slo-architect

name	slo-architect
description	Use when defining, reviewing, or operating SLOs/SLIs/error budgets. Triggers on "define an SLO", "what should our SLO be", "error budget", "burn rate", "SLI", "service level objective", "Google SRE workbook", "multi-window burn-rate alert", or any reliability-target question. Ships SLO designer, error-budget calculator with multi-window burn-rate thresholds, and SLO reviewer that catches the common bugs (target too aggressive, window too short, conflicting SLOs, no SLI definition). 4 references on SLO principles + SLI design + error budget math + composition with feature-flags-architect/chaos-engineering/kubernetes-operator. NOT a generic observability skill — specifically the SLO discipline.
context	fork
version	2.9.0
author	claude-code-skills
license	MIT
tags	["slo","sli","sla","error-budget","burn-rate","sre","reliability","google-sre-workbook","observability"]
compatible_tools	["claude-code","codex-cli","cursor","antigravity","opencode","gemini-cli"]

SLO Architect

Define SLOs that mean something. Most "SLOs" in the wild are arbitrary numbers no one believes — 99.9% on every endpoint, no SLI definition, no error budget, no policy for what happens when budget burns. This skill enforces the discipline from Google's SRE Workbook: pick the right SLI, set a target users actually care about, calculate the error budget, wire multi-window burn-rate alerts, and have a written policy for when budget runs out.

When to use

• Defining a new SLO for a service or feature
• Reviewing existing SLOs for common bugs
• Picking the right SLI (event-based vs time-window based vs request-based)
• Computing error budgets and burn-rate alert thresholds
• Tying SLOs to existing controls — feature flags abort, chaos blast radius, operator capability levels

When NOT to use

• General observability strategy (metrics + logs + traces) → use observability-designer
• Customer-facing SLAs with legal teeth → that's contract drafting, not engineering
• Performance load testing (capacity, not reliability) → use performance-profiler
• Active incident response → use incident-response

Core principle: an SLO is a promise about user experience

SLI  ⟶  measurable signal of user-perceived health (e.g., HTTP 2xx rate)
SLO  ⟶  target for the SLI over a window (e.g., 99.9% over 30 days)
SLA  ⟶  customer-facing commitment with consequences (separate concern)
EB   ⟶  error budget: 100% − SLO target = how much "bad" you can spend
BR   ⟶  burn rate: how fast you're consuming the error budget

The four cardinal mistakes:

1. Target too high (99.99%+ on services that can't support it) — every minor blip violates SLO; alerts become noise.
2. Wrong SLI (CPU usage as proxy for user experience) — system can be "green" while users suffer.
3. No error budget policy — burning budget means nothing if there's no agreed action.
4. Single-window burn-rate alert — either too noisy (page on a 5-min spike) or too slow (notice budget exhausted after the fact).

The 3 tools below catch each of these.

Quick start

SKILL=engineering/slo-architect/skills/slo-architect

# 1. Design an SLO
python "$SKILL/scripts/slo_designer.py" \
  --service checkout-svc \
  --sli-type request-success-rate \
  --target 99.9 \
  --window-days 30

# 2. Compute error budget + multi-window burn-rate alerts
python "$SKILL/scripts/error_budget_calculator.py" \
  --target 99.9 --window-days 30

# 3. Review existing SLO definitions for common bugs
python "$SKILL/scripts/slo_review.py" --slo-doc docs/slos/

The 3 Python tools

All stdlib-only.

slo_designer.py

Generates a structured SLO definition with required fields. Refuses to render if any required field is missing (exit 1).

python scripts/slo_designer.py \
  --service checkout-svc \
  --sli-type request-success-rate \
  --target 99.9 \
  --window-days 30 \
  --owner team-checkout

SLI types supported:

• request-success-rate — (total_requests - bad_requests) / total_requests
• request-latency — count(requests < threshold) / total_requests
• availability-time — (window - downtime) / window
• data-freshness — count(data_age < threshold) / total_data_points
• correctness — count(correct_outputs) / total_outputs

Output is markdown by default with all required fields filled or marked <must define>. JSON output (--format json) is consumed by slo_review.py.

error_budget_calculator.py

Given target availability + window, computes:

• Allowed downtime in the window
• Multi-window burn-rate thresholds per Google SRE Workbook (Chapter 5):
  • Fast burn — page if 2% of monthly budget consumed in 1 hour
  • Slow burn — page if 10% consumed in 6 hours, ticket if 10% in 3 days
• Recommended alerting rules (PromQL-shaped output)

python scripts/error_budget_calculator.py --target 99.9 --window-days 30
python scripts/error_budget_calculator.py --target 99.95 --window-days 7 --format json

slo_review.py

Audits a directory of SLO definitions (markdown or JSON) for the common bugs.

python scripts/slo_review.py --slo-doc docs/slos/

Checks:

• target_too_high: target ≥ 99.99% (sustainable only with massive engineering investment)
• target_too_low: target ≤ 99.0% (probably wrong SLI; users will notice)
• window_too_short: window < 7 days (statistical noise dominates)
• window_too_long: window > 90 days (slow feedback)
• no_sli_definition: SLI section missing or vague ("everything OK")
• no_error_budget_policy: no documented action when budget burns
• cpu_as_sli: CPU/memory used as user-experience proxy (wrong signal)

SLI selection cheatsheet

User experience	SLI type	What you measure
"Did the request succeed?"	request-success-rate	2xx / total
"Was the response fast?"	request-latency	count(p99 < threshold) / total
"Was the service up?"	availability-time	(window - downtime) / window
"Is the data current?"	data-freshness	count(data_age < threshold) / total
"Was the answer correct?"	correctness	count(correct) / total

See references/sli_design.md for examples and anti-patterns.

Error budget math (the basics)

For 99.9% SLO over 30 days:

• Allowed unavailability: 0.1% × 30 × 24 × 60 = 43.2 minutes
• 1-hour fast-burn threshold (2% of monthly budget burned): 2% × 43.2 / 60 ≈ 1.44 ratio multiplier
• 6-hour slow-burn threshold (10% in 6h): 10% × 43.2 / 360 ≈ 0.6 ratio multiplier

error_budget_calculator.py does this math for you and emits ready-to-paste alert rules.

Composition with the rest of the portfolio

This skill explicitly composes with three others:

Skill	Composition
feature-flags-architect	Rollout abort criteria reference SLO burn-rate thresholds
chaos-engineering	Blast-radius calculator already takes monthly error budget as input — define it here
kubernetes-operator	Operator capability L4 (Deep Insights) requires SLOs + Prometheus rules

The error_budget_calculator.py output is in the same shape engineering/skills/chaos-engineering/scripts/blast_radius_calculator.py expects on stdin.

Workflows

Workflow 1: Define a new SLO

1. Pick the user journey to protect (e.g., "checkout completion").
2. Choose SLI type (request-success-rate, latency, availability, freshness, correctness).
3. Define the SLI precisely: numerator/denominator with concrete labels.
4. Pick a target by measuring 30 days of historical SLI value:
     target = floor(p50 of last 30 days × 100) / 100
   This avoids targets the system has never sustained.
5. Pick a window (28 days = 4 calendar weeks, recommended).
6. Run slo_designer.py to render the SLO definition.
7. Run error_budget_calculator.py to get burn-rate alerts.
8. Write the error budget policy (what happens when budget burns).
9. Run slo_review.py — must pass before the SLO is "live".

Workflow 2: Quarterly SLO review

1. For every active SLO, run slo_review.py — fix any FAIL findings.
2. Look at last quarter's data:
   - Was the SLO too easy (never burned budget)? Tighten target.
   - Was it too hard (frequently burned)? Loosen target OR fix the system.
   - Did burn-rate alerts fire usefully (not too noisy, not too late)? Adjust thresholds.
3. Audit error budget policies — were they actually followed when budget burned?
4. Commit revised SLOs; archive old versions with date stamps.

Workflow 3: SLO-driven rollback

1. New deploy starts burning error budget faster than baseline.
2. Burn-rate alert fires (from error_budget_calculator.py thresholds).
3. Auto-rollback via feature flag (kill switch from feature-flags-architect).
4. Postmortem feeds into next SLO revision.

References

• references/slo_principles.md — SLI vs SLO vs SLA, Google SRE Workbook canon
• references/sli_design.md — picking the right SLI; 5 types with examples
• references/error_budget.md — error budget math, burn-rate alerts, budget policy
• references/composition.md — how SLOs feed feature flags, chaos, operators

Slash command

/slo-design — interactive SLO design wizard that runs all 3 tools.

Asset templates

• assets/slo_template.yaml — fillable SLO YAML
• assets/error_budget_policy.md — fillable policy template

Anti-patterns

• 99.99% on every endpoint — copy-paste SLOs that nobody verified the system can sustain
• CPU usage as SLI — system metrics aren't user experience
• Single-window burn-rate alert — too noisy if 5-min, too slow if 30-day
• No error budget policy — burning budget means nothing without an action
• SLOs without owners — no one is responsible; they bit-rot
• SLOs reviewed once a year — system characteristics change faster than that
• SLAs in the SLO doc — different audience, different stakes; keep them separate
• SLO target = SLA target — SLO must be tighter (you should beat your contract before customers notice)

Verifiable success

A team using this skill should achieve:

• 100% of SLOs pass slo_review.py with 0 FAIL findings
• Every SLO has a documented owner, error budget, burn-rate alerts, and policy
• Burn-rate alerts fire ≤2 times/month per SLO that's hit (signal, not noise)
• Mean time to detect SLO violation: <30 min (multi-window burn-rate alerts working)
• Quarterly SLO review happens every quarter (not annually)