4 Agent Skills for Backend Engineers: Design Review, Zero-Downtime Migration, Production GenAI, Forecasting

说明与用途

针对大型后端系统的自动化检查清单，用于设计评审、零停机迁移、GenAI 生产就绪和预测，提升可靠性并加速交付。

问题

Backend teams need repeatable, automated reviews for design, migration, GenAI readiness, and forecasting to avoid incidents and downtime.

结果

Automated, repeatable reviews that surface real incident failure modes, ensure zero‑downtime migration plans are reversible, validate GenAI production readiness, and catch forecasting pitfalls, leading to higher reliability and faster delivery.

工作流

• Install the skills package via npx skills add shubhamkumbhalkar/skills on any agent (Claude Code, Codex, Cursor).
• Select the appropriate skill: distributed‑systems‑design‑review, zero‑downtime‑migration, genai‑production‑readiness, or time‑series‑forecasting‑review.
• Point the skill at the target artifact (design doc, PR, migration plan, or forecasting model).
• Let the agent run the checklist, generating a report with failure scenarios, blast radius, fixes, and a verdict (Ship / Ship‑with‑changes / Needs‑rework).
• Review the report, apply recommended fixes, and re‑run if necessary.
• Use the final verdict to decide whether to ship or iterate.
• Share findings with the team and iterate on the skill if gaps are found.

证据

Point it at a design doc or PR and it reviews for the failure modes that cause real incidents: retries without idempotency, retry storms with no circuit breaker, missing backpressure, cross‑store write drift, hot partitions, and missing golden‑signal observability.

来源

This skill produces a phased plan built on one rule: every phase is independently reversible. Expand (dual‑write), backfill (idempotent, reconciled), shadow‑verify against real traffic, incremental cutover (1/5/25/50/100%), then contract.

来源

This skill checks the things a demo skips: an eval set with a regression gate, grounding and hallucination handling, prompt‑injection defense on untrusted input, a human‑in‑the‑loop boundary for consequential actions, cost/latency bounds and fallbacks, PII handling, and observability.

来源

Forecasting is deceptively easy to get wrong: leakage and bad backtests inflate offline metrics, then quiet drift erodes them in production.

来源