slo-architect

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---
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

```bash
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`).

```bash
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