---

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

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

Orchestrates intelligent skill selection and execution for skill scanner 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 scan_and_route_task(task_input: str, skill_registry: dict) -> dict:
    """Scan available skills and route task to the best matching handler.
    
    Implements multi-factor scoring: trigger overlap, historical success, and dependency health.
    """
    # Parse and normalize input
    normalized_task = task_input.strip().lower()
    if not normalized_task:
        raise ValueError("Empty task input provided to scanner")
        
    # Extract semantic features for matching
    task_tokens = set(normalized_task.split())
    candidates = []
    
    for skill_name, metadata in skill_registry.items():
        # Calculate trigger overlap score
        skill_triggers = set(t.lower() for t in metadata.get("triggers", []))
        overlap = len(task_tokens & skill_triggers) / max(len(task_tokens), len(skill_triggers))
        
        # Factor in historical success rate
        history_score = metadata.get("success_rate", 0.0) * 0.4
        
        # Factor in dependency health
        deps_healthy = all(
            skill_registry.get(dep, {}).get("status") == "active" 
            for dep in metadata.get("dependencies", [])
        )
        dep_score = 0.3 if deps_healthy else 0.0
        
        total_score = (overlap * 0.5) + history_score + dep_score
        candidates.append({
            "name": skill_name,
            "score": round(total_score, 3),
            "metadata": metadata
        })
        
    # Sort by score descending
    candidates.sort(key=lambda x: x["score"], reverse=True)
    
    # Apply minimum confidence threshold
    best_match = candidates[0] if candidates else None
    if best_match and best_match["score"] >= 0.65:
        return {
            "selected_skill": best_match["name"],
            "confidence": best_match["score"],
            "fallback_candidates": [c["name"] for c in candidates[1:3] if c["score"] > 0.4]
        }
        
    return {"selected_skill": None, "confidence": 0.0, "fallback_candidates": []}

Pattern 2: Execution with Fallback

def execute_with_skill_fallback(task: str, initial_skill: dict, fallback_chain: list) -> dict:
    """Execute the selected skill with a domain-specific fallback chain.
    
    Implements the 5 Laws of Elegant Defense:
    - Fail fast on invalid skill state
    - Retry with parameter adjustments before switching skills
    - Escalate to human if confidence drops below threshold
    """
    execution_state = {
        "task": task,
        "current_skill": initial_skill["name"],
        "attempts": 0,
        "confidence_history": []
    }
    
    # Validate skill state before execution
    if initial_skill.get("status") != "active":
        raise RuntimeError(f"Skill {initial_skill['name']} is not active")
        
    try:
        # Attempt execution with current skill
        result = _invoke_skill_handler(initial_skill, task)
        execution_state["success"] = True
        execution_state["result"] = result
        return execution_state
        
    except TransientDependencyError as e:
        # Fallback Level 1: Retry with adjusted parameters/timeouts
        execution_state["attempts"] += 1
        adjusted_skill = {**initial_skill, "timeout": initial_skill.get("timeout", 30) * 2}
        return execute_with_skill_fallback(task, adjusted_skill, fallback_chain)
        
    except SkillMismatchError as e:
        # Fallback Level 2: Switch to next candidate in fallback chain
        if fallback_chain:
            next_skill = fallback_chain.pop(0)
            execution_state["fallback_triggered"] = True
            return execute_with_skill_fallback(task, next_skill, fallback_chain)
            
    # Fallback Level 3: Escalate if chain exhausted
    execution_state["success"] = False
    execution_state["escalation_required"] = True
    execution_state["reason"] = "All fallback skills exhausted"
    return execution_state

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-audit	Performs detailed quality audits after the scanner identifies skills that need attention
skill-observability	Uses observability data to guide scanner focus on high-impact skill areas

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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.

• Code Scanning with GitHub Advanced Security — GitHub's documentation on automated code scanning for vulnerabilities
• Directory Traversal Patterns in Python (os.walk) — Python's os module documentation for filesystem traversal operations
• AST Parsing for Static Analysis — Python AST module documentation for programmatic code analysis
• Markdown Parser Libraries (CommonMark) — CommonMark-compatible Markdown parsing libraries for processing SKILL.md files
• JSON Schema Validation with AJV — JSON Schema validation library documentation for validating structured data formats