176 lines
3.4 KiB
Markdown
176 lines
3.4 KiB
Markdown
---
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description: Analyze the impact and scope of production errors
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category: debugging
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tools: Task, TodoWrite
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model: inherit
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version: 1.0.0
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---
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# Error Impact Analysis
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Assess the severity, reach, and business impact of production errors.
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## Usage
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```bash
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/error-impact-analysis <error-or-timeframe>
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Examples:
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/error-impact-analysis "ISSUE-789"
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/error-impact-analysis "checkout errors last 7 days"
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/error-impact-analysis "critical errors this week"
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/error-impact-analysis "impact of recent deployment"
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```
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## What This Analyzes
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### Quantitative Impact
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- Number of occurrences
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- Number of users affected
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- Error rate over time
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- Affected environments/releases
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### Qualitative Impact
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- Error severity (critical, high, medium, low)
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- Affected user workflows
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- Business function impact (checkout, signup, etc.)
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- User experience degradation
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### Trend Analysis
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- Is it increasing or decreasing?
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- When did it start?
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- Related to specific release?
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- Correlation with traffic/usage
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## Example Analyses
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### Single Issue Impact
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```bash
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/error-impact-analysis "What's the impact of MYAPP-123? How many users, revenue impact?"
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```
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### Category Impact
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```bash
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/error-impact-analysis "Overall impact of all payment-related errors this month"
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```
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### Release Health
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```bash
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/error-impact-analysis "Error impact comparison: current release vs previous release"
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```
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### Critical Errors
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```bash
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/error-impact-analysis "Show all critical errors and their combined user impact"
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```
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## Output Format
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Analysis includes:
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**Scope**:
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- Total occurrences
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- Unique users affected
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- Affected countries/regions
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- Browser/device breakdown
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**Severity Assessment**:
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- Error frequency
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- User impact score
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- Business criticality
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- Blocking vs non-blocking
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**Trends**:
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- Occurrence over time (chart/data)
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- Peak times
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- Growth rate
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- Comparison to baseline
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**Business Impact**:
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- Affected revenue-generating flows
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- Customer support tickets related
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- SLA implications
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- Reputation risk
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**Prioritization**:
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- Recommendation on urgency
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- Comparison with other errors
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- ROI of fixing
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## Integration with Analytics
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Enable both plugins for deeper impact analysis:
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```bash
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/plugin enable catalyst-debugging
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/plugin enable catalyst-analytics
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/error-impact-analysis "How many users who hit error X churned vs users who didn't?"
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```
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This combines:
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- Sentry error data (who hit the error)
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- PostHog behavior data (did they churn)
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## Incident Response Workflow
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### 1. Assess Impact
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```bash
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/error-impact-analysis "new spike in errors at 3pm"
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```
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### 2. Determine Severity
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Based on output:
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- **Critical**: >1000 users, blocking checkout/signup
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- **High**: >100 users, degraded experience
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- **Medium**: <100 users, minor inconvenience
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- **Low**: <10 users, edge case
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### 3. Prioritize Response
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```bash
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> "Based on this impact, should we rollback or hotfix?"
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```
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### 4. Track Resolution
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```bash
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> "After fix, compare error rates before and after"
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```
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## Tips for Impact Analysis
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1. **Consider timeframe** - "last hour" for incidents, "last week" for trends
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2. **Segment users** - Impact on paid vs free users may differ
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3. **Check related errors** - One root cause may affect multiple error types
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4. **Compare releases** - Pinpoint when impact started
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5. **Business context** - Impact during peak hours is more severe
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## Context Cost
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Plugin uses ~20k tokens. Disable after analysis:
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```bash
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/plugin disable catalyst-debugging
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```
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---
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**See also**: `/debug-production-error`, `/trace-analysis`
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