From aws-cost-saver
Reviews AWS cost optimization findings using 4 parallel agents for verification, quality, context, and patterns; scores confidence and filters false positives. Use after scanning to update findings.json.
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Multi-perspective review of cost optimization findings with confidence-based filtering.
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Multi-perspective review of cost optimization findings with confidence-based filtering.
# Review findings (outputs to terminal)
/reviewing-findings
# Review and update findings.json
/reviewing-findings --update
Skip review if:
review_status)Launch 4 independent review agents:
Agent #1: Resource Verification
├── Verify resource still exists
├── Check current utilization metrics
└── Confirm finding is still valid
Agent #2: Recommendation Quality
├── Validate recommendation is actionable
├── Check for edge cases (ASG, DR, scheduled)
└── Verify savings calculation logic
Agent #3: Business Context
├── Identify environment (prod/dev/staging)
├── Check for dependencies
└── Flag potential risks
Agent #4: Historical Pattern
├── Check for burst patterns
├── Identify seasonal usage
└── Review recent changes
Each agent assigns confidence (0-100):
| Score | Meaning |
|---|---|
| 90-100 | Definite savings - act immediately |
| 70-89 | High confidence - safe to implement |
| 50-69 | Medium confidence - needs validation |
| 25-49 | Low confidence - likely false positive |
| 0-24 | Skip - insufficient evidence |
Filter threshold: 50 (adjustable)
Add review metadata to each finding:
{
"check_id": "EC2-001",
"monthly_savings": 150.00,
"review_status": {
"reviewed_at": "2026-01-19T15:00:00Z",
"final_confidence": 85,
"agents": {
"resource_verification": 90,
"recommendation_quality": 80,
"business_context": 85,
"historical_pattern": 85
},
"action": "recommended",
"notes": "Resource verified idle for 21 days"
}
}
Verify:
Red flags:
Verify:
Red flags:
Verify:
Red flags:
Verify:
Red flags:
Prompt: For each finding in findings.json, verify the resource:
1. Call AWS API to check resource exists
2. Get current utilization metrics (last 7 days)
3. Compare current state vs finding state
4. Score confidence based on verification
Output JSON with:
- resource_verified: boolean
- current_metrics: object
- confidence: 0-100
- notes: string
Prompt: For each finding, evaluate the recommendation:
1. Is the recommendation actionable?
2. Are there edge cases not considered?
3. Is the savings calculation reasonable?
4. Are there dependencies to consider?
Score based on:
- Clear next steps: +20
- Edge cases addressed: +15
- Accurate savings: +25
- No dependencies: +20
- Reasonable effort: +20
Prompt: For each finding, assess business context:
1. Identify environment (check name, tags)
2. Check for production dependencies
3. Assess risk of implementing recommendation
4. Consider compliance requirements
Adjust confidence:
- Production resource: -10 (needs careful review)
- Has dependencies: -20
- Compliance-related: -15
- Dev/test: +10 (lower risk)
Prompt: For each finding, analyze patterns:
1. Check CloudWatch metrics for patterns
2. Identify burst/scheduled workloads
3. Review resource modification history
4. Check for seasonal patterns
Red flags:
- Burst pattern detected: -25
- Recent scaling event: -20
- Seasonal variation: -15
- Consistent low usage: +15
## Findings Review
Reviewed 35 findings. Results:
✓ 25 findings APPROVED (confidence ≥70)
⚠ 6 findings NEEDS VALIDATION (confidence 50-69)
✗ 4 findings FILTERED (confidence <50)
### Top Approved Findings
1. **RDS-002**: Over-provisioned RDS Instance
Resource: production-clinical-trial-matcher
Savings: $95.00/month
Confidence: 85%
Notes: CPU avg 2.6% for 31 days, no burst patterns
2. **EC2-012**: Unattached EBS Volume
Resource: vol-0f282561946f02d6a
Savings: $8.00/month
Confidence: 92%
Notes: Unattached for 45 days, has snapshot backup
### Needs Validation
1. **LAMBDA-001**: Memory Over-provisioning
Resource: production-file-handler
Confidence: 55%
Issue: Only 78 invocations - insufficient data
Action: Monitor for 14 more days
### Filtered (False Positives)
1. **EC2-001**: Idle EC2 Instance
Resource: i-0885726ca0d3e7856
Original confidence: 75%
Final confidence: 35%
Reason: Part of ASG, scheduled scaling detected
Each finding gets a review_status object added.
findings.jsonfindings.json- [ ] Load findings.json
- [ ] Launch Agent #1: Resource Verification
- [ ] Launch Agent #2: Recommendation Quality
- [ ] Launch Agent #3: Business Context
- [ ] Launch Agent #4: Historical Pattern
- [ ] Merge agent results
- [ ] Calculate final confidence scores
- [ ] Apply filter threshold
- [ ] Update findings with review_status
- [ ] Save updated findings.json
- [ ] Output summary to terminal