SL

slo-implementation

Helps define and track service reliability goals through SLIs and SLOs to manage production performance.

Install

mkdir -p .claude/skills/slo-implementation && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/731" && unzip -o skill.zip -d .claude/skills/slo-implementation && rm skill.zip

Installs to .claude/skills/slo-implementation

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

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.
226 chars✓ has a “when” trigger
Advanced

Key capabilities

  • Define SLIs for availability, latency, and durability
  • Calculate error budgets based on SLO targets
  • Implement Prometheus recording rules for reliability metrics
  • Configure SLO-based alerting rules for burn rates
  • Visualize reliability data in Grafana dashboards

How it works

The framework translates business reliability goals into technical SLIs using PromQL queries. It establishes error budgets and alerting rules that trigger based on the rate at which the budget is consumed.

Inputs & outputs

You give it
Service reliability requirements and performance metrics
You get back
SLO definitions, error budget policies, and alerting rules

When to use slo-implementation

  • Define availability targets for a service
  • Implement latency SLIs
  • Create an error budget for a new release

About this skill

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)

Additional patterns and templates

More detailed templates and worked examples live in references/details.md. Read that file for the full pattern library.

When not to use it

  • When service reliability is not a business requirement
  • For systems without existing telemetry or monitoring

Prerequisites

Prometheus monitoring systemGrafana for visualization

Limitations

  • Requires accurate and consistent metric collection
  • Alerting thresholds must be tuned to specific service behavior

How it compares

It provides a standardized hierarchy and mathematical approach to reliability that replaces ad-hoc alerting with data-driven error budget management.

Compared to similar skills

slo-implementation side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
slo-implementation (this skill)32moNo flagsAdvanced
distributed-tracing52moNo flagsIntermediate
service-mesh-observability52moNo flagsAdvanced
observability-engineer124moNo flagsAdvanced

Try saying

Example prompts that trigger this skill in your AI assistant.

More by wshobson

View all by wshobson

You might also like

distributed-tracing

wshobson

Implement distributed tracing with Jaeger and Tempo to track requests across microservices and identify performance bottlenecks. Use when debugging microservices, analyzing request flows, or implementing observability for distributed systems.

577

service-mesh-observability

wshobson

Implement comprehensive observability for service meshes including distributed tracing, metrics, and visualization. Use when setting up mesh monitoring, debugging latency issues, or implementing SLOs for service communication.

574

observability-engineer

sickn33

Build production-ready monitoring, logging, and tracing systems. Implements comprehensive observability strategies, SLI/SLO management, and incident response workflows. Use PROACTIVELY for monitoring infrastructure, performance optimization, or production reliability.

1242

prometheus-configuration

wshobson

Set up Prometheus for comprehensive metric collection, storage, and monitoring of infrastructure and applications. Use when implementing metrics collection, setting up monitoring infrastructure, or configuring alerting systems.

645

langfuse

davila7

Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.

743

langsmith-observability

davila7

LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.

430

Search skills

Search the agent skills registry