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:

Target too high (99.99%+ on services that can't support it) — every minor blip violates SLO; alerts become noise.
Wrong SLI (CPU usage as proxy for user experience) — system can be "green" while users suffer.
No error budget policy — burning budget means nothing if there's no agreed action.
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 - 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)

bash

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.

bash

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

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)

slo-architect

NPX Install

Tags

SKILL.md Content

SLO Architect

When to use

When NOT to use

Core principle: an SLO is a promise about user experience

Quick start

The 3 Python tools

`slo_designer.py`

`error_budget_calculator.py`

`slo_review.py`

SLI selection cheatsheet

Error budget math (the basics)

Composition with the rest of the portfolio

Workflows

Workflow 1: Define a new SLO

Workflow 2: Quarterly SLO review

Workflow 3: SLO-driven rollback

References

Slash command

Asset templates

Anti-patterns

Verifiable success