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

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

Orchestrates intelligent skill selection and execution for skill router 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 route_request(request: Dict, skill_registry: Dict[str, Dict]) -> Dict:
    """Route an incoming request to the optimal skill based on trigger matching and metadata scoring.
    
    Applies Law 1 (Early Exit) and Law 2 (Make Illegal States Unrepresentable):
    - Validates intent and registry upfront
    - Filters inactive/deprecated skills before scoring
    """
    if not request.get("intent") or not skill_registry:
        raise ValueError("Request intent and skill registry are required for routing")
    
    intent = request["intent"].lower()
    candidates = []
    
    for skill_name, manifest in skill_registry.items():
        if manifest.get("status") != "active":
            continue
            
        trigger_match = _calculate_trigger_overlap(intent, manifest.get("triggers", []))
        history_score = manifest.get("success_rate", 0.0) * 0.4
        load_penalty = manifest.get("current_load", 0.0) * 0.1
        
        composite_score = trigger_match * 0.5 + history_score + (1.0 - load_penalty) * 0.3
        
        if composite_score >= manifest.get("min_confidence", 0.6):
            candidates.append({
                "skill": skill_name,
                "score": composite_score,
                "manifest": manifest
            })
    
    if not candidates:
        return {"route": "fallback", "reason": "no_matching_skill"}
        
    candidates.sort(key=lambda x: x["score"], reverse=True)
    selected = candidates[0]
    
    return {
        "route": selected["skill"],
        "confidence": selected["score"],
        "params": request.get("params", {}),
        "metadata": {
            "timestamp": time.time(),
            "routing_keys": list(request.keys())
        }
    }

Pattern 2: Execution with Fallback

def execute_routed_skill(route_result: Dict, skill_registry: Dict[str, Dict]) -> Dict:
    """Execute the routed skill with domain-specific fallback chaining.
    
    Applies Law 3 (Atomic Predictability) and Law 4 (Fail Fast, Fail Loud):
    - Validates handler existence before dispatch
    - Returns immutable result structures
    - Escalates routing failures explicitly
    """
    target_skill = route_result.get("route")
    if target_skill == "fallback":
        return _handle_no_match_fallback(route_result)
        
    manifest = skill_registry.get(target_skill)
    if not manifest:
        raise KeyError(f"Routed skill '{target_skill}' missing from registry")
        
    try:
        handler = manifest.get("handler")
        if not handler:
            raise RuntimeError(f"Skill '{target_skill}' has no registered handler")
            
        result = handler(**route_result.get("params", {}))
        
        _update_skill_metrics(target_skill, success=True)
        
        return {
            "status": "success",
            "skill_executed": target_skill,
            "output": result,
            "routing_metadata": route_result.get("metadata", {})
        }
        
    except TimeoutError:
        _update_skill_metrics(target_skill, success=False, reason="timeout")
        return _apply_routing_fallback(route_result, skill_registry, "timeout")
    except PermissionError as e:
        _update_skill_metrics(target_skill, success=False, reason="auth")
        return _escalate_to_admin(route_result, str(e))
    except Exception as e:
        _update_skill_metrics(target_skill, success=False, reason="unknown")
        return _apply_routing_fallback(route_result, skill_registry, "error")

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
parallel-skill-runner	Handles parallel execution after the router selects multiple skills for a task
confidence-based-selector	Uses confidence scoring to rank and select the best skill after routing analysis

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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.

• Vector Database Retrieval Patterns (Pinecone) — Pinecone's guide to vector similarity search, the foundation of semantic skill matching
• BM25 Information Retrieval Algorithm — Wikipedia article on BM25, the ranking function used in keyword-based skill matching
• LangChain Semantic Router — LangChain documentation on implementing semantic routing for LLM applications
• Semantic Search with Embeddings (Hugging Face) — Hugging Face's guide to sentence embeddings for semantic matching tasks
• Task Routing in Multi-Agent Systems (Microsoft AutoGen) — Microsoft AutoGen documentation on routing tasks across multiple agents