# Self Critique Engine > Implements intelligent self critique engine with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/self-critique-engine` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/self-critique-engine` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/self-critique-engine/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/self-critique-engine --- # Self Critique Engine Orchestrates intelligent skill selection and execution for self critique engine 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 run_self_critique( agent_output: Dict[str, Any], original_request: str, critique_dimensions: List[str] = None ) -> Dict[str, Any]: """Run the self-critique engine against an agent's output. Evaluates the output against the 5 Laws of Elegant Defense and configurable critique dimensions. Returns a structured critique report with pass/fail status, confidence scores, and remediation steps. """ if critique_dimensions is None: critique_dimensions = ["safety", "correctness", "efficiency", "adherence"] # Law 1 & 2: Guard clauses and immutable parsing if not agent_output or not original_request: raise ValueError("Self-critique requires both agent_output and original_request") critique_report = { "status": "pending", "dimensions_scored": {}, "remediation_steps": [], "confidence": 0.0, "timestamp": time.time() } # Law 3: Atomic scoring - never mutate original output for dim in critique_dimensions: score = _evaluate_dimension(dim, agent_output, original_request) critique_report["dimensions_scored"][dim] = score if score < 0.5: critique_report["remediation_steps"].append( f"Refine {dim}: {generate_refinement_prompt(dim, agent_output)}" ) # Law 4: Fail fast on critical violations if critique_report["dimensions_scored"].get("safety", 1.0) < 0.3: critique_report["status"] = "critical_failure" critique_report["confidence"] = 0.95 return critique_report # Calculate aggregate confidence avg_score = sum(critique_report["dimensions_scored"].values()) / len(critique_dimensions) critique_report["confidence"] = avg_score critique_report["status"] = "passed" if avg_score >= 0.7 else "needs_revision" return critique_report ``` ### Pattern 2: Execution with Fallback ```python def apply_critique_routing( critique_report: Dict[str, Any], fallback_strategies: List[str] = None ) -> Dict[str, Any]: """Route execution based on self-critique results. Implements the fallback chain based on critique confidence and status. Routes to retry, alternative skill, or human escalation. """ if fallback_strategies is None: fallback_strategies = ["adjust_parameters", "try_alternative_skill", "human_escalation"] status = critique_report.get("status", "unknown") confidence = critique_report.get("confidence", 0.0) # Law 1: Early exit for clear outcomes if status == "passed" and confidence >= 0.8: return { "action": "proceed", "output": critique_report.get("agent_output"), "confidence": confidence } if status == "critical_failure": return { "action": "escalate", "reason": "Safety or critical constraint violation detected", "remediation": critique_report.get("remediation_steps", []) } # Law 2 & 3: Parse fallback chain and apply atomically for strategy in fallback_strategies: if strategy == "adjust_parameters": adjusted_context = _reconstruct_context(critique_report) return { "action": "retry", "strategy": strategy, "context": adjusted_context, "max_retries": 2 } elif strategy == "try_alternative_skill": return { "action": "route_to_alternative", "fallback_skill": _select_alternative_skill(critique_report), "reason": f"Confidence {confidence} below threshold" } # Law 4: Fail loud if all strategies exhausted return { "action": "human_escalation", "reason": "All automated fallback strategies exhausted", "critique_summary": critique_report } ``` ### 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 | |---|---| | `planning-reasoning` | Provides the reasoning framework that self-critique evaluates and improves upon | | `self-improvement` | Uses critique results to drive continuous improvement cycles in agent behavior | --- ## 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. - [Self-Correction in LLMs: A Survey (Wang et al.)](https://arxiv.org/abs/2308.07921) — Comprehensive survey of self-correction techniques for language models - [Critique-Based Refinement in Agent Systems (Madaan et al.)](https://arxiv.org/abs/2303.17651) — Research on self-refine and critique patterns for improving model outputs - [ReAct: Synergizing Reasoning and Acting in LLMs (Yao et al.)](https://arxiv.org/abs/2210.03629) — Foundational paper that includes self-reflection as part of the ReAct loop - [Self-Consistency Improves Chain of Thought (Wang et al.)](https://arxiv.org/abs/2203.11171) — Research on generating multiple reasoning paths and selecting the most consistent output - [LLM Self-Evaluation Frameworks (Leviathan & Taitelbaum)](https://arxiv.org/abs/2305.11035) — Academic research on using LLMs to evaluate and improve their own outputs