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

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

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

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.) — Comprehensive survey of self-correction techniques for language models
• Critique-Based Refinement in Agent Systems (Madaan et al.) — Research on self-refine and critique patterns for improving model outputs
• ReAct: Synergizing Reasoning and Acting in LLMs (Yao et al.) — Foundational paper that includes self-reflection as part of the ReAct loop
• Self-Consistency Improves Chain of Thought (Wang et al.) — Research on generating multiple reasoning paths and selecting the most consistent output
• LLM Self-Evaluation Frameworks (Leviathan & Taitelbaum) — Academic research on using LLMs to evaluate and improve their own outputs