---

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

• guidance
• examples
• do-dont description: Implements intelligent skill improver 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-improver, skill improver, how do i skill-improver, orchestrate skill-improver, automate skill-improver, agent skill-improver 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 Improver

Orchestrates intelligent skill selection and execution for skill improver 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 analyze_skill_improvement(
    skill_metadata: Dict,
    usage_metrics: Dict,
    context_gaps: List[str]
) -> Dict:
    """Analyze a skill's current state and calculate improvement potential.
    
    Applies multi-factor scoring to determine if a skill needs optimization,
    and generates a prioritized improvement plan based on the 5 Laws of Elegant Defense.
    
    Args:
        skill_metadata: Current skill definition including triggers, prompts, and constraints
        usage_metrics: Historical performance data (success_rate, avg_latency, error_types)
        context_gaps: Identified missing context or edge cases from recent failures
        
    Returns:
        Improvement plan with priority score, suggested changes, and fallback recommendation
    """
    # Guard clause - Early Exit (Law 1)
    if not skill_metadata or not usage_metrics:
        raise ValueError("Skill metadata and usage metrics are required for analysis")
        
    # Parse input - Make Illegal States Unrepresentable (Law 2)
    success_rate = usage_metrics.get("success_rate", 0.0)
    error_frequency = usage_metrics.get("error_frequency", 0)
    gap_severity = len(context_gaps) * 0.15
    
    # Calculate improvement score (0.0-1.0)
    improvement_score = (1.0 - success_rate) * 0.6 + min(error_frequency / 10, 0.4) + gap_severity
    improvement_score = min(max(improvement_score, 0.0), 1.0)
    
    # Determine optimization strategy
    if improvement_score < 0.3:
        strategy = "MAINTAIN"
        suggested_changes = []
    elif improvement_score < 0.7:
        strategy = "OPTIMIZE"
        suggested_changes = _generate_optimization_suggestions(skill_metadata, context_gaps)
    else:
        strategy = "REWRITE"
        suggested_changes = _generate_rewrite_blueprint(skill_metadata, context_gaps)
        
    # Atomic Predictability (Law 3) - Return new dict, don't mutate inputs
    return {
        "skill_id": skill_metadata.get("id"),
        "improvement_score": round(improvement_score, 3),
        "strategy": strategy,
        "suggested_changes": suggested_changes,
        "fallback_to_static": improvement_score > 0.9,
        "analysis_timestamp": time.time()
    }

Pattern 2: Execution with Fallback

def apply_skill_improvement(
    improvement_plan: Dict,
    skill_template: Dict,
    max_iterations: int = 3
) -> Dict:
    """Execute skill improvement workflow with fallback chain for resilience.
    
    Implements the Fail Fast, Fail Loud principle (Law 4):
    - Invalid improvement plans halt immediately with descriptive errors
    - No silent failures or partial optimizations
    
    Fallback chain:
    1. Apply incremental prompt/config adjustments
    2. Revert to validated static template if optimization degrades performance
    3. Flag for human expert review if improvement score remains critical
    
    Args:
        improvement_plan: Output from analyze_skill_improvement
        skill_template: Base skill definition to apply changes against
        max_iterations: Maximum optimization cycles before fallback
        
    Returns:
        Optimized skill definition with validation results and confidence metrics
    """
    # Guard clause - validate plan (Early Exit)
    if improvement_plan.get("strategy") not in ("OPTIMIZE", "REWRITE"):
        raise ValueError(f"Invalid improvement strategy: {improvement_plan.get('strategy')}")
        
    # Parse context - Ensure trusted state (Law 2)
    validated_template = _validate_skill_template(skill_template)
    
    for iteration in range(max_iterations):
        try:
            # Apply domain-specific improvement logic
            optimized_skill = _apply_optimization_rules(validated_template, improvement_plan)
            
            # Validate against 5 Laws of Elegant Defense
            validation_result = _validate_against_defense_laws(optimized_skill)
            
            if validation_result["passes"]:
                return {
                    "success": True,
                    "skill_id": optimized_skill["id"],
                    "optimized_config": optimized_skill,
                    "iterations_used": iteration + 1,
                    "confidence_delta": validation_result["confidence_score"]
                }
                
        except InvalidStateError as e:
            # Fail Fast - Don't try to patch bad data (Law 4)
            raise SkillImprovementError(
                f"Invalid state during optimization: {str(e)}"
            ) from e
            
        except DegradationError as e:
            # Optimization degraded performance - trigger fallback
            if iteration == max_iterations - 1:
                return _apply_fallback_to_static_template(validated_template)
                
    # All iterations exhausted - Fail Loud (Law 4)
    raise SkillImprovementError(
        f"Failed to improve skill after {max_iterations} optimization cycles"
    )

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 counterpart — after creating skills, use this skill to iteratively improve them
self-critique-engine	Provides critique methodologies that skill improver applies to evaluate and refine skills

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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.

• Continuous Integration Best Practices (Atlassian) — Atlassian's guide to continuous improvement practices applicable to skill iteration
• Code Refactoring Patterns (Fowler) — Martin Fowler's catalog of refactoring patterns applicable to improving existing skills
• Prompt Iteration and Optimization Techniques — Research on iterative prompt improvement methods applicable to skill refinement
• A/B Testing for Documentation (Microsoft) — Microsoft's guidance on testing documentation changes with measurable quality improvements
• Technical Writing Iterative Process (Google) — Google's style guide on iterative editing and refinement processes for technical content