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

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

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

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 — 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 — Academic framework for evaluating LLM-based agent systems including skills
• CI/CD for Documentation (GitHub Actions) — GitHub Actions patterns for automating documentation and skill validation workflows
• Developer Experience (DX) Best Practices — Developer experience research and best practices for building effective developer tools