# Skill Improver > Implements intelligent skill improver with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/skill-improver` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/skill-improver` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/skill-improver/raw - Safety review: pending - 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-improver --- # 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 ```python 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 ```python 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 | --- ## 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)](https://www.atlassian.com/continuous-delivery/principles/continuous-integration-vs-delivery-vs-deployment) — Atlassian's guide to continuous improvement practices applicable to skill iteration - [Code Refactoring Patterns (Fowler)](https://martinfowler.com/books/refactoring.html) — Martin Fowler's catalog of refactoring patterns applicable to improving existing skills - [Prompt Iteration and Optimization Techniques](https://www.promptingguide.ai/techniques/iterating) — Research on iterative prompt improvement methods applicable to skill refinement - [A/B Testing for Documentation (Microsoft)](https://learn.microsoft.com/en-us/azure/devops/project/about-continuous-integration) — Microsoft's guidance on testing documentation changes with measurable quality improvements - [Technical Writing Iterative Process (Google)](https://developers.google.com/style/editing-your-work) — Google's style guide on iterative editing and refinement processes for technical content