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

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

Orchestrates intelligent skill selection and execution for skill sentinel 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_skill_candidates(
    task_intent: str,
    skill_registry: List[Dict],
    historical_metrics: Dict[str, float],
    system_health: Dict[str, bool]
) -> Optional[Dict]:
    """Score and rank skills based on intent matching, historical success, and system health.
    
    Implements Law 2 (Parse at boundary) by validating inputs upfront.
    Implements Law 3 (Atomic Predictability) by returning a fresh scored candidate dict.
    """
    if not task_intent or not skill_registry:
        raise ValueError("Task intent and skill registry must be non-empty")
        
    scored_candidates = []
    for skill in skill_registry:
        # Calculate multi-factor score
        intent_match = _compute_semantic_similarity(task_intent, skill.get("triggers", []))
        historical_success = historical_metrics.get(skill["id"], 0.5)
        availability = 1.0 if system_health.get(skill["id"], False) else 0.0
        
        # Weighted scoring formula
        composite_score = (0.4 * intent_match) + (0.4 * historical_success) + (0.2 * availability)
        
        if composite_score >= 0.7:
            scored_candidates.append({
                "skill_id": skill["id"],
                "name": skill["name"],
                "composite_score": round(composite_score, 3),
                "factors": {"intent": intent_match, "history": historical_success, "health": availability}
            })
            
    if not scored_candidates:
        return None
        
    # Return highest scoring candidate without mutating registry
    return max(scored_candidates, key=lambda x: x["composite_score"])

Pattern 2: Execution with Fallback

def execute_with_adaptive_fallback(
    selected_skill: Dict,
    execution_context: Dict,
    fallback_registry: List[Dict],
    confidence_store: Dict[str, float]
) -> Dict:
    """Execute skill with a structured fallback chain and dynamic confidence updates.
    
    Implements Law 4 (Fail Fast, Fail Loud) by raising on invalid states.
    Implements Law 1 (Early Exit) for guard clauses.
    """
    if not selected_skill or not execution_context.get("inputs"):
        raise SkillOrchestrationError("Missing required skill or execution inputs")
        
    fallback_steps = [
        {"type": "retry", "params": {"backoff": "exponential"}},
        {"type": "alternative_skill", "candidates": fallback_registry},
        {"type": "human_escalation", "priority": "high"}
    ]
    
    for step_idx, fallback_step in enumerate(fallback_steps):
        try:
            if fallback_step["type"] == "retry":
                result = _run_skill_with_backoff(selected_skill, execution_context)
            elif fallback_step["type"] == "alternative_skill":
                result = _try_alternative_skills(fallback_step["candidates"], execution_context)
            else:
                result = _escalate_to_human(selected_skill, execution_context)
                
            # Update confidence based on execution outcome
            new_confidence = _calculate_execution_confidence(result)
            confidence_store[selected_skill["id"]] = new_confidence
            
            return {
                "status": "success",
                "skill_id": selected_skill["id"],
                "fallback_level": step_idx,
                "confidence": new_confidence,
                "result": result
            }
            
        except InvalidStateError as e:
            raise SkillOrchestrationError(f"Invalid state at fallback step {step_idx}: {e}") from e
        except TransientFailure as e:
            if step_idx == len(fallback_steps) - 1:
                raise SkillOrchestrationError(f"All fallbacks exhausted for {selected_skill['id']}") from e
                
    return {"status": "failed", "skill_id": selected_skill["id"], "fallback_level": len(fallback_steps)}

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-scanner	Scans for skill issues; sentinel monitors runtime behavior and enforces quality gates
security-audit	Security-focused sentinel patterns that complement general skill quality monitoring

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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.

• SentinelOne Endpoint Protection Documentation — Enterprise security sentinel platform documentation (conceptual reference for sentinel architecture patterns)
• Open Policy Agent (OPA) — OPA documentation for policy enforcement and runtime guardrails in distributed systems
• Kubernetes Admission Controllers — Kubernetes admission controller patterns for runtime enforcement of policies
• AWS GuardDuty Threat Detection — AWS documentation on threat detection and anomaly monitoring as sentinel patterns
• Falco Runtime Security Monitoring — Sysdig Falco documentation for runtime security monitoring and anomaly detection in containers