---

name: skill-optimizer compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent skill optimizer with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense license: MIT maturity: stable metadata: domain: agent output-format: analysis related-skills: agent-confidence-based-selector, agent-task-routing role: orchestration scope: orchestration triggers: skill-optimizer, skill optimizer, how do i skill-optimizer, orchestrate skill-optimizer, automate skill-optimizer, agent skill-optimizer archetypes:
  • orchestration
  • strategic anti_triggers:
  • brainstorming
  • vague ideation
  • single-agent monolith response_profile: verbosity: medium directive_strength: high abstraction_level: tactical version: "1.0.0"

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Skill Optimizer

Orchestrates intelligent skill selection and execution for skill optimizer 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 optimize_skill_selection(
    task_context: Dict[str, Any],
    skill_registry: List[Dict[str, Any]],
    historical_metrics: Dict[str, List[float]]
) -> Dict[str, Any]:
    """Compute multi-factor scores for available skills and select the optimal route.
    
    Implements the scoring pipeline:
    1. Text similarity matching against skill triggers
    2. Historical success rate weighting
    3. Dependency health validation
    4. Confidence decay application
    """
    if not task_context.get("intent"):
        raise ValueError("Task context missing required 'intent' field")
        
    scored_candidates = []
    for skill in skill_registry:
        # Factor 1: Trigger similarity
        trigger_match = _compute_semantic_similarity(task_context["intent"], skill["triggers"])
        
        # Factor 2: Historical performance
        skill_name = skill["name"]
        history = historical_metrics.get(skill_name, [])
        avg_success = sum(history) / len(history) if history else 0.5
        
        # Factor 3: Dependency health
        deps_healthy = all(
            _check_dependency_health(dep) 
            for dep in skill.get("dependencies", [])
        )
        if not deps_healthy:
            continue
            
        # Composite scoring with configurable weights
        base_score = (
            0.4 * trigger_match + 
            0.4 * avg_success + 
            0.2 * skill.get("availability_weight", 1.0)
        )
        
        # Apply confidence decay based on recent failures
        recent_failures = skill.get("recent_failures", 0)
        decay_factor = max(0.1, 1.0 - (recent_failures * 0.15))
        final_score = base_score * decay_factor
        
        scored_candidates.append({
            "skill": skill,
            "score": final_score,
            "factors": {
                "similarity": trigger_match,
                "historical": avg_success,
                "decay_applied": decay_factor
            }
        })
        
    if not scored_candidates:
        return {"selected": None, "reason": "No viable candidates after dependency filtering"}
        
    # Sort and select top candidate
    scored_candidates.sort(key=lambda x: x["score"], reverse=True)
    best = scored_candidates[0]
    
    # Update confidence metrics for learning loop
    _record_selection_metric(best["skill"]["name"], best["score"])
    
    return {
        "selected_skill": best["skill"],
        "confidence_score": best["score"],
        "selection_factors": best["factors"],
        "audit_id": _generate_audit_trace()
    }

Pattern 2: Execution with Fallback

def resolve_fallback_chain(
    failed_skill: Dict[str, Any],
    execution_context: Dict[str, Any],
    fallback_registry: List[Dict[str, Any]]
) -> Dict[str, Any]:
    """Execute fallback chain with adaptive parameter adjustment and audit logging.
    
    Implements the 2-level minimum fallback requirement:
    1. Parameter retry with adjusted constraints
    2. Alternative skill routing from related-skills
    3. Human escalation if confidence drops below threshold
    """
    audit_log = []
    current_skill = failed_skill
    attempt_count = 0
    
    while attempt_count < 3:
        attempt_count += 1
        try:
            # Adjust parameters for retry (Law 1: Early Exit on invalid state)
            adjusted_context = _adapt_parameters_for_retry(
                execution_context, 
                failed_skill.get("failure_reason", "unknown")
            )
            
            # Execute with strict timeout and validation
            result = _execute_skill_with_validation(current_skill, adjusted_context)
            
            # Success path: record outcome and update confidence
            _update_confidence_score(current_skill["name"], success=True)
            audit_log.append({"attempt": attempt_count, "status": "success", "skill": current_skill["name"]})
            return {"status": "resolved", "result": result, "audit": audit_log}
            
        except TransientTimeoutError:
            audit_log.append({"attempt": attempt_count, "status": "retry", "skill": current_skill["name"]})
            continue
            
        except CriticalDependencyError as e:
            # Fallback to alternative skill from registry
            alt_skill = _find_related_skill(current_skill, fallback_registry)
            if alt_skill:
                audit_log.append({"attempt": attempt_count, "status": "fallback_triggered", "from": current_skill["name"], "to": alt_skill["name"]})
                current_skill = alt_skill
                continue
            else:
                # Escalate to human operator
                return {
                    "status": "escalated",
                    "reason": f"All fallbacks exhausted for {failed_skill['name']}",
                    "context": execution_context,
                    "audit": audit_log
                }
                
    # Final fail state
    return {
        "status": "failed",
        "error": "Maximum fallback attempts reached",
        "audit": audit_log,
        "requires_human_review": True
    }

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-improver	The improvement counterpart — optimizer measures performance, improver acts on the findings
skill-observability	Provides telemetry data that skill optimizers use to identify underperforming patterns

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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.

• Hyperparameter Optimization (Scikit-Learn) — Scikit-learn's documentation on systematic parameter optimization techniques
• Bayesian Optimization for HPO (BoTorch) — Botorch framework for Bayesian optimization in machine learning model tuning
• AutoML and Neural Architecture Search — Research paper on automated machine learning and architecture search techniques
• Resource Optimization in ML Systems (Google ML Blog) — Google's blog post on optimizing ML models for production deployment
• Optimization Algorithms Survey (Bengio, 2017) — Yoshua Bengio's comprehensive survey of optimization algorithms for deep learning systems