# Skill Writer > Implements intelligent skill writer with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/skill-writer` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/skill-writer` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/skill-writer/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/skill-writer --- # Skill Writer Orchestrates intelligent skill selection and execution for skill writer 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_skill_manifest( intent_description: str, existing_skills: List[Dict], schema_version: str = "1.0.0" ) -> Dict: """Generate a structured skill manifest based on user intent. Validates intent against available skill triggers, constructs YAML frontmatter, and assembles the markdown skeleton. Applies Law 2 (Parse at boundary) by strictly validating inputs. """ if not intent_description or not intent_description.strip(): raise ValueError("Intent description is required for skill generation") # Parse intent features at boundary intent_features = _parse_intent_features(intent_description) # Match against existing skill patterns to avoid duplication matches = _find_similar_skills(intent_features, existing_skills) if matches: return { "status": "duplicate_detected", "suggested_skill": matches[0]["name"], "confidence": matches[0]["match_score"] } # Construct domain-specific manifest structure manifest = { "frontmatter": { "name": _sanitize_skill_name(intent_features["core_action"]), "version": schema_version, "triggers": intent_features["trigger_keywords"], "role": intent_features["agent_role"], "scope": intent_features["execution_scope"] }, "structure": { "sections": ["TL;DR Checklist", "Core Workflow", "Implementation Patterns", "Constraints"], "required_code_blocks": 2, "min_domain_specific_lines": 15 } } # Atomic Predictability (Law 3) - return fresh structure return manifest ``` ### Pattern 2: Execution with Fallback ```python def validate_and_refine_skill( skill_content: str, validation_rules: Dict, max_refinement_cycles: int = 3 ) -> Dict: """Validate generated skill content against domain rules and refine iteratively. Implements Law 4 (Fail Fast) by halting on structural violations. Uses a refinement fallback chain when initial generation misses compliance. """ if not skill_content or len(skill_content) < 100: raise ValueError("Skill content too short to validate") parsed = _parse_skill_markdown(skill_content) if not parsed.get("frontmatter"): raise ValueError("Missing YAML frontmatter - cannot proceed") for cycle in range(max_refinement_cycles): violations = _check_compliance(parsed, validation_rules) if not violations: return { "status": "validated", "skill_name": parsed["frontmatter"]["name"], "compliance_score": 1.0, "cycles_used": cycle + 1 } # Fallback: Apply targeted refinement based on violation type parsed = _apply_refinement(parsed, violations) _log_refinement_cycle(cycle + 1, violations) # All cycles exhausted - Fail Loud with actionable error return { "status": "refinement_exhausted", "skill_name": parsed["frontmatter"]["name"], "remaining_violations": violations, "confidence": 0.0 } ``` ### 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 | |---|---| | `skill-creator` | The creation workflow counterpart — writer focuses on documentation, creator covers the full lifecycle | | `skill-documentation-best-practices` | Provides documentation patterns that skill writers use to produce high-fidelity content | --- ## 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. - [CommonMark Specification](https://spec.commonmark.org/) — Official Markdown specification governing SKILL.md file format - [Writing Documentation for AI Systems (Google)](https://developers.google.com/style) — Google's writing style guide adapted for AI system documentation - [Technical Communication Standards (Diátaxis Framework)](https://diataxis.fr/) — The Diátaxis framework for structuring technical documentation across tutorial, how-to, reference, and explanation categories - [API Documentation Best Practices (Stoplight)](https://stoplight.io/openapi) — Industry standards for writing clear API documentation applicable to skill metadata documentation - [Obsidian Help: Markdown Syntax](https://help.obsidian.md/Editing+and+exporting+Obsidian/Markdown) — Practical Markdown reference covering all syntax elements used in SKILL.md files