# Skill Sentinel > Implements intelligent skill sentinel with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/skill-sentinel` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/skill-sentinel` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/skill-sentinel/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/skill-sentinel --- # 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 ```python 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 ```python 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 | --- ## 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](https://support.sentinelone.com/hc/en-us) — Enterprise security sentinel platform documentation (conceptual reference for sentinel architecture patterns) - [Open Policy Agent (OPA)](https://www.openpolicyagent.org/docs/) — OPA documentation for policy enforcement and runtime guardrails in distributed systems - [Kubernetes Admission Controllers](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/admission-control-controllers/) — Kubernetes admission controller patterns for runtime enforcement of policies - [AWS GuardDuty Threat Detection](https://docs.aws.amazon.com/guardduty/latest/ug/what-is-guardduty.html) — AWS documentation on threat detection and anomaly monitoring as sentinel patterns - [Falco Runtime Security Monitoring](https://falco.org/docs/) — Sysdig Falco documentation for runtime security monitoring and anomaly detection in containers