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).
Scanned 9/11/2026
Install to Claude Code
npx -y skills add curiositech/windags-skills --skill dag-feedback-synthesizer --agent claude-codeInstalls into .claude/skills of the current project.
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---
license: BSL-1.1
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: Agent & Orchestration
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, transforming quality signals into actionable improvement guidance that maximizes re-execution success.
## DECISION POINTS
### Signal Conflict Resolution Tree
```
Multiple conflicting quality signals detected?
├─ Validation FAILED + Confidence HIGH (>0.8)
│ └─ Priority: Fix validation errors first (structure over content)
│ └─ Action: Generate structural improvements, preserve content approach
├─ Validation PASSED + Confidence LOW (<0.5)
│ └─ Priority: Address confidence factors (content over structure)
│ └─ Action: Focus on accuracy, sources, completeness improvements
├─ Hallucination DETECTED + User Feedback POSITIVE
│ └─ Priority: Verify hallucination severity vs user satisfaction
│ └─ Action: If severity > 0.8, prioritize factual fixes over user preferences
└─ All Signals NEGATIVE
└─ Priority: Triage by estimated impact score
└─ Action: Select top 3 improvements by impact × feasibility
```
### Improvement Grouping Strategy
```
Total improvements count?
├─ 1-3 improvements: Individual handling
│ └─ Create detailed guidance for each
├─ 4-8 improvements: Category grouping
│ └─ Group by: missing_content > incorrect_content > structural > quality
├─ 9+ improvements: Priority filtering
│ └─ Filter to critical/high only, defer medium/low to next iteration
└─ Budget constraints (tokens < 1000)?
└─ Emergency mode: Critical validation fixes only
```
### Feedback Synthesis Approach
```
Iteration number?
├─ First iteration (n=1):
│ └─ Comprehensive feedback, include examples and context
├─ Second iteration (n=2):
│ └─ Focus on unaddressed items from iteration 1, add anti-patterns
├─ Third+ iteration (n≥3):
│ └─ Radical strategy change - question fundamental approach
└─ Final iteration (budget exhausted)?
└─ Accept best effort - generate "good enough" criteria
```
## FAILURE MODES
### 1. Conflicting Signal Paralysis
**Symptoms**: Multiple contradictory quality signals create unclear priorities
**Detection Rule**: If improvement priorities contain both "fix X" and "preserve X" for same element
**Fix**: Apply signal hierarchy (validation > confidence > hallucination > iteration triggers)
### 2. Zero Net Improvement Trap
**Symptoms**: Feedback addresses detected issues but creates new problems of equal severity
**Detection Rule**: If sum(improvement.estimatedImpact) < 0.1 or creates circular dependencies
**Fix**: Focus on single highest-impact change, defer others to subsequent iterations
### 3. Over-Generalized Suggestions
**Symptoms**: Feedback too vague to act on ("improve quality", "be more specific")
**Detection Rule**: If improvement suggestions lack concrete examples or success criteria
**Fix**: Generate specific examples, measurable criteria, and exact change instructions
### 4. Context Destruction Pattern
**Symptoms**: Improvements erase successful elements while fixing problems
**Detection Rule**: If preserveElements overlap with improvement targets
**Fix**: Explicitly protect working elements in guidance, use additive rather than replacement suggestions
### 5. Feedback Saturation Overflow
**Symptoms**: Too many improvements overwhelm agent execution capacity
**Detection Rule**: If improvement count > 8 or total estimated impact > 3.0
**Fix**: Apply ruthless prioritization - only critical and high priority items
## WORKED EXAMPLES
### Example 1: Code Review Task - Conflicting Signals
**Input Signals**:
- Validation: PASSED (schema valid)
- Confidence: 0.4 (low sources score: 0.2)
- Hallucination: 2 confirmed findings about API security
- User feedback: "Good structure but missing performance analysis"
**Decision Process**:
1. Check signal conflicts: Validation passed but confidence low + hallucinations detected
2. Apply hierarchy: Hallucination (accuracy) > Confidence (sources) > User feedback (completeness)
3. Group improvements: accuracy (critical), sources (high), completeness (medium)
**Generated Feedback**:
```yaml
improvements:
- priority: critical
issue: "Hallucination detected: Claims JWT tokens are stored in localStorage"
suggestion: "Remove claim about localStorage. State that token storage method should be specified in implementation"
successCriteria: "No hallucination findings for security claims"
- priority: high
issue: "Sources score 20% - no documentation references"
suggestion: "Add links to Express.js security docs, OWASP guidelines"
successCriteria: "Sources score above 70%"
```
### Example 2: Documentation Generation - Zero Improvement Pattern
**Input Signals**:
- Validation: FAILED (missing required fields)
- Confidence: 0.8 (high)
- Multiple low-impact improvements identified
**Decision Process**:
1. Calculate total impact: 5 improvements × 0.1 impact = 0.5 total
2. Detect zero improvement pattern (impact < threshold)
3. Switch to single high-impact focus
**Generated Feedback**:
```yaml
summary:
overallAssessment: needs_work
strategy: "Focus on single critical fix rather than multiple small changes"
improvements:
- priority: critical
issue: "Missing required 'installation' section"
suggestion: "Add ## Installation section with npm install command and basic setup"
estimatedImpact: 0.9
```
## QUALITY GATES
- [ ] All critical validation errors have corresponding improvements
- [ ] No improvement suggestions conflict with preserveElements
- [ ] Each improvement has specific, measurable success criteria
- [ ] Total improvement count ≤ 8 or filtered by budget constraints
- [ ] All hallucination findings severity > 0.5 are addressed
- [ ] Confidence factors below 0.6 have targeted improvements
- [ ] Feedback includes concrete examples for vague issues
- [ ] Priority scoring follows impact × feasibility formula
- [ ] Context preservation prevents regression on working elements
- [ ] Guidance includes specific prompt additions for re-execution
## NOT-FOR BOUNDARIES
**This skill should NOT be used for**:
- Detecting when iteration is needed → use `dag-iteration-detector`
- Tracking convergence across iterations → use `dag-convergence-monitor`
- Validating output structure → use `dag-output-validator`
- Scoring confidence levels → use `dag-confidence-scorer`
- Detecting hallucinations → use `dag-hallucination-detector`
**Delegate to other skills when**:
- Need to evaluate if another iteration is warranted → `dag-iteration-detector`
- Need to assess overall task progress → `dag-convergence-monitor`
- Need to validate specific output format → `dag-output-validator`
- Input contains requests for iteration decision making → `dag-iteration-detector`Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
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