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Analyser les problèmes de cluster Kubernetes via MCP avec K8sGPT

Exécutez K8sGPT en tant que serveur MCP pour qu'un agent puisse analyser un cluster Kubernetes, expliquer les ressources défaillantes et retourner des indices de correction priorisés en langage naturel.

Qu'est-ce que Analyser les problèmes de cluster Kubernetes via MCP avec K8sGPT ?

Cette compétence permet à un agent IA (comme Claude Code, Cursor ou Codex) d'analyser un cluster Kubernetes à l'aide de K8sGPT via le protocole MCP. Elle transforme K8sGPT en serveur MCP, ce qui permet à l'agent de scanner les ressources du cluster, d'identifier les composants défaillants et de fournir des explications ainsi que des suggestions de correction priorisées en langage naturel. C'est particulièrement utile pour les développeurs et les ingénieurs DevOps qui souhaitent diagnostiquer rapidement les problèmes de cluster sans avoir à interpréter manuellement des logs complexes. La compétence s'appuie sur K8sGPT v0.4.14 ou ultérieur, nécessite une clé API OpenAI (par défaut), et peut être intégrée à des clients compatibles MCP comme Claude Desktop. Elle repose sur l'installation préalable de K8sGPT (via Homebrew, compilation, ou gestionnaire de paquets) et la configuration de l'accès au cluster Kubernetes.

Compatible avec✓Claude Code✓Codex CLI~Cursor
npx skills add https://github.com/agentskillexchange/skills/tree/main/skills/analyze-kubernetes-cluster-issues-through-mcp-with-k8sgpt
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Documentation

Que fait Analyser les problèmes de cluster Kubernetes via MCP avec K8sGPT ?

Exécutez K8sGPT en tant que serveur MCP pour qu'un agent puisse analyser un cluster Kubernetes, expliquer les ressources défaillantes et retourner des indices de correction priorisés en langage naturel.

Prérequis

Un cluster Kubernetes accessible, K8sGPT, et un client compatible MCP

Installation

Utilisez le chemin d'installation standard ou adapté à votre environnement :

  • brew install k8sgpt
  • brew tap k8sgpt-ai/k8sgpt
  • compilé à partir des sources. k8sgpt Install Clang ou exécutez brew install gcc.

Exigences et mises en garde de la source officielle :

  • K8sGPT peut être intégré à Claude Desktop pour fournir une analyse de cluster Kubernetes alimentée par l'IA. Cette intégration nécessite K8sGPT v0.4.14 ou ultérieur.
  • L'analyseur Node a pris 160.109833ms
  • Node

Notes d'utilisation de base ou de démarrage :

Documentation

Source

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

Academic Paper Citation Network Mapper

Builds citation networks from Semantic Scholar API and CrossRef DOI metadata. Visualizes paper influence graphs using NetworkX, identifies seminal works, and tracks research lineage across fields.

ACF Custom Fields REST Exposer

Exposes Advanced Custom Fields data through the WordPress REST API using register_rest_field and acf_format_value. Handles repeater fields, flexible content layouts, and gallery fields with proper serialization.

ACF Extended for Advanced Custom Fields Workflows

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ACF to Gutenberg Block Migrator

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Activepieces Open Source Workflow Automation

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ActivityWatch Cross-Platform Automated Time Tracker and Producti

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act Local GitHub Actions Runner

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Add a browser review checkpoint before risky agent actions with

Use AgentClick when an agent should pause before risky commands, plans, drafts, or code changes so a human can inspect, edit, approve, or reject them in a purpose-built browser UI.

Add Claude design-system and accessibility workflows with UX/UI

Use UX/UI Agent Skills when Claude should generate tokens, component specs, accessibility audits, and framework-specific UI code from a repeatable design workflow.

Add executable smoke tests for shell scripts and CLIs before ref

Use Bats-core when an agent needs to turn fragile shell scripts or command-line workflows into something it can verify repeatedly after edits. The agent writes focused Bash tests for success paths, failure paths, and output contracts, then runs them locally or in CI before a refactor, release, or incident fix goes out.

Add graph-backed memory and context retrieval to agent workflows

Use Cognee to ingest project knowledge into graph and vector memory so agents can retrieve durable context across sessions and workflows.

Add IDE-style semantic code navigation and symbol-safe refactors

Give a coding agent symbol-aware lookup, cross-file rename, and structural edit tools before it starts making brittle text-only changes.

Add managed long-term memory to LangGraph agents with LangMem

Give LangGraph agents memory management and search tools so they can store, retrieve, and update durable facts across sessions.

Add multimodal lifelong memory to MCP and Python agents with Sim

Use SimpleMem to store, compress, index, and retrieve text or multimodal memories for agents through MCP or Python integrations.

Add persistent project memory and hook-driven synthesis with Cla

Keep Claude Code sessions grounded in prior decisions, project context, and daily handoff notes instead of starting from zero every time.

Add Postgres-native vector retrieval to agent and RAG workflows

Store embeddings beside application data in Postgres, create vector indexes, and query nearest neighbors for semantic search, RAG, recommendations, or agent memory retrieval.

Address GitHub PR review comments from the current branch with g

Find the open PR for the current branch, gather unresolved review comments, and drive a focused comment-resolution workflow with gh-authenticated context.

Add runtime guardrails to TypeScript agents with VoltAgent

Use VoltAgent to intercept, validate, and enforce input/output policies in TypeScript agent workflows.

Add temporal context memory to agent workflows with Zep

Use Zep as an external context layer for agents that need to store events, assemble temporal graph context, and retrieve relevant memory before model calls.

Adobe Firefly API Batch Image Generator

Calls Adobe Firefly

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