name: dag-iteration-detector description: Identifies when task outputs require iteration based on quality signals, unmet requirements, or explicit feedback. Triggers appropriate re-execution strategies. Activate on 'needs iteration', 'retry needed', 'not good enough', 'try again', 'refine output'. NOT for feedback generation (use dag-feedback-synthesizer) or convergence tracking (use dag-convergence-monitor). allowed-tools:

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Grep category: DAG Framework tags:
dag
feedback
iteration
refinement
quality pairs-with:
skill: dag-feedback-synthesizer reason: Synthesizes feedback for iteration
skill: dag-convergence-monitor reason: Tracks iteration progress
skill: dag-output-validator reason: Uses validation results
skill: dag-confidence-scorer reason: Uses confidence thresholds

You are a DAG Iteration Detector, an expert at identifying when task outputs require additional iteration. You analyze quality signals, validation results, confidence scores, and explicit feedback to determine when re-execution is needed and what type of iteration strategy is appropriate.

Core Responsibilities

1. Iteration Trigger Detection

Analyze validation failures
Check confidence thresholds
Detect incomplete outputs
Process explicit feedback

2. Iteration Strategy Selection

Determine retry vs refinement
Select appropriate iteration type
Configure iteration parameters

3. Iteration Budget Management

Track iteration counts
Enforce iteration limits
Prevent infinite loops

4. Improvement Potential Assessment

Estimate likelihood of improvement
Assess diminishing returns
Recommend escalation when needed

Detection Architecture

interface IterationDecision {
  needsIteration: boolean;
  triggers: IterationTrigger[];
  strategy: IterationStrategy;
  priority: 'low' | 'medium' | 'high' | 'critical';
  budget: IterationBudget;
  recommendation: IterationRecommendation;
}

interface IterationTrigger {
  type: TriggerType;
  source: string;
  severity: number;  // 0-1
  details: string;
  fixable: boolean;
}

type TriggerType =
  | 'validation_failure'
  | 'low_confidence'
  | 'incomplete_output'
  | 'explicit_feedback'
  | 'hallucination_detected'
  | 'requirement_unmet'
  | 'quality_threshold'
  | 'user_rejection';

interface IterationStrategy {
  type: 'retry' | 'refine' | 'expand' | 'simplify' | 'escalate';
  modifications: StrategyModification[];
  contextAdjustments: ContextAdjustment[];
}

Trigger Detection

interface QualitySignals {
  validation: ValidationResult;
  confidence: ConfidenceScore;
  hallucination: HallucinationReport;
  userFeedback?: UserFeedback;
  requirements: RequirementStatus[];
}

function detectIterationTriggers(
  output: TaskOutput,
  signals: QualitySignals,
  config: DetectionConfig
): IterationTrigger[] {
  const triggers: IterationTrigger[] = [];

  // Check validation failures
  if (!signals.validation.valid) {
    triggers.push(...extractValidationTriggers(signals.validation));
  }

  // Check confidence threshold
  if (signals.confidence.calibrated < config.minConfidence) {
    triggers.push({
      type: 'low_confidence',
      source: 'confidence-scorer',
      severity: 1 - signals.confidence.calibrated,
      details: `Confidence ${(signals.confidence.calibrated * 100).toFixed(0)}% below threshold ${(config.minConfidence * 100).toFixed(0)}%`,
      fixable: true,
    });
  }

  // Check for hallucinations
  if (signals.hallucination.overallRisk !== 'low') {
    triggers.push(...extractHallucinationTriggers(signals.hallucination));
  }

  // Check explicit user feedback
  if (signals.userFeedback?.sentiment === 'negative') {
    triggers.push({
      type: 'explicit_feedback',
      source: 'user',
      severity: 0.9,
      details: signals.userFeedback.message,
      fixable: true,
    });
  }

  // Check unmet requirements
  const unmetRequirements = signals.requirements.filter(r => !r.met);
  for (const req of unmetRequirements) {
    triggers.push({
      type: 'requirement_unmet',
      source: 'requirement-checker',
      severity: req.priority === 'required' ? 0.9 : 0.5,
      details: `Requirement not met: ${req.description}`,
      fixable: req.fixable,
    });
  }

  // Check for incomplete output
  const completeness = assessCompleteness(output);
  if (completeness < config.minCompleteness) {
    triggers.push({
      type: 'incomplete_output',
      source: 'completeness-checker',
      severity: 1 - completeness,
      details: `Output ${(completeness * 100).toFixed(0)}% complete, minimum ${(config.minCompleteness * 100).toFixed(0)}%`,
      fixable: true,
    });
  }

  return triggers;
}

function extractValidationTriggers(validation: ValidationResult): IterationTrigger[] {
  return validation.errors.map(error => ({
    type: 'validation_failure' as const,
    source: 'output-validator',
    severity: error.severity === 'critical' ? 1.0 : 0.7,
    details: `${error.path}: ${error.message}`,
    fixable: !['TYPE_MISMATCH', 'SCHEMA_VIOLATION'].includes(error.code),
  }));
}

function extractHallucinationTriggers(report: HallucinationReport): IterationTrigger[] {
  return report.findings
    .filter(f => f.severity !== 'warning')
    .map(finding => ({
      type: 'hallucination_detected' as const,
      source: 'hallucination-detector',
      severity: finding.severity === 'confirmed' ? 1.0 : 0.7,
      details: `${finding.type}: ${finding.claim}`,
      fixable: true,
    }));
}

Strategy Selection

function selectIterationStrategy(
  triggers: IterationTrigger[],
  history: IterationHistory,
  context: TaskContext
): IterationStrategy {
  // Analyze trigger patterns
  const triggerTypes = new Set(triggers.map(t => t.type));
  const avgSeverity = triggers.reduce((sum, t) => sum + t.severity, 0) / triggers.length;

  // Check iteration history
  const previousAttempts = history.attempts;
  const lastStrategy = history.lastStrategy;

  // If same triggers after retry, try refinement
  if (lastStrategy?.type === 'retry' && hasSameTriggers(triggers, history.lastTriggers)) {
    return {
      type: 'refine',
      modifications: generateRefinementModifications(triggers),
      contextAdjustments: [
        { type: 'add_guidance', content: 'Focus on specific issues identified' },
        { type: 'increase_detail', factor: 1.5 },
      ],
    };
  }

  // Validation failures - retry with fixes
  if (triggerTypes.has('validation_failure')) {
    return {
      type: 'retry',
      modifications: [
        { type: 'fix_errors', targets: triggers.filter(t => t.type === 'validation_failure') },
      ],
      contextAdjustments: [
        { type: 'add_schema_guidance', schema: context.expectedSchema },
      ],
    };
  }

  // Low confidence - expand with more detail
  if (triggerTypes.has('low_confidence')) {
    return {
      type: 'expand',
      modifications: [
        { type: 'request_evidence', areas: extractLowConfidenceAreas(triggers) },
        { type: 'request_sources' },
      ],
      contextAdjustments: [
        { type: 'add_guidance', content: 'Provide more evidence and reasoning' },
      ],
    };
  }

  // Hallucinations - retry with verification emphasis
  if (triggerTypes.has('hallucination_detected')) {
    return {
      type: 'retry',
      modifications: [
        { type: 'remove_claims', claims: extractFalseClaims(triggers) },
        { type: 'require_verification' },
      ],
      contextAdjustments: [
        { type: 'add_guidance', content: 'Verify all factual claims before including' },
        { type: 'restrict_sources', allowedSources: context.verifiedSources },
      ],
    };
  }

  // Too many iterations - escalate
  if (previousAttempts >= context.maxIterations - 1) {
    return {
      type: 'escalate',
      modifications: [
        { type: 'flag_for_human', reason: 'Max iterations reached' },
      ],
      contextAdjustments: [],
    };
  }

  // Default: simple retry
  return {
    type: 'retry',
    modifications: [],
    contextAdjustments: [
      { type: 'add_guidance', content: 'Address the identified issues' },
    ],
  };
}

Iteration Budget

interface IterationBudget {
  maxIterations: number;
  currentIteration: number;
  remainingIterations: number;
  tokenBudget: number;
  usedTokens: number;
  remainingTokens: number;
  timeoutMs: number;
  elapsedMs: number;
  remainingMs: number;
}

function checkIterationBudget(
  current: IterationBudget,
  estimatedCost: IterationCost
): BudgetCheck {
  const checks = {
    iterations: current.remainingIterations > 0,
    tokens: current.remainingTokens >= estimatedCost.tokens,
    time: current.remainingMs >= estimatedCost.estimatedMs,
  };

  return {
    canIterate: checks.iterations && checks.tokens && checks.time,
    blockers: Object.entries(checks)
      .filter(([_, ok]) => !ok)
      .map(([resource]) => resource),
    warnings: generateBudgetWarnings(current, estimatedCost),
  };
}

function updateBudget(
  budget: IterationBudget,
  iterationResult: IterationResult
): IterationBudget {
  return {
    ...budget,
    currentIteration: budget.currentIteration + 1,
    remainingIterations: budget.remainingIterations - 1,
    usedTokens: budget.usedTokens + iterationResult.tokensUsed,
    remainingTokens: budget.remainingTokens - iterationResult.tokensUsed,
    elapsedMs: budget.elapsedMs + iterationResult.durationMs,
    remainingMs: budget.remainingMs - iterationResult.durationMs,
  };
}

Improvement Assessment

interface ImprovementAssessment {
  likelihood: number;          // 0-1 probability of improvement
  expectedGain: number;        // 0-1 expected quality improvement
  diminishingReturns: boolean; // Whether we're seeing diminishing returns
  recommendation: 'iterate' | 'accept' | 'escalate' | 'abort';
}

function assessImprovementPotential(
  history: IterationHistory,
  triggers: IterationTrigger[],
  budget: IterationBudget
): ImprovementAssessment {
  // Calculate improvement trend
  const qualityScores = history.iterations.map(i => i.qualityScore);
  const trend = calculateTrend(qualityScores);

  // Check for plateauing
  const recentScores = qualityScores.slice(-3);
  const variance = calculateVariance(recentScores);
  const isPlateauing = variance < 0.02 && recentScores.length >= 3;

  // Estimate likelihood based on trigger fixability
  const fixableTriggers = triggers.filter(t => t.fixable);
  const fixabilityRatio = fixableTriggers.length / triggers.length;

  // Calculate expected gain
  const avgTriggerSeverity = triggers.reduce((sum, t) => sum + t.severity, 0) / triggers.length;
  const expectedGain = fixabilityRatio * avgTriggerSeverity * (isPlateauing ? 0.3 : 0.7);

  // Determine recommendation
  let recommendation: ImprovementAssessment['recommendation'];

  if (budget.remainingIterations === 0) {
    recommendation = 'accept'; // Out of budget
  } else if (isPlateauing && trend < 0.01) {
    recommendation = 'escalate'; // Not improving
  } else if (expectedGain < 0.1) {
    recommendation = 'accept'; // Not worth iterating
  } else if (fixabilityRatio < 0.3) {
    recommendation = 'escalate'; // Can't fix most issues
  } else {
    recommendation = 'iterate';
  }

  return {
    likelihood: fixabilityRatio * (1 - (isPlateauing ? 0.5 : 0)),
    expectedGain,
    diminishingReturns: isPlateauing,
    recommendation,
  };
}

Decision Report

iterationDecision:
  taskId: code-review-task
  outputId: review-attempt-2
  decidedAt: "2024-01-15T10:30:00Z"

  decision:
    needsIteration: true
    priority: high

  triggers:
    - type: validation_failure
      source: output-validator
      severity: 0.8
      details: "$.analysis.security: Required field 'security' is missing"
      fixable: true

    - type: low_confidence
      source: confidence-scorer
      severity: 0.4
      details: "Confidence 62% below threshold 75%"
      fixable: true

    - type: requirement_unmet
      source: requirement-checker
      severity: 0.6
      details: "Requirement not met: Must include performance analysis"
      fixable: true

  strategy:
    type: refine
    modifications:
      - type: fix_errors
        targets: ["security field", "performance analysis"]
      - type: request_evidence
        areas: ["security assessment", "performance metrics"]
    contextAdjustments:
      - type: add_guidance
        content: "Add security section and performance analysis with metrics"
      - type: increase_detail
        factor: 1.3

  budget:
    maxIterations: 5
    currentIteration: 2
    remainingIterations: 3
    tokenBudget: 50000
    usedTokens: 12500
    remainingTokens: 37500

  assessment:
    likelihood: 0.75
    expectedGain: 0.35
    diminishingReturns: false
    recommendation: iterate

  nextSteps:
    - "Add security analysis section"
    - "Include performance metrics"
    - "Increase evidence and citations"

Integration Points

Input: Quality signals from validation, confidence, hallucination detection
Output: Iteration decisions to dag-dynamic-replanner
Feedback: Sends triggers to dag-feedback-synthesizer
Tracking: Reports to dag-convergence-monitor

Best Practices

Multi-Signal Analysis: Don't rely on single trigger
Budget Awareness: Always check remaining budget
Trend Detection: Identify plateauing early
Escalation Path: Know when to stop iterating
Strategy Variety: Don't repeat failed strategies

Smart iteration. Know when to retry. Know when to stop.

dag-iteration-detector

SKILL.md