executing-plans

# 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 ```python 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 ```python 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 | | --- --- ## 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)](<https://www.pmi.org/pmbok-guide-standards>) - [Agile Project Management (Scrum Guide)](<https://scrumguides.org/scrum-guide.html>) - [OKR Planning Framework](<https://www.atlassian.com/agile/project-management/okrs>) - [WBS Work Breakdown Structure](<https://en.wikipedia.org/wiki/Work_breakdown_structure>) - [Critical Path Method (CPM)](<https://en.wikipedia.org/wiki/Critical_path_method>)