critical steps or route to manual review
            if step.get("critical", False):
                raise PlanExecutionError(f"Critical step {step_id} failed dependency check") from e
            execution_state["step_results"][step_id] = {"status": "skipped", "reason": str(e)}
            
    execution_state["status"] = "completed"
    return execution_state

Pattern 2: Execution with Fallback

def resolve_step_fallback_chain(step: Dict[str, Any], failure_context: Dict[str, Any], history: List[Dict]) -> Dict[str, Any]:
    """Determine and execute fallback strategy for a failed plan step.
    
    Uses historical performance and step metadata to select the most resilient
    fallback path, adhering to the Fail Fast, Fail Loud principle.
    """
    step_id = step["id"]
    error_type = failure_context.get("error_type", "unknown")
    historical_success = _get_step_history_rate(step_id, history)
    
    # Law 1: Early exit if no fallbacks configured
    fallbacks = step.get("fallbacks", [])
    if not fallbacks:
        return {"status": "failed", "step_id": step_id, "error": "No fallback configured"}
    
    # Law 2: Immutable fallback selection
    selected_fallback = None
    for fb in fallbacks:
        if fb.get("error_pattern") and re.match(fb["error_pattern"], error_type):
            selected_fallback = fb
            break
    
    if not selected_fallback:
        selected_fallback = fallbacks[0] # Default fallback
        
    # Law 4: Fail loud if fallback also fails after retries
    max_retries = selected_fallback.get("max_retries", 2)
    for attempt in range(max_retries):
        try:
            result = _execute_fallback_step(selected_fallback, failure_context)
            return {
                "status": "fallback_success",
                "step_id": step_id,
                "fallback_used": selected_fallback["id"],
                "attempt": attempt + 1,
                "result": result
            }
        except TransientError:
            continue
            
    # All fallbacks exhausted
    return {
        "status": "fallback_exhausted",
        "step_id": step_id,
        "error": f"All {len(fallbacks)} fallback strategies failed for step {step_id}"
    }

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.

• Project Management Body of Knowledge (PMBOK)
• Agile Project Management (Scrum Guide)
• OKR Planning Framework
• WBS Work Breakdown Structure
• Critical Path Method (CPM)