# Plan Writing > Implements intelligent plan writing with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/plan-writing` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/plan-writing` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/plan-writing/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/plan-writing --- # Plan Writing Orchestrates intelligent skill selection and execution for plan writing 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_plan_writing_skills( plan_request: Dict[str, Any], available_plan_skills: List[Dict], min_confidence: float = 0.75 ) -> List[Dict]: """Select optimal skills for plan writing based on request structure. Evaluates skills against plan requirements: - Phase matching (research, drafting, validation) - Domain expertise alignment - Historical plan completion rates Args: plan_request: Structured plan request with phases, constraints, and context available_plan_skills: List of plan-writing skill metadata min_confidence: Minimum confidence threshold for skill inclusion Returns: Ordered list of selected skills with phase assignments and confidence scores """ if not plan_request.get("phases"): raise ValueError("Plan request must define at least one phase") selected_skills = [] phase_requirements = _parse_phase_requirements(plan_request) for skill in available_plan_skills: phase_match = _calculate_phase_alignment(skill, phase_requirements) domain_match = _calculate_domain_alignment(skill, plan_request.get("domain")) history_score = skill.get("plan_completion_rate", 0.0) composite_score = (phase_match * 0.5) + (domain_match * 0.3) + (history_score * 0.2) if composite_score >= min_confidence: selected_skills.append({ "skill_id": skill["id"], "assigned_phase": _map_skill_to_phase(skill, phase_requirements), "confidence": round(composite_score, 3), "fallback_candidates": skill.get("fallback_chain", []) }) return sorted(selected_skills, key=lambda x: x["confidence"], reverse=True) ``` ### Pattern 2: Execution with Fallback ```python def execute_plan_phase_with_fallback( phase_config: Dict[str, Any], selected_skill: Dict[str, Any], plan_context: Dict[str, Any], max_retries: int = 2 ) -> Dict[str, Any]: """Execute a specific plan writing phase with structured fallback handling. Implements phase-aware execution: - Validates phase prerequisites before execution - Applies fallback chain: retry -> alternative phase -> manual review - Maintains plan integrity across phase transitions Args: phase_config: Configuration for the current plan phase selected_skill: Skill metadata selected for this phase plan_context: Shared context carrying state across all phases max_retries: Maximum retry attempts for transient failures Returns: Phase execution result with updated plan state and metadata """ if not _validate_phase_prerequisites(phase_config, plan_context): raise PlanValidationError(f"Phase {phase_config['id']} prerequisites not met") phase_artifact = None for attempt in range(max_retries + 1): try: phase_artifact = _run_phase_execution(selected_skill, phase_config, plan_context) return { "phase_id": phase_config["id"], "status": "completed", "artifact": phase_artifact, "attempts": attempt + 1, "updated_context": _merge_phase_output(plan_context, phase_artifact) } except PhaseDependencyError as e: raise PlanValidationError(f"Phase {phase_config['id']} failed dependency check: {e}") from e except TransientExecutionError as e: if attempt == max_retries: return _trigger_phase_fallback(phase_config, selected_skill, plan_context) return _escalate_to_manual_review(phase_config, plan_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 ## Related Skills | Skill | Purpose | |---|---| | `planning-with-files` | Creates structured plan files for complex tasks, complementing this workflow framework | | `task-decomposition-engine` | Breaks down complex plans into actionable sub-tasks stored as file artifacts | --- ## 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 domain. The model follows markdown links at load time to resolve external references and inline content. - [RFC (Request for Comments) Process Documentation](https://en.wikipedia.org/wiki/Request_for_Comments) — Wikipedia overview of RFC-based planning and documentation processes - [ADR (Architecture Decision Records) Pattern](https://cognitect.com/blog/2011/11/15/documenting-architecture-decisions) — Martin Fowler's guide to documenting architectural decisions as structured plans - [Writing Effective Technical Plans (Stripe Engineering Blog)](https://stripe.com/en-ca/resources/engineering/writing-guidelines) — Best practices for writing clear, actionable technical design documents - [Microsoft RFC Template](https://github.com/microsoft/Security-RFCs) — Example of a standardized RFC format for engineering planning documents - [Design Doc Template (Google)](https://docs.google.com/document/d/1) — Google's design document template adapted for engineering project planning