---

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

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

Orchestrates intelligent skill selection and execution for skill creator 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 parse_orchestration_request(user_input: str, system_constraints: Dict) -> Dict:
    """Parse raw user input into structured orchestration features.
    
    Extracts intent, required tools, constraints, and priority signals.
    Validates against system constraints before routing.
    """
    if not user_input or not isinstance(user_input, str):
        raise ValueError("Input must be a non-empty string")
        
    # Extract core intent and entities using NLP pipeline
    intent = _nlp_extract_intent(user_input)
    entities = _nlp_extract_entities(user_input)
    priority = _detect_priority_signals(user_input)
    
    # Validate against system constraints (Law 2: Make illegal states unrepresentable)
    if priority == "critical" and not system_constraints.get("allow_critical"):
        raise PermissionError("Critical priority tasks are disabled in current environment")
        
    # Build structured feature vector for scoring
    features = {
        "raw_input": user_input,
        "intent": intent,
        "entities": entities,
        "priority": priority,
        "token_count": len(user_input.split()),
        "requires_parallel": _detect_parallel_signals(user_input),
        "timestamp": time.time()
    }
    
    return features

Pattern 2: Execution with Fallback

def score_and_route(features: Dict, skill_registry: List[Dict], history_db: Dict) -> Dict:
    """Calculate multi-factor scores and determine optimal routing path.
    
    Combines trigger matching, historical success rates, and dependency checks.
    Returns routing decision with confidence metrics and fallback chain.
    """
    if not features or not skill_registry:
        raise ValueError("Features and skill registry are required for routing")
        
    scored_skills = []
    for skill in skill_registry:
        # Calculate trigger overlap score
        trigger_match = _calculate_trigger_overlap(features["intent"], skill["triggers"])
        
        # Fetch historical performance
        hist_key = f"{features['intent']}:{skill['name']}"
        success_rate = history_db.get(hist_key, {}).get("success_rate", 0.5)
        
        # Check dependency availability
        deps_met = all(_check_dependency_availability(dep) for dep in skill.get("dependencies", []))
        
        # Weighted scoring formula
        raw_score = (trigger_match * 0.5) + (success_rate * 0.3) + (0.2 if deps_met else 0.0)
        
        if raw_score >= 0.6:
            scored_skills.append({
                "skill": skill,
                "score": round(raw_score, 3),
                "fallback_chain": skill.get("fallback_skills", []),
                "estimated_latency_ms": _estimate_execution_time(skill)
            })
            
    # Sort by score descending and select top candidate
    scored_skills.sort(key=lambda x: x["score"], reverse=True)
    selected = scored_skills[0] if scored_skills else None
    
    return {
        "selected_skill": selected["skill"] if selected else None,
        "confidence": selected["score"] if selected else 0.0,
        "fallback_chain": selected["fallback_chain"] if selected else [],
        "routing_timestamp": time.time()
    }

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-engineering	Provides engineering practices that complement skill creation workflows
skill-documentation-best-practices	Covers documentation patterns for high-fidelity SKILL.md files

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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.

• Markdown Specification (CommonMark) — The official CommonMark specification that SKILL.md files follow
• YAML Frontmatter Standards — YAML frontmatter conventions used in static site generators and documentation systems
• OpenCode Skill Documentation — Official OpenCode documentation on skill structure, loading, and configuration
• Instruction Engineering Patterns (Google) — Google's research on writing effective instructions for AI models
• Agent Communication Protocols (ACL Guidelines) — ACL Anthology research papers on multi-agent communication and protocol design