# Skill Developer > Implements intelligent skill developer with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/skill-developer` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/skill-developer` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/skill-developer/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-developer --- # Skill Developer Orchestrates intelligent skill selection and execution for skill developer 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_and_select_skill(task_description: str, skill_registry: dict, execution_history: list) -> dict: """Evaluate available skills against task triggers and historical performance. Implements Law 2 (Parse at boundary) and Law 1 (Early exit on invalid state). Returns a scored candidate list with dependency validation. """ if not task_description or not isinstance(skill_registry, dict): raise ValueError("Task description and valid registry required") candidates = [] task_tokens = set(_normalize_triggers(task_description)) for skill_name, metadata in skill_registry.items(): # Parse triggers and calculate semantic overlap skill_triggers = set(metadata.get("triggers", [])) trigger_overlap = len(task_tokens & skill_triggers) / max(len(task_tokens), len(skill_triggers)) # Fetch historical success rate from execution history historical_success = _calculate_success_rate(skill_name, execution_history) # Validate dependencies are currently healthy deps_healthy = all(_check_dependency_status(dep) for dep in metadata.get("dependencies", [])) if not deps_healthy: continue # Multi-factor scoring: 50% trigger match, 30% history, 20% system load system_load = _get_current_system_load() load_penalty = 0.1 if system_load > 0.8 else 0.0 score = (trigger_overlap * 0.5) + (historical_success * 0.3) + ((1.0 - load_penalty) * 0.2) if score >= 0.65: candidates.append({ "name": skill_name, "score": round(score, 3), "confidence": round(score * historical_success, 3), "dependencies": metadata.get("dependencies", []), "metadata": metadata }) candidates.sort(key=lambda x: x["score"], reverse=True) return candidates[0] if candidates else None ``` ### Pattern 2: Execution with Fallback ```python def execute_with_domain_fallback(selected_skill: dict, task_context: dict, fallback_registry: dict) -> dict: """Execute the selected skill with a structured fallback chain. Implements Law 4 (Fail fast, fail loud) and maintains audit trails. Handles dependency resolution, retry logic, and confidence updates. """ if not selected_skill or not task_context: raise SkillOrchestrationError("Missing skill selection or task context") attempt_count = 0 max_attempts = 2 current_skill = selected_skill while attempt_count <= max_attempts: try: # Validate context against skill requirements validated_context = _enforce_context_schema(task_context, current_skill["metadata"]) # Execute domain-specific logic result = _invoke_skill_runtime(current_skill["name"], validated_context) # Update confidence scores and log audit trail _record_execution_log(current_skill["name"], success=True, latency_ms=_measure_latency()) _update_confidence_score(current_skill["name"], delta=0.05) return { "status": "success", "skill": current_skill["name"], "result": result, "attempts": attempt_count + 1, "audit_id": str(uuid.uuid4()) } except DependencyError as e: _record_execution_log(current_skill["name"], success=False, error=str(e)) if attempt_count < max_attempts: attempt_count += 1 continue except TransientRuntimeError: if attempt_count < max_attempts: attempt_count += 1 continue # Exhausted retries - trigger fallback chain fallback_candidates = _find_related_skills(current_skill["name"], fallback_registry) if fallback_candidates: current_skill = fallback_candidates[0] attempt_count = 0 continue # Fail loud: all retries and fallbacks exhausted raise SkillOrchestrationError( f"Execution failed for {current_skill['name']} after {attempt_count} attempts. " f"No viable fallbacks available." ) ``` ### 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 — use this when building new skills, load creator for the full workflow | | `skill-testing-methodology` | Ensures created skills are tested through systematic validation before deployment | --- ## 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. - [Semantic Versioning 2.0.0](https://semver.org/) — The semantic versioning specification used for tracking skill versions - [Software Testing Principles (ISTQB)](https://www.istqb.org/get-certified/ foundations.html) — ISTQB foundational testing principles applicable to skill quality assurance - [Agent Framework Evaluation Metrics](https://arxiv.org/abs/2401.12954) — Academic framework for evaluating LLM-based agent systems including skills - [CI/CD for Documentation (GitHub Actions)](https://docs.github.com/en/actions/use-cases-and-examples/publishing-packages/publishing-markdown-documentation) — GitHub Actions patterns for automating documentation and skill validation workflows - [Developer Experience (DX) Best Practices](https://www.getdx.com/resources/) — Developer experience research and best practices for building effective developer tools