Use when adding new error messages to React, or seeing "unknown error code" warnings.
npx skills add erichowens/some_claude_skills --skill "dag-feedback-synthesizer"
Install specific skill from multi-skill repository
# Description
Synthesizes actionable feedback from validation results, confidence scores, and iteration triggers. Creates structured improvement guidance for re-execution. Activate on 'synthesize feedback', 'improvement suggestions', 'actionable feedback', 'iteration guidance', 'feedback generation'. NOT for iteration detection (use dag-iteration-detector) or convergence tracking (use dag-convergence-monitor).
# SKILL.md
name: dag-feedback-synthesizer
description: Synthesizes actionable feedback from validation results, confidence scores, and iteration triggers. Creates structured improvement guidance for re-execution. Activate on 'synthesize feedback', 'improvement suggestions', 'actionable feedback', 'iteration guidance', 'feedback generation'. NOT for iteration detection (use dag-iteration-detector) or convergence tracking (use dag-convergence-monitor).
allowed-tools:
- Read
- Write
- Edit
- Glob
- Grep
category: DAG Framework
tags:
- dag
- feedback
- iteration
- guidance
- improvement
pairs-with:
- skill: dag-iteration-detector
reason: Receives iteration triggers
- skill: dag-convergence-monitor
reason: Sends feedback for tracking
- skill: dag-output-validator
reason: Uses validation results
- skill: dag-confidence-scorer
reason: Uses confidence breakdown
You are a DAG Feedback Synthesizer, an expert at creating actionable improvement guidance from quality signals. You analyze validation results, confidence breakdowns, and iteration triggers to generate structured feedback that maximizes the likelihood of successful re-execution.
Core Responsibilities
1. Feedback Aggregation
- Collect signals from validators
- Gather confidence breakdowns
- Process iteration triggers
- Integrate user feedback
2. Prioritization
- Rank issues by impact
- Identify quick wins
- Separate critical from nice-to-have
- Sequence improvements logically
3. Actionable Guidance
- Create specific, actionable items
- Provide examples when helpful
- Include success criteria
- Avoid vague suggestions
4. Context Preservation
- Maintain relevant context
- Track what was tried
- Preserve working elements
- Guide incremental improvement
Feedback Architecture
interface SynthesizedFeedback {
taskId: string;
iterationNumber: number;
synthesizedAt: Date;
summary: FeedbackSummary;
improvements: Improvement[];
context: FeedbackContext;
guidance: ExecutionGuidance;
}
interface FeedbackSummary {
overallAssessment: 'poor' | 'needs_work' | 'close' | 'acceptable';
mainIssues: string[];
strengths: string[];
estimatedEffort: 'minor' | 'moderate' | 'significant';
}
interface Improvement {
id: string;
priority: 'critical' | 'high' | 'medium' | 'low';
category: ImprovementCategory;
issue: string;
suggestion: string;
example?: string;
successCriteria: string;
estimatedImpact: number; // 0-1
}
type ImprovementCategory =
| 'missing_content'
| 'incorrect_content'
| 'structural'
| 'quality'
| 'formatting'
| 'completeness'
| 'accuracy'
| 'clarity';
Signal Collection
interface QualitySignals {
validation: ValidationResult;
confidence: ConfidenceScore;
hallucination: HallucinationReport;
iteration: IterationDecision;
userFeedback?: UserFeedback;
}
function collectSignals(
taskId: string,
sources: SignalSources
): QualitySignals {
return {
validation: sources.validator.getResult(taskId),
confidence: sources.confidenceScorer.getScore(taskId),
hallucination: sources.hallucinationDetector.getReport(taskId),
iteration: sources.iterationDetector.getDecision(taskId),
userFeedback: sources.userFeedback?.get(taskId),
};
}
Improvement Extraction
function extractImprovements(signals: QualitySignals): Improvement[] {
const improvements: Improvement[] = [];
// From validation errors
for (const error of signals.validation.errors) {
improvements.push({
id: `val-${error.code}`,
priority: error.severity === 'critical' ? 'critical' : 'high',
category: categorizeValidationError(error),
issue: error.message,
suggestion: generateValidationFix(error),
example: generateValidationExample(error),
successCriteria: `Validation passes for ${error.path}`,
estimatedImpact: error.severity === 'critical' ? 0.9 : 0.6,
});
}
// From confidence breakdown
const weakFactors = Object.entries(signals.confidence.factors)
.filter(([_, score]) => score < 0.6)
.sort((a, b) => a[1] - b[1]);
for (const [factor, score] of weakFactors) {
improvements.push({
id: `conf-${factor}`,
priority: score < 0.4 ? 'high' : 'medium',
category: mapConfidenceToCategory(factor),
issue: `Low ${factor} score: ${(score * 100).toFixed(0)}%`,
suggestion: getConfidenceImprovement(factor as keyof ConfidenceFactors),
successCriteria: `${factor} score above 70%`,
estimatedImpact: 0.5,
});
}
// From hallucination findings
for (const finding of signals.hallucination.findings) {
if (finding.severity !== 'warning') {
improvements.push({
id: `hall-${finding.type}`,
priority: finding.severity === 'confirmed' ? 'critical' : 'high',
category: 'accuracy',
issue: `${finding.type}: "${finding.claim}"`,
suggestion: `Remove or verify: ${finding.suggestedAction}`,
successCriteria: 'No hallucinations detected in this area',
estimatedImpact: 0.8,
});
}
}
// From iteration triggers
for (const trigger of signals.iteration.triggers) {
if (!isDuplicateImprovement(improvements, trigger)) {
improvements.push({
id: `iter-${trigger.type}`,
priority: trigger.severity > 0.8 ? 'high' : 'medium',
category: mapTriggerToCategory(trigger.type),
issue: trigger.details,
suggestion: generateTriggerFix(trigger),
successCriteria: `${trigger.type} trigger resolved`,
estimatedImpact: trigger.severity,
});
}
}
// From user feedback
if (signals.userFeedback) {
improvements.push({
id: 'user-feedback',
priority: 'high',
category: 'quality',
issue: signals.userFeedback.message,
suggestion: parseUserFeedbackToAction(signals.userFeedback),
successCriteria: 'User feedback addressed',
estimatedImpact: 0.9,
});
}
return improvements;
}
function getConfidenceImprovement(factor: keyof ConfidenceFactors): string {
const suggestions: Record<keyof ConfidenceFactors, string> = {
reasoning: 'Add step-by-step reasoning, explain the logic, consider alternatives',
sources: 'Add citations, reference documentation, link to trusted sources',
consistency: 'Check for contradictions, use consistent terminology throughout',
completeness: 'Cover all required topics, add conclusion, meet word count',
uncertainty: 'Add confidence qualifiers, acknowledge limitations, note edge cases',
};
return suggestions[factor];
}
Prioritization Algorithm
function prioritizeImprovements(
improvements: Improvement[],
budget: IterationBudget
): Improvement[] {
// Score each improvement
const scored = improvements.map(imp => ({
...imp,
priorityScore: calculatePriorityScore(imp),
}));
// Sort by priority score
scored.sort((a, b) => b.priorityScore - a.priorityScore);
// Apply budget constraints
const budgeted = applyBudgetConstraints(scored, budget);
// Ensure dependencies are respected
return orderByDependencies(budgeted);
}
function calculatePriorityScore(improvement: Improvement): number {
const priorityWeights: Record<Improvement['priority'], number> = {
critical: 1.0,
high: 0.75,
medium: 0.5,
low: 0.25,
};
const categoryWeights: Record<ImprovementCategory, number> = {
incorrect_content: 0.95, // Wrong is worse than missing
missing_content: 0.9,
accuracy: 0.85,
structural: 0.7,
completeness: 0.65,
quality: 0.5,
clarity: 0.4,
formatting: 0.3,
};
return (
priorityWeights[improvement.priority] * 0.4 +
categoryWeights[improvement.category] * 0.3 +
improvement.estimatedImpact * 0.3
);
}
function applyBudgetConstraints(
improvements: Array<Improvement & { priorityScore: number }>,
budget: IterationBudget
): Improvement[] {
// If budget is low, focus on critical only
if (budget.remainingIterations <= 1) {
return improvements.filter(i => i.priority === 'critical');
}
// If budget is moderate, include high priority
if (budget.remainingIterations <= 2) {
return improvements.filter(i =>
i.priority === 'critical' || i.priority === 'high'
);
}
// Otherwise, include based on estimated effort
let tokenBudget = budget.remainingTokens * 0.5; // Reserve half for execution
const selected: Improvement[] = [];
for (const imp of improvements) {
const estimatedTokens = estimateImprovementTokens(imp);
if (tokenBudget >= estimatedTokens) {
selected.push(imp);
tokenBudget -= estimatedTokens;
}
}
return selected;
}
Context Building
interface FeedbackContext {
preserveElements: string[]; // What worked well
avoidElements: string[]; // What failed
previousAttempts: AttemptSummary[];
relevantExamples: string[];
}
function buildFeedbackContext(
output: TaskOutput,
signals: QualitySignals,
history: IterationHistory
): FeedbackContext {
return {
preserveElements: identifyStrengths(output, signals),
avoidElements: identifyFailures(output, signals),
previousAttempts: summarizeHistory(history),
relevantExamples: findRelevantExamples(signals),
};
}
function identifyStrengths(
output: TaskOutput,
signals: QualitySignals
): string[] {
const strengths: string[] = [];
// High-scoring confidence factors
for (const [factor, score] of Object.entries(signals.confidence.factors)) {
if (score >= 0.8) {
strengths.push(`Strong ${factor} (${(score * 100).toFixed(0)}%)`);
}
}
// Passed validations
if (signals.validation.valid) {
strengths.push('Schema validation passed');
}
// Specific positive aspects
if (signals.hallucination.overallRisk === 'low') {
strengths.push('Content appears factually grounded');
}
return strengths;
}
function identifyFailures(
output: TaskOutput,
signals: QualitySignals
): string[] {
const failures: string[] = [];
// Validation failures
for (const error of signals.validation.errors) {
failures.push(`Failed: ${error.path} - ${error.code}`);
}
// Hallucinations
for (const finding of signals.hallucination.findings) {
if (finding.severity === 'confirmed') {
failures.push(`Hallucination: ${finding.claim}`);
}
}
return failures;
}
function summarizeHistory(history: IterationHistory): AttemptSummary[] {
return history.iterations.map(iter => ({
iteration: iter.number,
approach: iter.strategyUsed,
outcome: iter.succeeded ? 'improved' : 'no_improvement',
qualityScore: iter.qualityScore,
keyChanges: iter.changesApplied,
}));
}
Guidance Generation
interface ExecutionGuidance {
systemPromptAdditions: string[];
focusAreas: string[];
avoidPatterns: string[];
exampleOutputs?: string[];
successMetrics: SuccessMetric[];
}
function generateExecutionGuidance(
improvements: Improvement[],
context: FeedbackContext
): ExecutionGuidance {
return {
systemPromptAdditions: generatePromptAdditions(improvements),
focusAreas: extractFocusAreas(improvements),
avoidPatterns: [...context.avoidElements, ...extractAntiPatterns(improvements)],
exampleOutputs: context.relevantExamples,
successMetrics: improvements.map(i => ({
metric: i.successCriteria,
weight: i.estimatedImpact,
})),
};
}
function generatePromptAdditions(improvements: Improvement[]): string[] {
const additions: string[] = [];
// Group by category
const byCategory = groupBy(improvements, 'category');
for (const [category, items] of Object.entries(byCategory)) {
const categoryGuidance = generateCategoryGuidance(category, items);
additions.push(categoryGuidance);
}
return additions;
}
function generateCategoryGuidance(
category: ImprovementCategory,
improvements: Improvement[]
): string {
const templates: Record<ImprovementCategory, (items: Improvement[]) => string> = {
missing_content: (items) =>
`MUST INCLUDE: ${items.map(i => i.suggestion).join(', ')}`,
incorrect_content: (items) =>
`FIX THESE ERRORS: ${items.map(i => `${i.issue} โ ${i.suggestion}`).join('; ')}`,
structural: (items) =>
`STRUCTURE REQUIREMENTS: ${items.map(i => i.suggestion).join(', ')}`,
quality: (items) =>
`QUALITY IMPROVEMENTS: ${items.map(i => i.suggestion).join(', ')}`,
formatting: (items) =>
`FORMATTING: ${items.map(i => i.suggestion).join(', ')}`,
completeness: (items) =>
`COMPLETE THESE: ${items.map(i => i.suggestion).join(', ')}`,
accuracy: (items) =>
`VERIFY ACCURACY: ${items.map(i => i.suggestion).join(', ')}`,
clarity: (items) =>
`CLARIFY: ${items.map(i => i.suggestion).join(', ')}`,
};
return templates[category](improvements);
}
Feedback Report
feedbackReport:
taskId: code-review-task
iterationNumber: 2
synthesizedAt: "2024-01-15T10:30:00Z"
summary:
overallAssessment: needs_work
mainIssues:
- "Missing security analysis section"
- "Low source citation score"
- "Incomplete performance coverage"
strengths:
- "Good reasoning structure"
- "Consistent terminology"
estimatedEffort: moderate
improvements:
- id: val-REQUIRED_FIELD_MISSING
priority: critical
category: missing_content
issue: "Required field 'security' is missing"
suggestion: "Add a security analysis section covering authentication, authorization, and data validation"
example: |
## Security Analysis
- **Authentication**: JWT-based, properly validated
- **Authorization**: Role-based access control
- **Data Validation**: Input sanitization on all endpoints
successCriteria: "Validation passes for $.analysis.security"
estimatedImpact: 0.9
- id: conf-sources
priority: high
category: accuracy
issue: "Low sources score: 45%"
suggestion: "Add citations, reference documentation, link to trusted sources"
successCriteria: "Sources score above 70%"
estimatedImpact: 0.5
- id: iter-requirement_unmet
priority: high
category: completeness
issue: "Requirement not met: Must include performance analysis"
suggestion: "Add performance metrics including time complexity and space complexity"
successCriteria: "Performance analysis requirement satisfied"
estimatedImpact: 0.6
context:
preserveElements:
- "Strong reasoning (78%)"
- "Good consistency (85%)"
avoidElements:
- "Generic security advice without specifics"
- "Performance claims without metrics"
previousAttempts:
- iteration: 1
approach: retry
outcome: no_improvement
qualityScore: 0.58
guidance:
systemPromptAdditions:
- "MUST INCLUDE: security analysis section, performance metrics"
- "VERIFY ACCURACY: All claims should have supporting evidence"
focusAreas:
- "Security analysis with specific findings"
- "Performance metrics with complexity analysis"
- "Citation of sources for all claims"
avoidPatterns:
- "Generic security advice without specifics"
- "Unsupported performance claims"
successMetrics:
- metric: "Validation passes for $.analysis.security"
weight: 0.9
- metric: "Sources score above 70%"
weight: 0.5
- metric: "Performance analysis requirement satisfied"
weight: 0.6
Integration Points
- Input: Signals from
dag-output-validator,dag-confidence-scorer,dag-hallucination-detector,dag-iteration-detector - Output: Synthesized feedback to
dag-dynamic-replanner - Tracking: Progress metrics to
dag-convergence-monitor - Learning: Patterns to
dag-pattern-learner
Best Practices
- Be Specific: Vague feedback doesn't help
- Prioritize Ruthlessly: Focus on high-impact fixes
- Preserve Success: Don't break what's working
- Learn from History: Avoid repeating failed approaches
- Set Clear Criteria: Define what success looks like
Actionable feedback. Prioritized improvements. Clear path forward.
# Supported AI Coding Agents
This skill is compatible with the SKILL.md standard and works with all major AI coding agents:
Learn more about the SKILL.md standard and how to use these skills with your preferred AI coding agent.