菜单
Applies SRE practices for production reliability, service ownership, resilience, toil reduction and operability. Use when owning production services, defining SLOs, or improving operational maturity.
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基于 SOC 职业分类
Governs AI models through inventory, purpose, risk tier, approval, monitoring, drift, kill-switch and retirement. Use when introducing, changing or operating models in production or agent workflows.
Applies an Asimov-inspired hierarchy of AI safety laws when material AI or instruction risk is present. Use when harm, unlawful requests, authority conflicts, or safety trade-offs need an explicit law hierarchy. Also known as apply_laws_of_AI.
Use when implementing input validation, output filtering, tool restrictions, policy enforcement, secure coding controls, destructive-tool guardrails, approval gates, or safe agent behaviour.
Use when starting a session or deciding which local skill applies; acts as the meta-skill for discovering and loading the smallest relevant skill.
Use when measuring coding-agent quality, RAG evaluation, regression risk, latency, cost, reliability, safety, or production performance for agent systems.
Designs ontology-driven knowledge graphs for explainable KG-RAG. Use when modelling semantic graphs, ontologies, or graph schemas for retrieval.
| name | sre-practice |
| description | Applies SRE practices for production reliability, service ownership, resilience, toil reduction and operability. Use when owning production services, defining SLOs, or improving operational maturity. |
| aliases | ["site-reliability-engineering","service-reliability","production-reliability","service-ownership"] |
Use this skill when working on production service design, operational readiness, reliability reviews, service ownership, resilience improvements, availability, latency, durability, on-call readiness, runbooks, safe automation, incident prevention or operational risk reduction.
Improve service reliability by connecting user impact, service objectives, operational evidence, automation, incident learning and safe release practices.
Service: What service or workflow is affected?
User impact: What user-visible failure are we preventing or reducing?
Objective: What reliability target or SLO is relevant?
Signal: How will we know the service is healthy or unhealthy?
Response: What should happen when the signal breaches threshold?
Recovery: How is the service restored?
Evidence: What metric, trace, log, test or runbook proves the change?
Prefer bounded retries with timeouts, circuit breakers, idempotent operations, meaningful health checks, graceful degradation, bulkheads, runbooks, clear service ownership and dashboards aligned to SLIs.
For product-specific DataOps/MCP examples, load skills_docs/overlays/mas-dataops-mcp-overlay.md.