# Writing Skills > Implements intelligent writing skills with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/writing-skills` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/writing-skills` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/writing-skills/raw - Safety review: PASS (external: skill-scanner PASS, skillspector PASS) - 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-skills --- # Writing Skills Orchestrates intelligent skill selection and execution for writing skills 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_writing_skill( request: Dict[str, Any], available_writing_skills: List[Dict], style_guide: Dict[str, Any] ) -> Optional[Dict]: """Select optimal writing skill based on tone, format, and audience requirements. Evaluates writing-specific factors: - Tone alignment (formal, conversational, technical, persuasive) - Format requirements (markdown, HTML, plain text, structured JSON) - Audience complexity and domain expertise level - Historical readability scores and engagement metrics Args: request: User writing request with tone, format, and audience fields available_writing_skills: List of writing skill metadata style_guide: Active style guide configuration Returns: Selected writing skill with confidence score and applied style rules """ if not request.get("content") or not request.get("tone"): raise ValueError("Writing request requires 'content' and 'tone' fields") target_tone = request["tone"].lower() target_format = request.get("format", "markdown") audience_level = request.get("audience_level", "general") best_match = None best_score = 0.0 for skill in available_writing_skills: tone_match = _calculate_tone_similarity(target_tone, skill.get("supported_tones", [])) format_compatible = target_format in skill.get("supported_formats", []) audience_fit = _assess_audience_compatibility(audience_level, skill.get("target_audience")) # Weighted scoring for writing-specific criteria score = (tone_match * 0.4) + (format_compatible * 0.3) + (audience_fit * 0.3) score *= skill.get("historical_readability_score", 0.8) if score > best_score: best_score = score best_match = skill if best_score < 0.65: return None # Apply style guide rules to selected skill result = dict(best_match) result["applied_style_rules"] = style_guide.get("rules", []) result["confidence"] = round(best_score, 3) return result ``` ### Pattern 2: Execution with Fallback ```python def execute_writing_task( selected_skill: Dict, writing_context: Dict, fallback_styles: List[str] = ["neutral", "simple"] ) -> Dict: """Execute writing pipeline with style-aware fallback chain. Implements writing-specific resilience: - Validates tone/format constraints before generation - Falls back to simpler style guides on readability failure - Routes sensitive/complex content to human review - Tracks engagement and clarity metrics for adaptive learning Args: selected_skill: Output from select_writing_skill writing_context: Raw content, audience, and formatting requirements fallback_styles: Ordered list of alternative tone/style guides Returns: Generated text with metadata (readability, tone_match, fallback_used) """ content = writing_context.get("content", "").strip() if not content: raise ValueError("Writing context requires non-empty content") current_style = selected_skill.get("applied_style_rules", []) fallback_index = 0 while True: try: # Assemble prompt with style constraints prompt = _build_writing_prompt(content, current_style, writing_context) # Execute generation generated_text = _run_generation_model(prompt, selected_skill["model_endpoint"]) # Post-process: readability & tone validation readability_score = _calculate_flesch_kincaid(generated_text) tone_match = _measure_tone_alignment(generated_text, writing_context["tone"]) if readability_score < 40 or tone_match < 0.6: raise ReadabilityFailure(f"Score: {readability_score}, Tone: {tone_match}") return { "success": True, "output": generated_text, "readability_score": readability_score, "tone_match": tone_match, "fallback_used": fallback_index > 0, "skill_used": selected_skill["name"] } except ReadabilityFailure as e: if fallback_index >= len(fallback_styles): raise WritingExecutionError("All style fallbacks exhausted") from e current_style = _apply_style_override(fallback_styles[fallback_index]) fallback_index += 1 except SensitiveContentError: return { "success": False, "requires_human_review": True, "reason": "Content flagged for compliance review", "original_context": writing_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. - [Flesch–Kincaid Readability Tests](https://en.wikipedia.org/wiki/Flesch%E2%80%93Kincaid_readability_tests) - [Hemingway Editor Style Guide Principles](https://hemingwayapp.com/about/) - [Google Developer Documentation Style Guide](https://developers.google.com/style) - [AP Stylebook — Associated Press Writing Standards](https://www.apstylebook.com/) - [StyleGuide – GitHub's Markdown Writing Conventions](https://github.github.io/gfm/) ## Related Skills | Skill | Purpose | |