mcp-data-observability-integration
De la autor
Guides agents to wire Model Context Protocol servers for live data platform observability including Spark execution plans, OOM diagnosis, Kafka consumer lag, and orchestration run state. Use when agents need cluster metrics, streaming lag, or job diagnostics instead of blind code changes.
Instalare
npx skills add vaquarkhan/data-engineering-agent-skills --skill mcp-data-observability-integration -a claude-code
Se instalează în .claude/skills/mcp-data-observability-integration/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Identic octet cu octet cu commit-ul scanat, cu licența depozitului și un fișier SOURCE.txt.
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De ce 78
aiqbench calculează scorul de încredere din semnale publice. Nu este un audit de securitate, așa că citește un skill înainte să-l instalezi.
Skill-uri similare
Cele mai apropiate de acesta prin ce fac și când sunt folosite.
- agent-observabilityBagelHole/DevOps-Security-Agent-SkillsInstrument AI agents with tracing, token metrics, latency, and cost visibility. Use for reliability and debugging.DevOps★ 1.2K
- data-observability-and-sla-managementvaquarkhan/data-engineering-agent-skillsGuides agents through data observability and service-level management. Use when defining or improving freshness, completeness, anomaly detection, alerting, lag tracking, run metadata, and ownership for production data products.DevOps★ 59
- mcp-setupLeadMagic/gtm-skillsConfigure Model Context Protocol servers for GTM workflows — server selection, tool scope, permissions, guardrails, observability, and multi-tool orchestration. Use when connecting CRM, enrichment, sequencing, analytics, or support tools to AI agents through MCP.DevOps★ 68
- observabilitynahid-sparktales/agent-dispatcherInstrument a service so the questions asked during an incident are answerable from data already being collected — rate, errors, latency distribution and saturation per route, structured events carrying a correlation id that survives process and queue boundaries, and alerts on symptoms users feel. Use before a service or a new critical path goes to production, after an incident that ended in "we had no data for that", or when adding a dependency, queue or job whose failure would be silent. Not for fighting a live outage (incident-response), not for profiling a known-slow path (performance-profiling), and not for tracing agent or LLM runs (llm-observability).DevOps★ 59
- mcp-managementMicrock/ordinary-claude-skillsManage Model Context Protocol (MCP) servers - discover, analyze, and execute tools/prompts/resources from configured MCP servers. Use when working with MCP integrations, need to discover available MCP capabilities, filter MCP tools for specific tasks, execute MCP tools programmatically, access MCP prompts/resources, or implement MCP client functionality. Supports intelligent tool selection, multi-server management, and context-efficient capability discovery.Planificare★ 408
- llm-observabilitynahid-sparktales/agent-dispatcherSee what an agent actually did — one trace per run with nested model, tool and retrieval spans, token and latency accounted per step, and failures clustered by mechanism instead of read one at a time. Use when an agent misbehaves in ways you cannot reproduce, when cost or latency is unexplained, when "it sometimes fails" is the whole bug report, or before writing evals when you do not yet know which failures exist. Not for judging whether an output is correct (agent-evals), and not a replacement for the application's own monitoring.DevOps★ 59
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