agentsclimarketplace

Slo implementation skills wshobson slo implementation

Skill bg-szy/TOP-SKILLS/skills/marketplace/slo-implementation__skills-wshobson-slo-implementation

全球最大的 Claude Code 技能聚合库 · 收录 3900+ 来自 12+ 来源的技能，提供在线搜索与趋势分析看板 / The world's largest Claude Code skill aggregation hub — 3900+ skills from 12+ sources with online search and trend dashboard

Install
npx -y skills add bg-szy/TOP-SKILLS --skill slo-implementation__skills-wshobson-slo-implementation

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
  • 4 stars4 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

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.

SKILL.md

8.4 KB, as published. Nobody here has run it

SLO Implementation

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

Purpose

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

When to Use

  • 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

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

2. Latency SLI

# 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/MonthDowntime/Year
99%7.2 hours3.65 days
99.9%43.2 minutes8.76 hours
99.95%21.6 minutes4.38 hours
99.99%4.32 minutes52.56 minutes

Choose Appropriate SLOs

Consider:

  • User expectations
  • Business requirements
  • Current performance
  • Cost of reliability
  • Competitor benchmarks

Example SLOs:

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

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

# 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

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:

# 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

# 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

Gives 1 of the 12 instructions most monitoring observability skills give

Counted across 481 of the 483 authors here whose files we hold, read 2026-08-06

  • link every alert to a runbookin 43 of 481, across 35 files
  • use structured json loggingin 36 of 481, across 31 files
  • alert on user-facing symptomsin 20 of 481, across 15 files
  • emit structured JSON logs with stable event namesin 18 of 481, across 13 files
  • propagate trace context across boundariesin 16 of 481
  • use histograms for latency trackingin 14 of 481, across 9 files
  • use OpenTelemetry for distributed tracingin 13 of 481, across 8 files
  • include a correlation ID on every log linein 13 of 481, across 8 files
  • Define service level objectiveshere, and in 10 of 481, across 7 files
  • Call useAzureMonitor before importing other modulesin 9 of 481, across 2 files
  • stop and ask for clarification if inputs are missingin 9 of 481, across 2 files
  • define on-call questions before adding telemetryin 9 of 481, across 4 files

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.