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

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

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

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 — Official Rails guides for upgrading between major and minor Rails versions
• Rails Security Advisory Archive — Official Rails security advisory archive covering known vulnerabilities and fixes
• Ruby Version Upgrade Guide (ruby-lang.org) — Ruby language version upgrade documentation and compatibility notes
• Active Record Migration Best Practices — Official Rails guide on managing database migrations during framework upgrades
• Rails Performance Upgrades (GoRails) — GoRails tutorials on upgrading Rails applications with performance optimization strategies