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 ```python 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 ```python 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](<https://www.atlassian.com/agile/project-management/okrs>) - [Agile Estimation Techniques (Planning Poker)](<https://www.agilenutshell.com/planningpoker>) - [Gantt Chart Methodology (PMI)](<https://www.pmi.org/learning/library/gantt-chart-techniques-project-schedule-5913>) - [WBS (Work Breakdown Structure) Guide](<https://www.projectsmart.co.uk/work-breakdown-structure.php>) - [Critical Path Method (CPM)](<https://en.wikipedia.org/wiki/Critical_path_method>) ## Related Skills | Skill | Purpose | |