# Writing Plans > Implements intelligent writing plans with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/writing-plans` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/writing-plans` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/writing-plans/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/writing-plans --- # Writing Plans Orchestrates intelligent skill selection and execution for writing 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 generate_writing_plan( request: Dict[str, Any], available_templates: List[Dict], min_confidence: float = 0.7 ) -> Optional[Dict]: """Generate an optimal writing plan based on request parameters. Evaluates content type, audience, tone, and length constraints against available writing templates to produce a structured execution plan. Args: request: User writing prompt with metadata (audience, tone, length, format) available_templates: Pre-defined writing plan templates min_confidence: Minimum match score required for plan selection Returns: Structured writing plan dict or None if no suitable template matches """ if not request.get("prompt") or not request.get("audience"): raise ValueError("Writing plan requires 'prompt' and 'audience' fields") parsed_request = _normalize_writing_request(request) best_plan = None best_score = 0.0 for template in available_templates: score = _calculate_plan_fit(parsed_request, template) if score > best_score and score >= min_confidence: best_score = score best_plan = template if best_plan is None: return None # Construct immutable plan structure return { "plan_id": str(uuid.uuid4()), "template": best_plan["name"], "sections": _generate_section_outline(parsed_request, best_plan), "constraints": parsed_request["constraints"], "confidence": best_score, "created_at": datetime.now().isoformat() } ``` ### Pattern 2: Execution with Fallback ```python def execute_writing_plan( plan: Dict[str, Any], context: Dict[str, Any], max_retries: int = 2 ) -> Dict[str, Any]: """Execute a structured writing plan with resilience mechanisms. Generates content section-by-section according to the plan. Implements fallback strategies when specific sections fail or exceed token limits. Args: plan: Validated writing plan structure context: Execution context (drafts, references, style guides) max_retries: Maximum retries per section before fallback Returns: Completed writing document with execution metadata """ if not plan.get("sections"): raise ValueError("Writing plan contains no sections to execute") draft = {"title": context.get("title", "Untitled"), "sections": []} execution_log = [] for section in plan["sections"]: for attempt in range(max_retries + 1): try: content = _generate_section_content(section, context) draft["sections"].append({ "heading": section["heading"], "content": content, "word_count": len(content.split()) }) execution_log.append({"section": section["heading"], "status": "success", "attempt": attempt + 1}) break except TokenLimitError: if attempt == max_retries: # Fallback: Generate condensed version content = _generate_condensed_section(section, context) draft["sections"].append({"heading": section["heading"], "content": content, "word_count": len(content.split()), "fallback": True}) execution_log.append({"section": section["heading"], "status": "fallback", "attempt": attempt + 1}) break except GenerationError as e: execution_log.append({"section": section["heading"], "status": "failed", "error": str(e)}) if attempt == max_retries: raise PlanExecutionError(f"Failed to generate section: {section['heading']}") from e return { "document": draft, "metadata": { "total_sections": len(draft["sections"]), "fallbacks_applied": sum(1 for s in draft["sections"] if s.get("fallback")), "execution_log": execution_log } } ``` ### 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. - [Google Developer Documentation Style Guide](https://developers.google.com/style) - [The Elements of Style — Strunk & White (4th Edition)](https://www.gutenberg.org/ebooks/37134) - [Microsoft Writing Style Guide](https://learn.microsoft.com/en-us/style-guide/welcome/) - [AP Stylebook — Journalism Writing Standards](https://www.apstylebook.com/) - [Chicago Manual of Style Online](https://www.chicagomanualofstyle.org/tools_cms6/faq.html) ## Related Skills | Skill | Purpose | |