/slo-design

Step through SLO design using the slo-architect skill. Produces an SLO definition, computes error budget + multi-window burn-rate alerts, and runs the reviewer to catch common bugs.

Usage

/slo-design
/slo-design --service checkout-svc --sli-type request-success-rate --target 99.9

Implementation

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

# Step 1: gather inputs (service, sli-type, target, window, owner)
# Step 2: render SLO definition
python "$SKILL/scripts/slo_designer.py" \
  --service "$SERVICE" \
  --sli-type "$SLI_TYPE" \
  --target "$TARGET" \
  --window-days "$WINDOW_DAYS" \
  --owner "$OWNER" \
  --policy-doc "$POLICY_DOC" \
  --format json > .slo.json

# Step 3: compute error budget + burn-rate alerts
python "$SKILL/scripts/error_budget_calculator.py" \
  --target "$TARGET" \
  --window-days "$WINDOW_DAYS"

# Step 4: render the markdown SLO for peer review
python "$SKILL/scripts/slo_designer.py" \
  --service "$SERVICE" \
  --sli-type "$SLI_TYPE" \
  --target "$TARGET" \
  --window-days "$WINDOW_DAYS" \
  --owner "$OWNER" \
  --policy-doc "$POLICY_DOC"

# Step 5: validate against the reviewer
echo "=== After saving the SLO, run slo_review.py against the doc ==="

Output

A markdown SLO definition with:

• Service, owner, user journey
• SLI type with numerator/denominator expressions
• Target, window, error budget
• Multi-window burn-rate alert thresholds (PromQL-shaped)
• Review cadence

Pre-conditions

• slo-architect skill installed
• Service identified
• 30 days of historical SLI data available (to pick a sustainable target)
• Error budget policy doc exists or will be created

Post-conditions

• .slo.json written for use with downstream tools (chaos-engineering blast radius, etc.)
• Markdown SLO streamed for review
• Recommendation printed: PASS / WARN / FAIL on slo_review.py checks