The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Kubeshark
EBPF-powered network observability for Kubernetes. Indexes L4/L7 traffic with full K8s context, decrypts TLS without keys. Queryable by AI agents via MCP and humans via dashboard.
Install
claude mcp add kubeshark -- kubeshark mcpInstall command taken from the project's README or registry entry. Always review code before running it.
README excerpt
Read full README on GitHub →Network Observability for SREs & AI Agents
Live Demo · Docs
Kubeshark indexes cluster-wide network traffic at the kernel level using eBPF — delivering instant answers to any query using network, API, and Kubernetes semantics.
What you can do:
- Download Retrospective PCAPs — cluster-wide packet captures filtered by nodes, time, workloads, and IPs. Store PCAPs for long-term retention and later investigation.
- Visualize Network Data — explore traffic matching queries with API, Kubernetes, or network semantics through a real-time dashboard.
- See Encrypted Traffic in Plain Text — automatically decrypt TLS/mTLS traffic using eBPF, with no key management or sidecars required.
- Integrate with AI — connect your favorite AI assistant (e.g. Claude, Copilot) to include network data in AI-driven workflows like incident response and root cause analysis.
Get Started
helm repo add kubeshark https://helm.kubeshark.com
helm install kubeshark kubeshark/kubeshark
kubectl port-forward svc/kubeshark-front 8899:80
Open http://localhost:8899 in your browser. You're capturing traffic.
For production use, we recommend using an ingress controller instead of port-forward.
Connect an AI agent via MCP:
brew install kubeshark
claude mcp add kubeshark -- kubeshark mcp
Network Data for AI Agents
Kubeshark exposes cluster-wide network data via MCP — enabling AI agents to query traffic, investigate API calls, and perform root cause analysis through natural language.
"Why did checkout fail at 2:15 PM?" "Which services have error rates above 1%?" "Show TCP retransmission rates across all node-to-node paths" "Trace request abc123 through all services"
Works with Claude Code, Cursor, and any MCP-compatible AI.
AI Skills
Open-source, reusable skills that teach AI agents domain-specific workflows on top of Kubeshark's MCP tools:
| Skill | Description |
|---|---|
| Network RCA | Retrospective root cause analysis — snapshots, dissection, PCAP extraction, trend comparison |
| KFL | KFL (Kubeshark Filter Language) expert — writes, debugs, and optimizes traffic filters |
Install as a Claude Code plugin:
/plugin marketplace add kubeshark/kubeshark
/plugin install kubeshark
Or clone and use directly — skills trigger automatically based on conversation context.
Query with API, Kubernetes, and Network Semantics
Kubeshark indexes cluster-wide network traffic by parsing it according to protocol specifications, with support for HTTP, gRPC, Redis, Kafka, DNS, and more. A single KFL query can combine all three semantic layers — Kubernetes identity, API context, and network attributes — to pinpoint exactly the traffic you need. No code instrumentation required.
KFL reference → · Traffic indexing →
Workload Dependency Map
A visual map of how workloads communicate, showing dependencies, traffic volume, and protocol usage across the cluster.
Similar plugs
Reverse engineer anything with agents, from app behavior down to native binaries.
Build Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace. Deploy on cloud, VPC, or self-hosted, so teams move from prototype…
User-friendly AI Interface (Supports Ollama, OpenAI API, ...)
An open-source AI agent that brings the power of Gemini directly into your terminal.