{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-weibaohui-k8m","slug":"weibaohui-k8m","name":"weibaohui/k8m","type":"repo","url":"https://github.com/weibaohui/k8m","page_url":"https://unfragile.ai/weibaohui-k8m","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-weibaohui-k8m__cap_0","uri":"capability://automation.workflow.multi.cluster.kubernetes.resource.discovery.and.dynamic.crud.operations","name":"multi-cluster kubernetes resource discovery and dynamic crud operations","description":"Implements a Dynamic Resource Controller that abstracts Kubernetes API operations across multiple clusters using a query builder and filtering system. Resources are discovered dynamically via the kom library integration, supporting both standard Kubernetes resources and Custom Resource Definitions (CRDs). The system maintains real-time resource caching with watch mechanisms and provides batch operations for bulk resource manipulation across clusters with namespace-level access control enforcement.","intents":["Query and list Kubernetes resources across multiple clusters with filtering and sorting","Create, update, delete, and patch resources in specific namespaces with permission validation","Watch resource changes in real-time and maintain synchronized cache state","Perform batch operations on multiple resources across clusters atomically"],"best_for":["DevOps teams managing multiple Kubernetes clusters","Platform engineers building multi-tenant cluster management systems","Developers automating infrastructure operations at scale"],"limitations":["Resource enrichment and aggregation adds latency proportional to cluster count","Watch mechanisms require persistent WebSocket connections per cluster","Batch operations are not transactional across clusters — partial failures possible","CRD support depends on proper schema registration in each cluster"],"requires":["Kubernetes 1.16+ clusters with API server access","Valid kubeconfig files or service account credentials for each cluster","Network connectivity from k8m instance to all cluster API servers","Go 1.18+ for building from source"],"input_types":["kubeconfig YAML","resource manifests (YAML/JSON)","query filters (label selectors, field selectors)","batch operation payloads (JSON)"],"output_types":["structured resource objects (JSON)","resource lists with metadata","operation status and results","real-time event streams"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-weibaohui-k8m__cap_1","uri":"capability://automation.workflow.interactive.pod.shell.access.and.terminal.multiplexing","name":"interactive pod shell access and terminal multiplexing","description":"Provides WebSocket-based interactive shell access to running pods using Kubernetes exec API with terminal multiplexing capabilities. The system establishes bidirectional communication channels for stdin/stdout/stderr, handles terminal resize events, and maintains session state across reconnections. Supports multiple concurrent shell sessions per pod with isolated I/O streams and automatic cleanup on disconnection.","intents":["Execute interactive commands inside running pods for debugging and troubleshooting","Maintain persistent shell sessions with terminal emulation (resize, signal handling)","Access multiple pods simultaneously with separate terminal windows","Debug application issues in production without SSH access to nodes"],"best_for":["DevOps engineers debugging containerized applications","Application developers troubleshooting production issues","SREs performing incident response and root cause analysis"],"limitations":["Requires pod to have a shell binary (sh, bash) — fails on distroless images","WebSocket connections are stateless — session loss on network interruption requires reconnection","Terminal emulation is basic — complex TUI applications may not render correctly","No session recording or audit logging of shell commands by default"],"requires":["Running pod with shell access (sh or bash)","Kubernetes API server with exec subresource enabled","WebSocket support in client browser or terminal","Network connectivity to Kubernetes API server"],"input_types":["pod name and namespace","container name (optional, defaults to first)","shell commands (text)","terminal control sequences (resize, signals)"],"output_types":["terminal output (stdout/stderr streams)","command exit codes","terminal dimensions and control events"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-weibaohui-k8m__cap_10","uri":"capability://tool.use.integration.plugin.system.with.dynamic.loading.and.lifecycle.management","name":"plugin system with dynamic loading and lifecycle management","description":"Implements a plugin architecture that allows dynamic loading of Go plugins at runtime with standardized lifecycle hooks (init, start, stop, shutdown). Plugins are organized into categories (core infrastructure, operational, AI/MCP) and can register custom resource controllers, API endpoints, and event handlers. The system manages plugin dependencies, version compatibility, and provides plugin configuration through YAML files.","intents":["Extend k8m functionality without modifying core codebase","Add custom resource controllers for domain-specific Kubernetes resources","Integrate third-party tools and services through plugin API","Develop organization-specific operational plugins for internal workflows"],"best_for":["Organizations building custom extensions to k8m","Developers integrating third-party Kubernetes tools","Teams maintaining internal operational plugins"],"limitations":["Go plugins require compilation against exact k8m version — binary compatibility issues common","Plugin loading is synchronous — slow plugins block k8m startup","No plugin sandboxing — malicious plugins have full k8m access","Plugin API is Go-only — no support for Python, Rust, or other languages"],"requires":["Go 1.18+ for plugin compilation","k8m source code for plugin development","Plugin configuration YAML in k8m config directory","Exact k8m version match for plugin binary compatibility"],"input_types":["plugin Go source code","plugin configuration (YAML)","plugin dependencies (Go modules)"],"output_types":["plugin status (loaded/failed/disabled)","plugin version and metadata","plugin error logs","registered endpoints and handlers"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-weibaohui-k8m__cap_11","uri":"capability://planning.reasoning.ai.powered.cluster.analysis.and.k8sgpt.integration","name":"ai-powered cluster analysis and k8sgpt integration","description":"Integrates with K8sGPT and configurable AI models (OpenAI, Anthropic, local LLMs) to analyze cluster state and provide intelligent troubleshooting recommendations. The system sends cluster diagnostics to AI models, processes responses, and presents findings in the UI. Supports analysis of pod failures, resource issues, security misconfigurations, and best practice violations with AI-generated explanations and remediation steps.","intents":["Get AI-powered explanations for cluster issues and pod failures","Receive remediation recommendations from AI analysis of cluster state","Analyze security misconfigurations and best practice violations","Reduce MTTR by automating root cause analysis"],"best_for":["DevOps teams reducing incident response time","Organizations without deep Kubernetes expertise","Teams automating cluster troubleshooting workflows"],"limitations":["AI analysis requires external API calls — adds 2-5 second latency per analysis","AI responses are non-deterministic — same issue may get different explanations","API costs scale with cluster size and analysis frequency","AI models may hallucinate or provide incorrect recommendations — human verification required"],"requires":["AI model API key (OpenAI, Anthropic, or compatible)","Network connectivity to AI API endpoints","K8sGPT binary or compatible analysis tool","Sufficient API quota for analysis requests"],"input_types":["cluster state (resources, events, logs)","pod failure details","error messages and logs","AI model configuration"],"output_types":["AI analysis results (natural language)","root cause explanations","remediation recommendations","severity assessment"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-weibaohui-k8m__cap_12","uri":"capability://automation.workflow.webhook.based.event.notifications.with.custom.payload.formatting","name":"webhook-based event notifications with custom payload formatting","description":"Sends cluster events and inspection results to external webhooks with customizable payload formatting and retry logic. The system batches events, formats them according to webhook configuration, and implements exponential backoff retry on failure. Supports multiple webhook endpoints with different event filters and payload templates, enabling integration with Slack, PagerDuty, custom monitoring systems, and other external services.","intents":["Send cluster events to external notification systems (Slack, PagerDuty, email)","Integrate k8m events with custom monitoring and alerting platforms","Trigger external workflows based on cluster state changes","Maintain audit trail of cluster events in external systems"],"best_for":["Teams integrating k8m with existing notification infrastructure","Organizations requiring event-driven automation","DevOps teams centralizing cluster event management"],"limitations":["Webhook delivery is asynchronous — no guarantee of delivery","Retry logic adds latency — failed webhooks retry for up to 24 hours","Payload formatting is template-based — complex transformations require custom code","No webhook signature verification — relies on HTTPS and network isolation"],"requires":["External webhook endpoint (HTTP/HTTPS)","Webhook configuration in k8m database","Network connectivity from k8m to webhook endpoints","Event filtering rules (optional)"],"input_types":["cluster events (pod failures, resource changes, inspections)","webhook endpoint URL","payload template (JSON/text)","event filters (label selectors, event types)"],"output_types":["webhook delivery status (success/failed/retrying)","formatted event payload (JSON/text)","delivery logs and retry history"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-weibaohui-k8m__cap_13","uri":"capability://automation.workflow.web.based.ui.with.amis.framework.and.ai.enhanced.components","name":"web-based ui with amis framework and ai-enhanced components","description":"Provides a web-based management interface built with AMIS framework featuring responsive layouts, custom Kubernetes-aware components, and AI-enhanced UI elements. The UI includes cluster/namespace selection, resource browsing with filtering, pod operations (logs, shell, metrics), and AI chat integration. Components are customized for Kubernetes workflows with kubeconfig editors, YAML validators, and real-time resource status displays.","intents":["Browse and manage Kubernetes resources through web interface without CLI","Perform pod operations (logs, shell, port-forward) through UI","Chat with AI about cluster issues and get recommendations","Monitor cluster health and resource usage in real-time"],"best_for":["Non-technical users managing Kubernetes clusters","DevOps teams preferring web UI over CLI","Organizations requiring audit trail of UI-based operations"],"limitations":["AMIS framework adds ~500KB JavaScript bundle size","Custom components require React/TypeScript knowledge for customization","Real-time updates use polling — not suitable for high-frequency changes","UI performance degrades with large resource counts (1000+ pods)"],"requires":["Modern web browser (Chrome, Firefox, Safari, Edge)","JavaScript enabled","Network connectivity to k8m backend","k8m backend API running"],"input_types":["user interactions (clicks, form inputs)","cluster/namespace selections","resource filters and searches","AI chat messages"],"output_types":["rendered resource lists and details","real-time logs and metrics","AI chat responses","operation status and results"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-weibaohui-k8m__cap_14","uri":"capability://safety.moderation.jwt.token.based.authentication.with.stateless.session.management","name":"jwt token-based authentication with stateless session management","description":"Implements JWT token-based authentication system for stateless session management without server-side session storage. Tokens contain user identity, roles, and namespace assignments, signed with configurable algorithms (HS256, RS256). The system validates tokens on each request, extracts user context, and enforces permissions based on token claims. Supports token refresh, expiration, and revocation through blacklist mechanism.","intents":["Authenticate users without server-side session storage","Maintain user context across stateless API requests","Enforce role-based permissions from token claims","Support token refresh and expiration for security"],"best_for":["Stateless API architectures and microservices","Organizations requiring scalable authentication","Teams implementing API-first Kubernetes management"],"limitations":["Token revocation requires blacklist — adds database lookup per request","Token claims are immutable — permission changes require token refresh","No built-in OIDC/SAML support — requires custom integration","Token expiration is fixed — no sliding window refresh"],"requires":["JWT signing key (HS256 secret or RS256 private key)","Token validation middleware in API layer","Database for token blacklist (optional)","Token refresh endpoint implementation"],"input_types":["user credentials (username/password or OIDC token)","JWT token (in Authorization header)","token refresh request"],"output_types":["JWT token (signed)","token claims (user ID, roles, namespaces)","token expiration timestamp","refresh token (optional)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-weibaohui-k8m__cap_15","uri":"capability://automation.workflow.file.operations.in.pods.with.upload.download.and.directory.browsing","name":"file operations in pods with upload/download and directory browsing","description":"Provides file operations within running pods including upload, download, and directory browsing through Kubernetes exec API. The system uses tar streaming for efficient file transfer, handles binary files, and maintains file permissions. Supports recursive directory operations and provides progress tracking for large file transfers.","intents":["Download logs, configs, or data files from running pods","Upload configuration files or patches to pods for testing","Browse pod filesystem to understand application state","Extract diagnostic data from pods without SSH access"],"best_for":["DevOps engineers debugging pod issues","Developers extracting application data from pods","SREs performing incident investigation"],"limitations":["Requires tar binary in pod — fails on minimal/distroless images","File transfer uses tar streaming — adds overhead vs direct file access","No file permission preservation — all files transferred with default permissions","Large file transfers may timeout — no resume capability"],"requires":["Running pod with tar and shell (sh/bash)","Kubernetes API server with exec subresource enabled","Network connectivity to Kubernetes API server","Sufficient pod disk space for temporary tar archives"],"input_types":["pod name and namespace","source/destination file paths","directory paths for browsing","file content (for uploads)"],"output_types":["file content (binary or text)","directory listing (JSON)","file metadata (size, permissions, modified time)","transfer progress and status"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-weibaohui-k8m__cap_16","uri":"capability://automation.workflow.cluster.discovery.and.dynamic.kubeconfig.registration","name":"cluster discovery and dynamic kubeconfig registration","description":"Supports dynamic cluster registration through kubeconfig upload/paste with automatic cluster discovery and validation. The system parses kubeconfig files, validates cluster connectivity, extracts cluster metadata, and stores configurations securely. Supports multiple kubeconfig formats and automatically detects cluster version, API capabilities, and available resources.","intents":["Add new clusters to k8m management without manual configuration","Validate cluster connectivity before adding to management","Automatically detect cluster capabilities and resource types","Support multiple kubeconfig formats and authentication methods"],"best_for":["DevOps teams managing dynamic cluster infrastructure","Organizations with frequently changing cluster inventory","Teams automating cluster onboarding workflows"],"limitations":["Kubeconfig parsing is strict — malformed configs fail validation","Cluster validation requires API server connectivity — fails for offline clusters","No automatic kubeconfig rotation — requires manual update on credential expiration","Stored kubeconfigs are sensitive — requires secure storage and access control"],"requires":["Valid kubeconfig file with cluster credentials","Network connectivity from k8m to cluster API server","Kubernetes API server with discovery API enabled","Secure storage for kubeconfig (encrypted database)"],"input_types":["kubeconfig YAML/JSON","cluster name (optional, auto-detected)","authentication credentials (embedded in kubeconfig)"],"output_types":["cluster metadata (name, version, API server URL)","available resource types (CRDs, built-in resources)","cluster connectivity status","validation errors (if any)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-weibaohui-k8m__cap_17","uri":"capability://automation.workflow.database.backed.configuration.management.with.hot.reload.support","name":"database-backed configuration management with hot-reload support","description":"Stores k8m configuration in database with support for hot-reload of configuration changes without restart. The system watches configuration tables for changes, reloads affected components, and maintains backward compatibility. Supports configuration versioning and rollback, with audit logging of configuration changes.","intents":["Update k8m configuration without restarting service","Maintain configuration history and enable rollback","Audit configuration changes for compliance","Support dynamic configuration updates for multi-instance deployments"],"best_for":["Production deployments requiring zero-downtime configuration updates","Organizations with compliance requirements for configuration auditing","Teams managing multiple k8m instances"],"limitations":["Hot-reload adds database polling overhead — ~1 query per 5 seconds","Some configuration changes require restart — not all settings support hot-reload","Configuration versioning adds database storage overhead","Concurrent configuration updates may cause race conditions"],"requires":["Database backend (PostgreSQL, MySQL, SQLite)","Database schema with configuration tables","Configuration watch mechanism (polling or triggers)","Audit logging tables"],"input_types":["configuration key-value pairs","configuration sections (YAML-like structure)"],"output_types":["configuration values (typed)","configuration version history","audit logs (who changed what when)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-weibaohui-k8m__cap_2","uri":"capability://automation.workflow.aggregated.log.streaming.and.filtering.from.multiple.pods","name":"aggregated log streaming and filtering from multiple pods","description":"Streams and aggregates logs from multiple pods across clusters using Kubernetes log API with real-time filtering, search, and tail capabilities. The system supports log aggregation from multiple containers per pod, timestamp-based filtering, and label-based pod selection. Logs are streamed via WebSocket with configurable buffer sizes and can be filtered by log level, keywords, or regex patterns without requiring external log aggregation infrastructure.","intents":["Stream logs from multiple pods in real-time for monitoring and debugging","Search historical logs with keyword and regex filtering across pod replicas","Tail logs from specific containers with timestamp-based filtering","Aggregate logs from pods matching label selectors without external ELK/Loki setup"],"best_for":["DevOps teams without centralized logging infrastructure (ELK, Loki)","Developers debugging application issues in staging/production","SREs performing incident investigation with quick log access"],"limitations":["No persistent log storage — logs are lost when pods are deleted or restarted","Filtering is client-side — large log volumes require downloading full logs first","No structured logging support — JSON logs are treated as plain text","Kubelet log rotation limits historical log access to recent entries only"],"requires":["Kubernetes 1.10+ with logs API endpoint","Pods writing logs to stdout/stderr (not sidecar log files)","Network connectivity to Kubernetes API server","Sufficient kubelet disk space for log retention"],"input_types":["pod name, namespace, container name","label selectors for pod filtering","timestamp ranges (RFC3339 format)","filter patterns (regex, keywords)","tail line count"],"output_types":["log line streams (text)","filtered log results (JSON with metadata)","log statistics (line count, time range)"],"categories":["automation-workflow","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-weibaohui-k8m__cap_3","uri":"capability://automation.workflow.pod.port.forwarding.with.automatic.tunnel.lifecycle.management","name":"pod port forwarding with automatic tunnel lifecycle management","description":"Establishes port forwarding tunnels from local ports to pod service ports using Kubernetes port-forward API with automatic lifecycle management. The system handles tunnel creation, connection pooling, and graceful cleanup on client disconnect. Supports multiple concurrent port forwards per pod and provides tunnel status monitoring with automatic reconnection on failure.","intents":["Access pod services locally without exposing them via Ingress or LoadBalancer","Debug services by connecting local tools (database clients, API testers) to pod ports","Establish secure tunnels to internal services for development and testing","Manage multiple concurrent port forwards with automatic cleanup"],"best_for":["Developers testing services locally without public exposure","DevOps engineers debugging internal service connectivity","QA teams accessing staging services for testing"],"limitations":["Port forwards are ephemeral — lost on pod restart or network interruption","Local port must be available — conflicts with existing services cause failures","No authentication/authorization on forwarded port — relies on network isolation","Bidirectional tunneling adds ~50-100ms latency vs direct pod access"],"requires":["Running pod with listening service port","Kubernetes API server with portforward subresource enabled","Local port availability (1024-65535)","Network connectivity to Kubernetes API server"],"input_types":["pod name and namespace","pod port number","local port number (optional, auto-assigned if omitted)"],"output_types":["tunnel status (active/inactive)","local port assignment","connection statistics","error messages on failure"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-weibaohui-k8m__cap_4","uri":"capability://data.processing.analysis.pod.resource.usage.metrics.collection.and.visualization","name":"pod resource usage metrics collection and visualization","description":"Collects and aggregates pod resource metrics (CPU, memory, network I/O) from Kubernetes metrics-server API with real-time updates and historical trending. The system queries metrics API for current usage, calculates resource utilization percentages against requests/limits, and provides time-series data for visualization. Supports aggregation across multiple pods and namespaces with configurable sampling intervals.","intents":["Monitor pod CPU and memory usage in real-time for capacity planning","Identify resource-hungry pods consuming excessive CPU or memory","Track resource trends over time to detect performance degradation","Validate resource requests/limits against actual usage patterns"],"best_for":["DevOps teams optimizing resource allocation and cost","Platform engineers monitoring cluster health and capacity","Developers profiling application resource consumption"],"limitations":["Requires metrics-server deployment — not available in all clusters","Metrics have 15-60 second latency — not suitable for real-time alerting","No historical data retention — only recent metrics available (default 15 minutes)","Metrics are aggregated at pod level — no per-container breakdown"],"requires":["Kubernetes 1.8+ with metrics-server deployed","Kubelet metrics endpoint enabled on all nodes","Network connectivity to metrics API server","Sufficient cluster resources for metrics collection"],"input_types":["pod name and namespace","label selectors for pod filtering","time range for historical data","sampling interval (optional)"],"output_types":["current metrics (CPU, memory in mCores/Mi)","utilization percentages (vs requests/limits)","time-series data (JSON)","aggregated statistics (min/max/avg)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-weibaohui-k8m__cap_5","uri":"capability://automation.workflow.automated.cluster.inspection.with.lua.script.execution.and.ai.powered.summaries","name":"automated cluster inspection with lua script execution and ai-powered summaries","description":"Implements a scheduled inspection system that executes custom Lua scripts against cluster state to identify issues, with results aggregated and summarized using AI models. The system maintains an inspection scheduler that runs checks on configurable intervals, records check events with pass/fail status, and uses AI to generate natural language summaries of cluster health. Supports built-in inspection scripts for common issues (resource limits, pod disruption budgets, security policies) and allows custom script injection.","intents":["Automatically detect cluster configuration issues and best practice violations","Generate AI-powered cluster health summaries without manual analysis","Track inspection history and trends to identify recurring issues","Execute custom diagnostic scripts against cluster state on schedule"],"best_for":["Platform teams automating cluster health monitoring","DevOps engineers reducing manual cluster audits","Organizations requiring compliance and best-practice verification"],"limitations":["Lua script execution is sandboxed — limited access to cluster APIs","AI summaries require external LLM API — adds cost and latency (2-5 seconds per summary)","Inspection scheduling is local to single k8m instance — no distributed scheduling","Custom scripts must be written in Lua — no Python/Go support"],"requires":["Lua 5.1+ runtime embedded in k8m binary","AI model API key (OpenAI, Anthropic, or compatible) for summaries","Kubernetes API access for inspection queries","Database for storing inspection results and history"],"input_types":["Lua script code (inspection logic)","inspection schedule (cron format)","cluster context and namespace filters","AI model configuration"],"output_types":["inspection results (pass/fail with details)","check events (JSON with timestamps)","AI-generated summaries (natural language)","inspection history and trends"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-weibaohui-k8m__cap_6","uri":"capability://tool.use.integration.mcp.server.with.schema.based.tool.registration.and.permission.aware.function.calling","name":"mcp server with schema-based tool registration and permission-aware function calling","description":"Implements a Model Context Protocol (MCP) server that exposes ~50 built-in Kubernetes management tools as callable functions with JSON schema validation and permission enforcement. The system registers tools with input/output schemas, validates function calls against schemas, and enforces user permissions before execution. Tools are organized by category (cluster management, pod operations, resource management) and support both synchronous and asynchronous execution with result streaming.","intents":["Enable AI agents to perform Kubernetes operations through standardized MCP tool interface","Provide schema-validated function calling with automatic input validation","Enforce fine-grained permissions on tool execution based on user roles","Integrate k8m operations into AI chat interfaces and autonomous agents"],"best_for":["AI agent developers building autonomous Kubernetes management systems","Teams integrating k8m with Claude, ChatGPT, or other LLM-based agents","Organizations requiring permission-aware AI-driven infrastructure operations"],"limitations":["MCP protocol overhead adds ~100-200ms per tool call vs direct API","Tool schemas must be manually maintained — schema drift possible if implementation changes","Permission enforcement is callback-based — requires custom permission logic per tool","No built-in tool result caching — repeated calls fetch fresh data"],"requires":["MCP client implementation (Claude SDK, custom client, or compatible tool)","k8m instance with MCP server enabled","User authentication and permission configuration","Network connectivity between MCP client and k8m server"],"input_types":["tool name (string)","tool arguments (JSON matching schema)","user context (for permission checking)"],"output_types":["tool results (JSON)","execution status (success/error)","permission denial messages","result streaming (for long-running operations)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-weibaohui-k8m__cap_7","uri":"capability://automation.workflow.multi.cluster.connection.lifecycle.management.with.heartbeat.monitoring.and.auto.reconnect","name":"multi-cluster connection lifecycle management with heartbeat monitoring and auto-reconnect","description":"Manages connection state machines for multiple Kubernetes clusters with periodic heartbeat monitoring and automatic reconnection on failure. The system maintains connection state (connected/disconnected/degraded), sends heartbeat probes at configurable intervals, detects connection loss, and automatically attempts reconnection with exponential backoff. Supports graceful degradation when clusters become temporarily unavailable and provides connection status visibility in the UI.","intents":["Maintain stable connections to multiple clusters with automatic recovery","Detect cluster API server failures and notify users of connectivity issues","Automatically reconnect to clusters when network is restored","Monitor cluster health through heartbeat signals without polling"],"best_for":["Multi-cluster management platforms requiring high availability","DevOps teams managing geographically distributed clusters","Organizations with unreliable network connectivity between control plane and clusters"],"limitations":["Heartbeat probes add network overhead — default 30-second interval may impact bandwidth","Exponential backoff delays reconnection — up to 5+ minutes for failed clusters","Connection state is local to k8m instance — no distributed state sharing","Degraded state detection is heuristic-based — may miss partial failures"],"requires":["Kubernetes API server with health check endpoint","Network connectivity to cluster API servers","k8m instance with persistent storage for connection state","Configurable heartbeat interval (default 30 seconds)"],"input_types":["cluster kubeconfig","heartbeat interval (seconds)","reconnection backoff parameters"],"output_types":["connection status (connected/disconnected/degraded)","last heartbeat timestamp","connection error messages","reconnection attempt logs"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-weibaohui-k8m__cap_8","uri":"capability://safety.moderation.namespace.scoped.access.control.with.role.based.permission.enforcement","name":"namespace-scoped access control with role-based permission enforcement","description":"Implements fine-grained namespace-level access control using a permission model that combines user roles, namespace assignments, and resource-level permissions. The system enforces permissions at the API layer through callbacks that validate user access before executing operations. Supports role definitions (admin, operator, viewer) with namespace-scoped assignments and integrates with JWT token system for stateless authentication.","intents":["Restrict user access to specific namespaces and resources","Enforce role-based permissions (admin/operator/viewer) across operations","Prevent unauthorized cluster modifications through permission callbacks","Audit user actions through permission enforcement logs"],"best_for":["Multi-tenant Kubernetes clusters requiring namespace isolation","Organizations with compliance requirements for access control","Teams implementing least-privilege access policies"],"limitations":["Permission model is namespace-scoped — cluster-level resources require special handling","Permission callbacks add ~10-20ms latency per operation","Role definitions are static — dynamic role creation requires code changes","No RBAC integration with Kubernetes RBAC — separate permission system to maintain"],"requires":["User authentication (JWT tokens or OIDC)","Database for storing user/role/namespace assignments","Permission callback implementation per resource type","Role definitions configured in k8m"],"input_types":["user identity (JWT claims)","target namespace","resource type and name","operation type (read/write/delete)"],"output_types":["permission decision (allow/deny)","denial reason (for audit)","permission scope (namespaces/resources allowed)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-weibaohui-k8m__cap_9","uri":"capability://automation.workflow.leader.election.for.high.availability.multi.instance.deployment","name":"leader election for high-availability multi-instance deployment","description":"Implements distributed leader election using Kubernetes lease objects to coordinate multiple k8m instances, ensuring only one instance performs cluster-wide operations (inspections, webhooks, leader-dependent tasks). The system uses Kubernetes API for lease-based coordination, handles graceful leadership transfer on instance failure, and provides leader status visibility. Supports active-passive and active-active deployment modes with automatic failover.","intents":["Deploy multiple k8m instances for high availability without duplicate operations","Ensure cluster-wide operations (inspections, webhooks) run only once","Automatically failover to standby instance on leader failure","Coordinate distributed k8m deployments without external consensus systems"],"best_for":["Production deployments requiring high availability","Organizations running multiple k8m instances for redundancy","Teams needing automatic failover without manual intervention"],"limitations":["Leader election adds Kubernetes API calls — ~1 call per 15 seconds per instance","Leadership transition takes 10-15 seconds — brief operation duplication possible","Lease-based election requires Kubernetes API access — not suitable for external coordination","No cross-cluster leader election — each cluster has independent leader"],"requires":["Kubernetes 1.14+ with lease API support","Multiple k8m instances with shared cluster access","Kubernetes API permissions for lease create/update/get","Network connectivity between k8m instances and Kubernetes API"],"input_types":["lease name and namespace","leader election duration (seconds)","instance identity (pod name or hostname)"],"output_types":["leader status (is_leader: true/false)","current leader identity","lease renewal status","election event logs"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["Kubernetes 1.16+ clusters with API server access","Valid kubeconfig files or service account credentials for each cluster","Network connectivity from k8m instance to all cluster API servers","Go 1.18+ for building from source","Running pod with shell access (sh or bash)","Kubernetes API server with exec subresource enabled","WebSocket support in client browser or terminal","Network connectivity to Kubernetes API server","Go 1.18+ for plugin compilation","k8m source code for plugin development"],"failure_modes":["Resource enrichment and aggregation adds latency proportional to cluster count","Watch mechanisms require persistent WebSocket connections per cluster","Batch operations are not transactional across clusters — partial failures possible","CRD support depends on proper schema registration in each cluster","Requires pod to have a shell binary (sh, bash) — fails on distroless images","WebSocket connections are stateless — session loss on network interruption requires reconnection","Terminal emulation is basic — complex TUI applications may not render correctly","No session recording or audit logging of shell commands by default","Go plugins require compilation against exact k8m version — binary compatibility issues common","Plugin loading is synchronous — slow plugins block k8m startup","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.5,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.689Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=weibaohui-k8m","compare_url":"https://unfragile.ai/compare?artifact=weibaohui-k8m"}},"signature":"RwMiouQZSdg9uPzocV8OXtuTiiIv1XihgrcBeKchr4kTartq8APlhO65i5Kv9J1T7kwoKT7RxGn2yB/996SkCg==","signedAt":"2026-06-21T05:29:36.652Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/weibaohui-k8m","artifact":"https://unfragile.ai/weibaohui-k8m","verify":"https://unfragile.ai/api/v1/verify?slug=weibaohui-k8m","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}