validating-production-slo

Validate live production releases against declared SLOs using multi-window burn-rate analysis.

Updated Apr 26, 2026
One-click install
npx skills add https://github.com/Jin9/skillify-foundation --skill validating-production-slo-jin9
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: validating-production-slo
Source: https://github.com/Jin9/skillify-foundation/tree/main/treasury/validating-production-slo
Command: npx skills add https://github.com/Jin9/skillify-foundation --skill validating-production-slo-jin9

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? After a deploy, teams need an objective verdict on whether a live release meets its declared SLOs before promoting, holding, or rolling back. This Skill measures live SLIs over a bake window, evaluates multi-window burn-rate, and emits a graded promote/hold/rollback recommendation without shifting traffic or altering alerts. ## Core Features & Use Cases - SLO Validation: Binds live SLI series to declared SLO definitions and evaluates error budgets with fast and slow burn-rate windows. - Graded Verdicts: Emits Pass, Marginal, or Fail grades mapped to promote, hold, or rollback recommendations with per-SLO judgements. - Schema-Validated I/O: Enforces JSON schemas for inputs (slo_defs, live_metrics, idempotency_key) and outputs (verdict, grade, per_slo, audit_id). - Use Case: After deploying a new service version, run post-release SLO validation over a 1-hour bake window to decide whether the release is healthy enough to promote or should be held for human review. ## Quick Start Validate the current production release against its declared SLOs using the live metrics from the last hour and tell me whether to promote, hold, or roll back.

Frequently Asked Questions about validating-production-slo

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I validate production SLOs after a deployment?▼

Provide the declared SLO definitions and live windowed metrics, then evaluate error-budget burn over fast and slow windows. The result is a Pass, Marginal, or Fail grade mapped to a promote, hold, or rollback recommendation.

What is multi-window burn-rate analysis for SLOs?▼

Multi-window burn-rate analysis computes error-budget consumption over a fast window (e.g. 1h) and a slow window (e.g. 6h) per SLO. A fast-burn breach triggers rollback, while an elevated slow burn triggers a hold for human review.

Can this skill create or modify SLIs, SLOs, or alerts?▼

No, it only consumes SLI/SLO definitions authored elsewhere and never authors or alters them, dashboards, or alerts. Use an observability-design skill for defining SLIs, SLOs, and alert rules.

When should I not use post-deploy SLO validation?▼

Do not use it for rollout-time canary-vs-baseline comparison, which belongs to canary analysis, or for an actively firing incident, which belongs to incident response. It also cannot return Pass on a window too short to be significant.

What happens when an SLO has no live SLI data?▼

The skill stops with a needs-input status and escalates to a human queue rather than emitting a verdict. Telemetry gaps or low-resolution data produce a hold recommendation with a note for human review.