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Site Reliability Engineering
SRE is the discipline of applying software engineering to operations problems. It
replaces ad-hoc ops work with principled systems: reliability targets backed by error
budgets, toil replaced by automation, and incidents treated as system failures rather
than human ones. This skill covers the full SRE lifecycle - from defining SLOs through
capacity planning and progressive delivery - as practiced by teams operating
production systems at scale. Designed for engineers moving from "keep the lights on"
to systematic reliability ownership.
When to use this skill
Trigger this skill when the user:
Needs to define or revise SLOs, SLIs, or SLAs for a service
Is calculating or acting on an error budget
Wants to identify, measure, or automate toil
Is running or writing a postmortem
Is designing or improving an on-call rotation
Is forecasting capacity needs or planning a load test
Is designing a rollout strategy (canary, blue/green, progressive)
Do NOT trigger this skill for:
Pure infrastructure provisioning without a reliability framing (use a Docker/K8s skill)
Application performance optimization without an SLO context (use a performance-engineering skill)
Key principles
Embrace risk with error budgets - 100% reliability is neither achievable nor
desirable. Every extra nine of availability comes at a cost: slower feature velocity,
more complex systems, higher operational burden. An error budget makes the
trade-off explicit: spend budget on risk-taking (deploys, experiments), save it
when reliability is threatened.
Eliminate toil - Toil is work that is manual, repetitive, automatable, reactive,
and scales with service growth without producing lasting value. Every hour of toil is
an hour not spent on reliability improvements. The goal is not zero toil (some is
unavoidable) but continuous reduction.
SLOs are the contract - SLOs align engineering and business on what reliability
is worth. They prevent both over-engineering ("five nines or nothing") and
under-investing ("it mostly works"). Write SLOs before writing on-call runbooks;
the SLO defines what warrants waking someone up.
Blameless postmortems - Systems fail, not people. Blaming individuals creates
an environment where engineers hide problems and avoid risk. Blameless postmortems
surface systemic issues and produce durable fixes. The goal is learning, not
accountability theater.
- The SRE charter is to automate operations
work until the team's operational load is below 50% of their time. The remaining
capacity is reserved for reliability engineering that makes the next incident less
likely or less severe.
Automate yourself out of a job
Core concepts
SLI / SLO / SLA hierarchy
SLA (Service Level Agreement)
- External contract with customers. Breach triggers penalties.
- Set conservatively: your internal SLO must be tighter than your SLA.
SLO (Service Level Objective)
- Internal target. Drives alerting, error budgets, and engineering decisions.
- Typically SLO = SLA - 0.5 to 1 percentage point headroom.
SLI (Service Level Indicator)
- The actual measurement. A ratio: good events / total events.
- Example: (requests completing < 300ms) / (all requests)
Rule of thumb: Define one availability SLI and one latency SLI per user-facing
service. Add correctness SLIs for data pipelines or financial systems.
Burn rate = observed error rate / allowed error rate. A burn rate of 1 means you
are spending budget at exactly the expected pace. A burn rate of 14.4 on a 30-day
window means the budget is gone in 50 hours.
Budget policy (what to do when budget is threatened):
Budget remaining
Action
> 50%
Normal feature velocity, deploys allowed
25-50%
Review recent changes, increase monitoring
10-25%
Freeze non-essential deploys, focus on stability
< 10%
Feature freeze, all hands on reliability work
Toil definition
Toil has all of these properties - if even one is missing, it may be legitimate work:
Manual: A human is in the loop doing repetitive keystrokes
Repetitive: Done more than once with the same steps
Automatable: A script or system could do it
Reactive: Triggered by a system event, not proactive engineering
No lasting value: Executing it does not improve the system; it just holds it steady
Scales with load: More traffic, more toil (a danger sign)
Incident severity levels
Severity
Customer impact
Response
Example
SEV1
Complete outage or data loss
Immediate page, war room
Payment service down
SEV2
Degraded core functionality
Page on-call
20% of requests erroring
SEV3
Minor degradation, workaround exists
Ticket, next business day
Slow dashboard loads
SEV4
Cosmetic issue or internal tool
Backlog
Wrong label in admin UI
On-call best practices
Rotate weekly; never longer than two weeks without a break
Guarantee engineers sleep: no P1 pages between 10pm-8am without escalation
Track on-call load: pages per shift, time-to-ack, total hours interrupted
Every on-call shift ends with a handoff: active incidents, lingering alerts, context
Budget 20-30% of the next sprint for on-call follow-up work
Common tasks
Define SLOs for a service
Step 1: Choose the right SLIs. Start from user journeys, not technical metrics.
User journey
SLI type
Measurement
"Page loads fast"
Latency
requests_under_300ms / total_requests
"API calls succeed"
Availability
non_5xx_responses / total_responses
"Data is correct"
Correctness
correct_outputs / total_outputs
"Writes persist"
Durability
successful_writes_verified / total_writes
Step 2: Set targets using historical data.
1. Pull 30 days of your current SLI measurements
2. Find your current actual performance (e.g., 99.85% availability)
3. Set SLO slightly below current performance (e.g., 99.7%)
4. Tighten over time as you improve reliability
Never set an SLO tighter than your best recent 30-day window without a
corresponding reliability investment plan.
Step 3: Choose the window. Rolling 30-day windows are standard. They smooth
spikes but respond to sustained degradation. Avoid calendar month windows - they reset
budgets on the 1st regardless of what happened on the 31st.
Step 4: Define measurement exclusions. Planned maintenance, dependencies outside
your control, and client errors (4xx) typically excluded from SLI calculations.
Calculate and track error budgets
Burn rate alerting (recommended over threshold alerting):
Fast burn alert (page immediately):
Condition: burn_rate > 14.4 for 5 minutes
Meaning: At this rate, 30-day budget exhausted in ~50 hours
Severity: SEV2, page on-call
Slow burn alert (ticket, investigate):
Condition: burn_rate > 3 for 60 minutes
Meaning: Budget exhausted in ~10 days if trend continues
Severity: SEV3, create ticket
Budget depletion alert (SEV1 escalation trigger):
Condition: budget_remaining < 10%
Action: Feature freeze, reliability sprint
Multi-window alerting catches both fast spikes and slow degradation:
5-minute window: catches fast burns (major incident)
Both windows alerting together = high-confidence page
Budget depletion actions:
Stop all non-essential deploys
Pull toil-reduction and reliability items from the backlog
Review the postmortem queue for unresolved action items
Document the decision with date and budget percentage in your incident tracker
Identify and reduce toil
Toil taxonomy - classify before automating:
Category
Examples
Priority
Interrupt-driven
Restarting crashed pods, clearing queues
High - on-call tax
Regular manual ops
Weekly capacity checks, certificate renewals
Medium - scheduled work
Deploy ceremony
Manual release steps, environment promotion
High - blocks velocity
Data cleanup
Fixing bad records, reconciliation jobs
Medium - correctness risk
Access management
Provisioning accounts, rotating credentials
High - security risk
Automation prioritization matrix:
HIGH FREQUENCY
|
Quick to | Slow to
automate | automate
|
AUTOMATE FIRST -----+----- SCHEDULE: PLAN PROJECT
|
|
AUTOMATE WHEN -----+----- ACCEPT OR ELIMINATE
CONVENIENT |
|
LOW FREQUENCY
Measure toil before and after automation: track hours/week per category per engineer.
If toil is growing, the automation is not keeping pace with service growth.
Run a blameless postmortem
When to hold one: Every SEV1. Every SEV2 with customer-visible impact. Any
incident that consumed more than 4 hours of on-call time. Recurring SEV3s from the
same root cause.
Timeline (24-48 hours after resolution):
Day 0 (during incident): Designate incident commander, keep a timeline in a shared doc
Day 1 (next morning): Assign postmortem owner, schedule meeting within 48 hours
Day 2 (postmortem): 60-90 min facilitated session
Day 3: Draft published internally for 24-hour comment period
Day 5: Final version published, action items entered in tracker
The five questions that drive every postmortem:
What happened and when? (timeline)
Why did it happen? (root cause - ask "why" five times)
Why did we not detect it sooner? (detection gap)
What slowed down the response? (mitigation gap)
What prevents recurrence? (action items)
Action item rules: Each item must have an owner, a due date, and a measurable
definition of done. "Improve monitoring" is not an action item. "Add burn-rate alert
for payments-api availability SLO by 2025-Q3" is.
See references/postmortem-template.md for the full template with example entries
and facilitation guide.
Design on-call rotation
Rotation structure:
Primary on-call: First responder. Acks within 15 min, mitigates or escalates.
Secondary on-call: Backup if primary misses ack within 15 min.
Escalation path: Engineering manager -> Director -> Incident commander (for SEV1 only)
Runbook requirements (every alert must have one):
Symptom: what the alert is telling you
Impact: who is affected and how severely
Steps: numbered investigation and mitigation steps
Escalation: who to call if steps do not resolve it
Context: links to dashboards, service documentation, past incidents
Handoff process (end of each on-call rotation):
Document any open or lingering issues
List any alerts that fired but did not page (worth reviewing)
Share known fragile areas or upcoming risky changes
Review toil hours and open action items with incoming on-call
Health metrics for on-call load:
Metric
Target
Alert threshold
Pages per on-call week
< 5
> 10
Pages outside business hours
< 2/week
> 5/week
Time-to-ack (P1)
< 5 min
> 15 min
Toil percentage of on-call time
< 50%
> 70%
Plan capacity
Demand forecasting approach:
1. Baseline: measure current peak RPS, CPU, memory, storage
2. Growth rate: calculate month-over-month traffic growth (last 6 months)
3. Project forward: apply growth rate to 6-month and 12-month horizons
4. Add headroom: 30-50% above projected peak for burst capacity
5. Trigger threshold: the utilization level that kicks off provisioning
Load testing before capacity decisions:
Define the traffic shape (ramp, steady state, spike)
Test to 150% of expected peak - find the breaking point before users do
Measure: latency distribution at load, error rate at load, resource utilization
Identify the bottleneck (CPU, DB connections, memory) before scaling the wrong thing
Headroom planning table:
Component
Trigger utilization
Target utilization
Action
Compute (CPU)
> 70% sustained
40-60%
Horizontal scale
Memory
> 80%
50-70%
Vertical scale or tune GC
Database (connections)
> 80% pool use
50-70%
Connection pooler, scale up
Storage
> 75%
< 60%
Provision more, archive old data
Network throughput
> 70%
< 50%
Scale or upgrade links
Cost vs reliability trade-off: Headroom is expensive. Justify each component's
target with an SLO - a 99.9% availability SLO for a stateless service does not
require the same headroom as a 99.99% SLO for a payment processor.
Automated rollback triggers: Instrument your CD pipeline to roll back
automatically when error rate or latency breaches the canary threshold. Do not
rely on humans to catch canary regressions - the whole point is to automate the
decision. If your deployment tool does not support automated rollback, treat that
as a toil item to fix.
Feature flags vs canary: Canary deploys test infrastructure changes (binary,
container, config). Feature flags test product changes (code paths). Use both.
Separate the risk of deploying new infrastructure from the risk of activating new
behavior.
Gotchas
SLO window reset on the 1st creates budget gaming - Calendar month windows reset error budget on the 1st regardless of what happened on the 31st. Teams learn to push risky deploys right after reset. Use rolling 30-day windows which are always live and cannot be gamed.
Burn rate alerts with a single window produce too much noise - A 5-minute burn rate alert alone generates pages for transient spikes that self-recover. Multi-window alerting (5-minute AND 1-hour both elevated) dramatically reduces false positives while keeping sensitivity to real incidents.
Toil metrics without a reduction target are just bookkeeping - Measuring toil hours without committing to a reduction target and a sprint allocation to address it creates awareness without action. The measure only has value if it gates a quarterly automation investment.
Canary rollout with no automated rollback is manual canary - A canary that requires a human to notice the error rate spike and manually roll back is not a canary - it is a staged rollout with extra steps. Automated rollback on threshold breach is the defining property; without it, the safety benefit is largely absent.
On-call runbooks that say "escalate to engineering" - A runbook whose resolution step is "page someone else" does not reduce on-call burden; it just shifts it. Every runbook must include at least one concrete mitigation step the on-call can take before escalating.
Anti-patterns / common mistakes
Mistake
Why it is wrong
What to do instead
Setting SLOs without historical data
Targets become aspirational fiction, not engineering constraints
Measure current performance first, set SLO at or slightly below it
Alerting on resource utilization not SLOs
CPU at 90% may not affect users; 1% error rate definitely does
Alert on SLO burn rate; use resource metrics for capacity planning only
Explicitly state "no blame" in the template; focus every question on systems
Counting toil in hours but not automating it
Creates awareness without action
Budget one sprint per quarter specifically for toil reduction
Infinite error budget freezes
Teams freeze deploys forever, killing velocity
Define explicit budget policy with percentage thresholds and time-bounded freezes
On-call without runbooks
Every incident requires heroics; knowledge stays in individuals
Treat "alert without runbook" as a blocker; write the runbook during the incident
References
For detailed guidance on specific domains, load the relevant file from references/:
references/postmortem-template.md - full postmortem template with example entries, facilitation guide, and action item tracker
Only load a references file when the current task requires it.
Companion check
On first activation of this skill in a conversation: check which companion skills are installed by running ls ~/.claude/skills/ ~/.agent/skills/ ~/.agents/skills/ .claude/skills/ .agent/skills/ .agents/skills/ 2>/dev/null. Compare the results against the recommended_skills field in this file's frontmatter. For any that are missing, mention them once and offer to install:
