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slo-implementation skill

/skills/slo-implementation

This skill defines and implements SLIs, SLOs, and error budgets to measure reliability and balance velocity.

This is most likely a fork of the slo-implementation skill from xfstudio
npx playbooks add skill sickn33/antigravity-awesome-skills --skill slo-implementation

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SKILL.md
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---
name: slo-implementation
description: Define and implement Service Level Indicators (SLIs) and Service Level Objectives (SLOs) with error budgets and alerting. Use when establishing reliability targets, implementing SRE practices, or measuring service performance.
---

# SLO Implementation

Framework for defining and implementing Service Level Indicators (SLIs), Service Level Objectives (SLOs), and error budgets.

## Do not use this skill when

- The task is unrelated to slo implementation
- You need a different domain or tool outside this scope

## Instructions

- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open `resources/implementation-playbook.md`.

## Purpose

Implement measurable reliability targets using SLIs, SLOs, and error budgets to balance reliability with innovation velocity.

## Use this skill when

- Define service reliability targets
- Measure user-perceived reliability
- Implement error budgets
- Create SLO-based alerts
- Track reliability goals

## SLI/SLO/SLA Hierarchy

```
SLA (Service Level Agreement)
  ↓ Contract with customers
SLO (Service Level Objective)
  ↓ Internal reliability target
SLI (Service Level Indicator)
  ↓ Actual measurement
```

## Defining SLIs

### Common SLI Types

#### 1. Availability SLI
```promql
# Successful requests / Total requests
sum(rate(http_requests_total{status!~"5.."}[28d]))
/
sum(rate(http_requests_total[28d]))
```

#### 2. Latency SLI
```promql
# Requests below latency threshold / Total requests
sum(rate(http_request_duration_seconds_bucket{le="0.5"}[28d]))
/
sum(rate(http_request_duration_seconds_count[28d]))
```

#### 3. Durability SLI
```
# Successful writes / Total writes
sum(storage_writes_successful_total)
/
sum(storage_writes_total)
```

**Reference:** See `references/slo-definitions.md`

## Setting SLO Targets

### Availability SLO Examples

| SLO % | Downtime/Month | Downtime/Year |
|-------|----------------|---------------|
| 99%   | 7.2 hours      | 3.65 days     |
| 99.9% | 43.2 minutes   | 8.76 hours    |
| 99.95%| 21.6 minutes   | 4.38 hours    |
| 99.99%| 4.32 minutes   | 52.56 minutes |

### Choose Appropriate SLOs

**Consider:**
- User expectations
- Business requirements
- Current performance
- Cost of reliability
- Competitor benchmarks

**Example SLOs:**
```yaml
slos:
  - name: api_availability
    target: 99.9
    window: 28d
    sli: |
      sum(rate(http_requests_total{status!~"5.."}[28d]))
      /
      sum(rate(http_requests_total[28d]))

  - name: api_latency_p95
    target: 99
    window: 28d
    sli: |
      sum(rate(http_request_duration_seconds_bucket{le="0.5"}[28d]))
      /
      sum(rate(http_request_duration_seconds_count[28d]))
```

## Error Budget Calculation

### Error Budget Formula

```
Error Budget = 1 - SLO Target
```

**Example:**
- SLO: 99.9% availability
- Error Budget: 0.1% = 43.2 minutes/month
- Current Error: 0.05% = 21.6 minutes/month
- Remaining Budget: 50%

### Error Budget Policy

```yaml
error_budget_policy:
  - remaining_budget: 100%
    action: Normal development velocity
  - remaining_budget: 50%
    action: Consider postponing risky changes
  - remaining_budget: 10%
    action: Freeze non-critical changes
  - remaining_budget: 0%
    action: Feature freeze, focus on reliability
```

**Reference:** See `references/error-budget.md`

## SLO Implementation

### Prometheus Recording Rules

```yaml
# SLI Recording Rules
groups:
  - name: sli_rules
    interval: 30s
    rules:
      # Availability SLI
      - record: sli:http_availability:ratio
        expr: |
          sum(rate(http_requests_total{status!~"5.."}[28d]))
          /
          sum(rate(http_requests_total[28d]))

      # Latency SLI (requests < 500ms)
      - record: sli:http_latency:ratio
        expr: |
          sum(rate(http_request_duration_seconds_bucket{le="0.5"}[28d]))
          /
          sum(rate(http_request_duration_seconds_count[28d]))

  - name: slo_rules
    interval: 5m
    rules:
      # SLO compliance (1 = meeting SLO, 0 = violating)
      - record: slo:http_availability:compliance
        expr: sli:http_availability:ratio >= bool 0.999

      - record: slo:http_latency:compliance
        expr: sli:http_latency:ratio >= bool 0.99

      # Error budget remaining (percentage)
      - record: slo:http_availability:error_budget_remaining
        expr: |
          (sli:http_availability:ratio - 0.999) / (1 - 0.999) * 100

      # Error budget burn rate
      - record: slo:http_availability:burn_rate_5m
        expr: |
          (1 - (
            sum(rate(http_requests_total{status!~"5.."}[5m]))
            /
            sum(rate(http_requests_total[5m]))
          )) / (1 - 0.999)
```

### SLO Alerting Rules

```yaml
groups:
  - name: slo_alerts
    interval: 1m
    rules:
      # Fast burn: 14.4x rate, 1 hour window
      # Consumes 2% error budget in 1 hour
      - alert: SLOErrorBudgetBurnFast
        expr: |
          slo:http_availability:burn_rate_1h > 14.4
          and
          slo:http_availability:burn_rate_5m > 14.4
        for: 2m
        labels:
          severity: critical
        annotations:
          summary: "Fast error budget burn detected"
          description: "Error budget burning at {{ $value }}x rate"

      # Slow burn: 6x rate, 6 hour window
      # Consumes 5% error budget in 6 hours
      - alert: SLOErrorBudgetBurnSlow
        expr: |
          slo:http_availability:burn_rate_6h > 6
          and
          slo:http_availability:burn_rate_30m > 6
        for: 15m
        labels:
          severity: warning
        annotations:
          summary: "Slow error budget burn detected"
          description: "Error budget burning at {{ $value }}x rate"

      # Error budget exhausted
      - alert: SLOErrorBudgetExhausted
        expr: slo:http_availability:error_budget_remaining < 0
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "SLO error budget exhausted"
          description: "Error budget remaining: {{ $value }}%"
```

## SLO Dashboard

**Grafana Dashboard Structure:**

```
┌────────────────────────────────────┐
│ SLO Compliance (Current)           │
│ ✓ 99.95% (Target: 99.9%)          │
├────────────────────────────────────┤
│ Error Budget Remaining: 65%        │
│ ████████░░ 65%                     │
├────────────────────────────────────┤
│ SLI Trend (28 days)                │
│ [Time series graph]                │
├────────────────────────────────────┤
│ Burn Rate Analysis                 │
│ [Burn rate by time window]         │
└────────────────────────────────────┘
```

**Example Queries:**

```promql
# Current SLO compliance
sli:http_availability:ratio * 100

# Error budget remaining
slo:http_availability:error_budget_remaining

# Days until error budget exhausted (at current burn rate)
(slo:http_availability:error_budget_remaining / 100)
*
28
/
(1 - sli:http_availability:ratio) * (1 - 0.999)
```

## Multi-Window Burn Rate Alerts

```yaml
# Combination of short and long windows reduces false positives
rules:
  - alert: SLOBurnRateHigh
    expr: |
      (
        slo:http_availability:burn_rate_1h > 14.4
        and
        slo:http_availability:burn_rate_5m > 14.4
      )
      or
      (
        slo:http_availability:burn_rate_6h > 6
        and
        slo:http_availability:burn_rate_30m > 6
      )
    labels:
      severity: critical
```

## SLO Review Process

### Weekly Review
- Current SLO compliance
- Error budget status
- Trend analysis
- Incident impact

### Monthly Review
- SLO achievement
- Error budget usage
- Incident postmortems
- SLO adjustments

### Quarterly Review
- SLO relevance
- Target adjustments
- Process improvements
- Tooling enhancements

## Best Practices

1. **Start with user-facing services**
2. **Use multiple SLIs** (availability, latency, etc.)
3. **Set achievable SLOs** (don't aim for 100%)
4. **Implement multi-window alerts** to reduce noise
5. **Track error budget** consistently
6. **Review SLOs regularly**
7. **Document SLO decisions**
8. **Align with business goals**
9. **Automate SLO reporting**
10. **Use SLOs for prioritization**

## Reference Files

- `assets/slo-template.md` - SLO definition template
- `references/slo-definitions.md` - SLO definition patterns
- `references/error-budget.md` - Error budget calculations

## Related Skills

- `prometheus-configuration` - For metric collection
- `grafana-dashboards` - For SLO visualization

Overview

This skill defines and implements Service Level Indicators (SLIs), Service Level Objectives (SLOs), and error budgets to measure and manage service reliability. It provides concrete metrics, Prometheus recording rules, alerting patterns, and review processes to balance reliability with development velocity. Use it to establish measurable reliability targets and operationalize SRE practices.

How this skill works

The skill converts desired reliability targets into SLIs and SLOs, calculates error budgets, and encodes those as Prometheus recording rules and alerting rules. It includes multi-window burn rate calculations, error budget policies, and dashboard queries for ongoing monitoring. Reviews and policies guide cadence for weekly, monthly, and quarterly SLO evaluation.

When to use it

  • Establish reliability targets for user-facing services
  • Implement error budgets to govern feature rollout decisions
  • Create SLO-based alerting to reduce incident noise
  • Measure latency, availability, or durability from production metrics
  • Prioritize engineering work based on error budget status

Best practices

  • Start with user-perceived SLIs (availability, latency, durability)
  • Choose achievable SLO targets informed by current performance and business needs
  • Use multi-window burn-rate alerts to minimize false positives
  • Document SLO decisions and align targets with business goals
  • Automate recording rules, error budget calculations, and dashboard reporting

Example use cases

  • Define a 99.9% availability SLO for a public API and implement Prometheus rules for SLI ratio and error budget
  • Create burn-rate alerts that trigger a feature freeze when the error budget is exhausted
  • Build a Grafana SLO dashboard showing compliance, remaining error budget, and burn-rate trends
  • Run weekly and monthly SLO reviews to decide on risky deployments based on remaining budget
  • Translate operational objectives into alarm thresholds and runbooks for on-call teams

FAQ

How is error budget calculated?

Error budget = 1 - SLO target. For example, a 99.9% SLO yields a 0.1% monthly budget (≈43.2 minutes/month).

Why use multi-window burn-rate alerts?

Combining short and long windows reduces false positives by requiring sustained or correlated high burn across multiple timescales before alerting.