# Skill Rails Upgrade > Implements intelligent skill rails upgrade with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/skill-rails-upgrade` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/skill-rails-upgrade` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/skill-rails-upgrade/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-rails-upgrade --- # Skill Rails Upgrade Orchestrates intelligent skill selection and execution for skill rails upgrade 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 evaluate_skill_rail_candidates( target_rail: str, candidate_skills: List[Dict], historical_metrics: Dict[str, float] ) -> List[Dict]: """Evaluate and rank skill candidates for a specific rail upgrade. Implements multi-factor scoring: text similarity, historical success rate, and system availability. Returns sorted list of viable candidates. """ scored_candidates = [] for skill in candidate_skills: # Calculate text similarity using token overlap (domain-specific) skill_triggers = set(skill.get("triggers", [])) target_features = set(target_rail.lower().split()) similarity = len(skill_triggers & target_features) / max(len(skill_triggers | target_features), 1) # Fetch historical performance from metrics store hist_success = historical_metrics.get(skill["id"], 0.0) # Check system availability and dependency health is_available = skill.get("status") == "active" and skill.get("dependencies_met", True) # Weighted scoring formula per orchestration policy weighted_score = (similarity * 0.4) + (hist_success * 0.4) + (float(is_available) * 0.2) if weighted_score >= 0.6: # Minimum threshold scored_candidates.append({ "skill_id": skill["id"], "name": skill["name"], "score": round(weighted_score, 3), "similarity": round(similarity, 3), "history": round(hist_success, 3), "available": is_available }) # Sort by score descending (Atomic Predictability - Law 3) scored_candidates.sort(key=lambda x: x["score"], reverse=True) return scored_candidates ``` ### Pattern 2: Execution with Fallback ```python def execute_rail_upgrade_with_resilience( selected_skill: Dict, upgrade_context: Dict, fallback_rails: List[str] ) -> Dict: """Execute a skill rail upgrade with built-in fallback mechanisms. Implements Fail Fast, Fail Loud (Law 4) and structured fallback chains. Handles validation, execution, and automatic rollback/switching. """ # Guard clause - Early Exit (Law 1) if not selected_skill or not upgrade_context.get("target_version"): raise ValueError("Missing required skill metadata or target version") # Parse input - Make Illegal States Unrepresentable (Law 2) validated_config = _validate_upgrade_config(upgrade_context, selected_skill) attempts = 0 max_attempts = 2 while attempts <= max_attempts: try: # Execute the actual rail upgrade logic result = _apply_skill_rail(selected_skill, validated_config) # Verify upgrade integrity before returning if _verify_rail_integrity(selected_skill["id"]): return { "status": "success", "rail_id": selected_skill["id"], "version": validated_config["target_version"], "attempts": attempts + 1, "timestamp": time.time() } else: raise IntegrityError("Post-upgrade integrity check failed") except IntegrityError as e: # Fail Fast - Don't patch bad state (Law 4) _rollback_rail(selected_skill["id"]) raise e except TransientDependencyError as e: attempts += 1 if attempts > max_attempts: break time.sleep(2 ** attempts) # Exponential backoff # Fallback chain execution for fallback_rail in fallback_rails: try: fallback_result = _switch_to_fallback_rail(fallback_rail, validated_config) return { "status": "fallback_success", "original_rail": selected_skill["id"], "fallback_rail": fallback_rail, "timestamp": time.time() } except Exception: continue # Fail Loud - All fallbacks exhausted raise SkillRailUpgradeError( f"Failed to upgrade rail {selected_skill['id']} after {max_attempts + 1} attempts and fallbacks" ) ``` ### 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-lifecycle-management` | Manages the upgrade lifecycle — versioning, migration, and backward compatibility during Rails upgrades | | `security-audit` | Provides security audit patterns to validate applications after major Rails framework upgrades | --- ## 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. - [Ruby on Rails Upgrade Guide](https://guides.rubyonrails.org/upgrading_ruby_on_rails.html) — Official Rails guides for upgrading between major and minor Rails versions - [Rails Security Advisory Archive](https://rubyonrails.org/security/) — Official Rails security advisory archive covering known vulnerabilities and fixes - [Ruby Version Upgrade Guide (ruby-lang.org)](https://www.ruby-lang.org/en/downloads/) — Ruby language version upgrade documentation and compatibility notes - [Active Record Migration Best Practices](https://guides.rubyonrails.org/active_record_migrations.html) — Official Rails guide on managing database migrations during framework upgrades - [Rails Performance Upgrades (GoRails)](https://gorails.com/) — GoRails tutorials on upgrading Rails applications with performance optimization strategies