drawio-skill is a comprehensive toolkit for generating, manipulating, validating, and publishing draw.io architecture diagrams from natural language, codebases, infrastructure-as-code, API specifications, and other structured sources. It provides a unified CLI (`diagramctl.py`) and an optional MCP server (`diagramctl_mcp.py`) that expose workflows for building, syncing, viewing, testing, reviewing, querying, what-if analysis, story generation, and publishing diagrams. The system uses a formal Diagram Intermediate Representation (IR) to separate semantic meaning from geometry, enabling incremental reconciliation, multi-view projection, architecture contract enforcement, and accessible HTML story output. It includes 15+ importers for languages (Python, JS, Go, Rust), IaC (Terraform, Kubernetes, Docker Compose), schemas (SQL, OpenAPI, AsyncAPI, Protobuf, GraphQL), and live infrastructure (Docker, Terraform state). The project is packaged as an AI agent skill compatible with Claude Code, Cursor, Copilot, and other Agent Skills hosts, and ships with GitHub Actions for CI integration. While primarily an application, it also bundles curated icon indices and style presets that could be considered data assets.
HyperDX / ClickStack is a full-stack observability platform for ingesting, storing, querying, and visualizing logs, traces, metrics, and session replays. The system is architected as a monorepo with a Next.js frontend (React, Mantine, CodeMirror, Recharts), an Express.js API backend (MongoDB metadata, ClickHouse primary data store), a CLI tool (ink-based TUI), and an OpenTelemetry collector for ingestion. Key capabilities include real-time dashboards with drag-and-drop tile layout, multi-channel alerting (webhook, Slack, incident.io), advanced search with Lucene and SQL, log pattern mining (Drain algorithm), session replay, service maps, Kubernetes monitoring, and a Model Context Protocol (MCP) server that exposes observability tools to AI assistants. The platform supports authentication via local accounts or OIDC, OpAMP agent configuration, and includes an evaluation framework (hdx-eval) for benchmarking agent performance on SRE scenarios. Although the repo is primarily an application, it contains data-intensive components like the log miner and analytics query engines that cross over into data engineering territory.
Robusta KRR is a CLI tool for optimizing Kubernetes resource allocation by recommending CPU and memory requests/limits based on Prometheus metrics. It features a modular architecture with pluggable strategies for recommendation algorithms, multiple output formats, and integrations with various Prometheus-compatible systems. The project includes an enforcer component (mutating webhook) for automatic application of recommendations. The codebase demonstrates good engineering practices with clear separation of concerns, proper error handling, and extensible design patterns.
JayeX (jx) is a modular command-line interface (CLI) tool for managing JayeX 3.x, a CI/CD platform for Kubernetes. The application is built with Go and uses the Cobra library for command parsing. It provides commands for managing namespaces, viewing dashboards, upgrading plugins, checking versions, and interacting with various JayeX components. The architecture follows a modular plugin system where core functionality is extended through installable plugins. The tool interacts with Kubernetes clusters via client-go and maintains version information through build-time injection. Entry points are in cmd/main.go which delegates to platform-specific app implementations.
Emissary-ingress (formerly Ambassador) is a Kubernetes-native API gateway and ingress controller built on Envoy Proxy. It sits at the edge of a Kubernetes cluster, managing ingress traffic with self-service configuration via Custom Resource Definitions (CRDs) like Mapping, Host, Module, AuthService, RateLimitService, and TLSContext. The system is architected as a dual-language control plane: a Go-based main entrypoint (`cmd/entrypoint/`) handles Kubernetes resource watching, snapshot assembly, secret reconciliation, and Envoy lifecycle management, while a Python-based configuration engine (`python/ambassador/`) compiles the desired state into an Intermediate Representation (IR) and generates Envoy xDS configuration (v3). A separate Go API extension server (`cmd/apiext/`) provides conversion webhooks and certificate management for CRD versions (v1, v2, v3alpha1). The compiled Envoy configuration is served via gRPC and pushed to Envoy through the ambex package using go-control-plane. Key data flows: Kubernetes resources (CRDs, Ingresses, Services) are watched by Go watchers and accumulated into snapshots via the `pkg/kates/accumulator`. Snapshots are passed to the Python diagd process, which builds an IR (Intermediate Representation) and generates Envoy v3 configuration for listeners, clusters, routes, filters (auth, rate limiting, gzip, CORS, tracing), and TLS settings. The resulting config is written as JSON files, read by the ambex ADS server, and pushed to Envoy. The system supports dynamic updates without restart, canary deployments, circuit breaking, health checks, Istio and Consul integration, and extensive observability (Prometheus, StatsD, Datadog). The codebase demonstrates strong engineering practices with a comprehensive test suite including Go unit tests, Python unit tests, Kubernetes integration tests (KAT framework), and e2e tests using chainsaw and k3d. It has a mature build system with multi-architecture Docker images, Helm charts, and automated release pipelines. While the dual-language architecture introduces some complexity and duplication (e.g., service name normalization in both Python and Go), the project is actively maintained with regular releases and a large community (4.5k stars, 700+ forks).
Zarf is an air-gap native package manager for Kubernetes. It provides a CLI tool to build, sign, publish, and deploy immutable software bundles (packages) into disconnected Kubernetes clusters. The architecture consists of a Go CLI that orchestrates package lifecycle (create, deploy, remove, publish, inspect), a mutating admission webhook agent that rewrites container image references and Git URLs to point to internal Zarf services, and a set of embedded tools (Helm, K9s, Syft, Cosign, etc.). Key capabilities include OCI-based package distribution, Helm chart and manifest deployment, Git repository mirroring, SBOM generation, and Cosign-based signing/verification with both key and keyless modes. The codebase is well-structured with extensive unit and e2e tests, a clean separation between API versions (v1alpha1/v1beta1) with bidirectional conversion, and several reusable subsystems for value merging, archive extraction, template processing, and Kubernetes resource waiting. It is actively maintained with frequent releases and a large community (2000+ stars).
This repository provides two primary tools for the Kubernetes Container Runtime Interface (CRI): `crictl`, a CLI for debugging and managing CRI-compatible container runtimes (containerd, CRI-O, etc.), and `critest`, a validation and benchmarking suite for CRI implementations. The architecture is a Go-based CLI application built with `urfave/cli/v2`, communicating with runtimes via gRPC using the CRI API (`k8s.io/cri-api`). Key entry points are `cmd/crictl/main.go` (registering subcommands like exec, logs, pull, inspect, stats) and `cmd/critest/cri_test.go` (running Ginkgo-based conformance and benchmark tests). Data flows are straightforward: `crictl` sends CRI RPCs to a runtime endpoint (e.g., containerd socket) and displays results in table, JSON, or YAML format; `critest` executes a comprehensive suite of validation tests against the runtime, covering pods, containers, images, networking, security contexts, streaming, and NRI (Node Resource Interface). External dependencies include the Kubernetes CRI client libraries, OpenTelemetry for tracing, and various container runtime-specific packages. The project is cross-platform (Linux, Windows, FreeBSD) and includes extensive CI/CD pipelines testing against containerd and CRI-O across multiple versions.
Kapitan is a configuration-as-code CLI tool that renders infrastructure manifests (Kubernetes, Terraform, Docker, etc.) from a hierarchical YAML inventory and pluggable template engines (Jsonnet, Jinja2, Kadet, Helm, Kustomize, CUE). It manages secrets via references (GPG, cloud KMS, Vault, base64), fetches remote dependencies (Git, HTTP, OCI, Helm charts), and supports multiple inventory backends (Reclass, OmegaConf). The compilation pipeline is inventory-driven: targets define classes and parameters, which are deep-merged and interpolated, then passed to input-type compilers that produce deterministic output files. The architecture is modular—separate packages for inputs, inventory backends, secrets/references, and dependency management—all orchestrated by the `kapitan compile` CLI command. It is not a data research project; it is a production-grade infrastructure tool with a large test suite, CI/CD, and documented extension points.
This is a Kubernetes controller that manages Helm chart deployments via Custom Resource Definitions (HelmChart and HelmChartConfig). It watches these CRDs and creates Kubernetes Jobs to run Helm install, upgrade, or delete operations, with a two-phase apply pattern that suspends jobs before updating them to avoid race conditions. The controller is built on the rancher/wrangler framework and handles complex features like failure policies, server-side apply, driver selection (secret vs configmap), values from secrets, HelmChartConfig merging, and bootstrapping. It is the core Helm management component of k3s and is designed to be deployed as a Deployment with a ServiceAccount and ClusterRole.
Kubenurse is a Kubernetes DaemonSet that monitors network connectivity within a cluster by performing periodic checks on API server access (direct and DNS), service/ingress reachability, and inter-node communication. It exports detailed Prometheus metrics including request duration histograms, error counters, and a neighbourhood incoming checks gauge. The architecture uses a controller-runtime client for Kubernetes API discovery, implements an efficient O(N) neighbourhood filtering algorithm based on SHA256 hashing to avoid quadratic scaling, and leverages Go's httptrace package for granular connection metrics. The application features graceful shutdown, TLS support, readiness probes that respect node schedulability, and extensive configuration via environment variables or Helm values.
LUML is an agent-native MLOps platform that orchestrates the full machine learning lifecycle: experiment tracking, model packaging, registry, deployment, monitoring, and AI-mediated code generation. The monorepo contains multiple services: a FastAPI backend (multi-tenant platform API with PostgreSQL), a Vue 3 frontend (SPA with Pinia, PrimeVue), a satellite runtime for edge deployment and monitoring (Docker/Kubernetes), an SDK for experiment tracking and model packaging (supporting sklearn, XGBoost, LangGraph, LLM evaluation), a Prisma service for agent-driven code generation (Claude/Codex orchestrator with git worktree isolation), a local experiment tracker (lumlflow) with both a Textual TUI and web interface, and WASM-based in-browser ML workers (prompt optimization, tabular forecasting). The architecture is microservice-oriented with a shared SDK and wire contracts. The platform is production-grade with comprehensive testing (unit, integration, E2E), CI/CD pipelines, OpenAPI contracts, and a well-defined RBAC permission matrix. It demonstrates high craftsmanship in areas like monitoring drift computation (PSI, multivariate), WebWorker RPC proxies, and LLM judge scoring, though the sheer size and number of packages introduce some coordination complexity.
Nacos is a production-grade dynamic service discovery, configuration management, and service management platform, originally built by Alibaba. The codebase is a large Java/Spring Boot monolith with a React frontend, organized into modules for core infrastructure (cluster, gRPC, Raft consensus, Distro sync), configuration management (CRUD, long-polling, gray rules, history), naming/service discovery (instance registration, health checks, push v2), and an extensive plugin system (auth, datasource, encryption, control, visibility). In recent versions, Nacos has expanded into AI resource management, adding modules for MCP server registry, agent specs, skills, prompts, A2A agent discovery, vector search indexing, and pipeline execution. The repository includes two frontend codebases (legacy console-ui and modern console-ui-next with Radix UI/Tailwind), a Java client SDK, and comprehensive unit/integration tests. The architecture is modular with SPI-based plugin loading, event-driven internal communication, and support for multiple databases (Derby, MySQL, PostgreSQL, Oracle). Despite its size and rapid AI feature growth, the codebase maintains solid engineering practices with clear separation of concerns, extensive test coverage, and a well-defined plugin contract.
HashiCorp Consul is a distributed service mesh platform providing service discovery, health checking, dynamic configuration, and multi-datacenter federation. The codebase implements a full-stack system: a Go-based agent that runs as a server or client, a CLI tool for administration, a web UI built with Ember.js, and extensive gRPC/protobuf-based APIs for internal and external communication. The agent uses Raft consensus for state replication, maintains an in-memory database (memdb) for service and configuration state, and generates Envoy xDS configuration for service mesh gateways. Key entry points include the agent server (agent/consul/), CLI commands (command/), and the UI router (ui/packages/consul-ui/app/router.js). External dependencies include protobuf, gRPC, Envoy, and various cloud provider SDKs (AWS, Azure, GCP). The codebase is actively maintained with a large test suite covering unit, integration, and acceptance tests.
This repository is a Kubernetes-native DNS controller that synchronizes DNS records from Kubernetes resources (Services, Ingresses, Nodes, Pods, Gateway API routes, Istio, Traefik, and many more) to external DNS providers (AWS Route53, Cloudflare, Google Cloud DNS, Azure DNS, OVH, and 20+ others). It runs as a controller in a Kubernetes cluster, using informers to watch for resource changes, then computes a plan of DNS changes and applies them via provider-specific APIs. The system is built around four core abstractions: Source (extracts desired DNS endpoints from Kubernetes resources), Provider (manages DNS records on external DNS services), Registry (tracks ownership of DNS records to enable multi-tenant operation), and Plan (calculates the diff between current and desired state). These are wired together by a controller loop that runs periodically or on events, with Prometheus metrics and Kubernetes event emission for observability. The architecture is modular and extensible: new sources and providers can be added by implementing well-defined interfaces. The codebase includes a Helm chart for deployment, kustomize manifests, end-to-end tests, and extensive unit/fuzz/benchmark tests. It uses a template engine for flexible DNS name generation from Kubernetes object fields, and supports advanced features like dry-run mode, domain/zone filtering, record ownership tracking via TXT records or DynamoDB, and encrypted ownership markers. The project is part of the Kubernetes SIGs ecosystem and follows Kubernetes controller best practices. Key entry points include main.go (binary entry), controller/execute.go (controller initialization and loop), source/store.go (source factory), provider/factory/provider.go (provider factory), and registry/factory/registry.go (registry factory). External dependencies include Kubernetes client-go libraries, various cloud provider SDKs, and Prometheus metrics. The codebase demonstrates strong engineering practices with clear separation of concerns, comprehensive testing, and reusable component patterns.
PGO (Crunchy Data Postgres Operator) is a production-grade Kubernetes operator that manages the full lifecycle of PostgreSQL clusters, including deployment, high availability via Patroni, backup/restore via pgBackRest, connection pooling via PgBouncer, monitoring via OpenTelemetry and pgMonitor, and a web UI via pgAdmin. The operator uses custom resource definitions (CRDs) for PostgresCluster, PGAdmin, and PGUpgrade, and reconciles desired state using controller-runtime with server-side apply. It manages StatefulSets, PVCs, Services, ConfigMaps, Secrets, Jobs, and RBAC across multiple namespaces. Architecturally, the operator is a single binary with multiple controllers (postgrescluster, standalone_pgadmin, pgupgrade) and sidecar containers for pgBackRest, PgBouncer, pgAdmin, and the OpenTelemetry collector. It uses feature gates to enable optional capabilities like volume snapshots, auto-grow volumes, and OpenTelemetry logging/metrics. The codebase is well-structured with distinct packages for naming conventions, PKI certificate management, PostgreSQL parameter management, and shell command construction. Extensive unit and e2e tests (using kuttl and chainsaw) cover most scenarios, including backup/restore, scaling, upgrades, and security contexts. Key external dependencies include controller-runtime, client-go, Patroni, pgBackRest, PgBouncer, pgAdmin, OpenTelemetry SDK, and pg_query_go for SQL parsing. The operator is deployed via Kustomize and supports multiple PostgreSQL versions (11-18) with PostGIS extensions.
This repository is a personal Nix configuration (dotfiles) for managing a heterogeneous fleet of machines: NixOS desktops and servers, macOS laptops (via nix-darwin), and Kubernetes clusters (k3s, KubeVirt). It is structured as a Nix flake with modular components for system configuration, home-manager profiles, package overlays, secrets management (agenix), and infrastructure monitoring (Grafana, Prometheus, VictoriaMetrics, Loki). The codebase demonstrates advanced Nix patterns: parameterized module generators for K3s and KubeVirt, automatic file scanning for module imports, declarative disk partitioning (disko), ephemeral root filesystems with persistent state (preservation), and sandboxed GUI applications (nixpak, bwrap). It also includes a custom desktop shell (noctalia) and extensive input method customization (Rime/Fcitx5). While highly personalized and not intended for direct reuse, the architectural patterns and modular design serve as a reference for building complex Nix-based infrastructure. The repository is actively maintained with eval tests and CI, but lacks integration tests for runtime services.
Optio is a self-hosted, Kubernetes-native orchestration platform for AI coding agents (Claude Code, OpenAI Codex, GitHub Copilot, Gemini, Cursor, OpenCode). In production it runs three task tiers — repo tasks (issue/request → agent pod → PR), standalone jobs (parameterized agent runs without repo checkout), and persistent agents (long-lived, message-driven agents with turn-based execution) — plus an autonomous PR review loop. The core loop is driven by ~15 BullMQ workers (task, PR watcher, review, webhook, ticket sync, reconcile, workflow trigger) against a Postgres database, with Redis pub/sub powering a real-time event bus and WebSocket log/terminal/chat streaming to the Next.js dashboard and CLI. Agents execute in ephemeral pods built from per-language Docker images; a K8s-style reconciliation control plane (world snapshot + pure decision functions + CAS-gated executor) converges four run kinds and catches drift. The architecture is a pnpm/turbo monorepo: a Fastify 5 API with Drizzle ORM and Zod-validated OpenAPI schemas; a Next.js 15 admin dashboard (tasks, jobs, reviews, sessions, agents, cluster, analytics); a Commander-based CLI (login, task/repo/session/secret commands over REST + WebSocket); and a static docs site. Shared packages provide a clean AgentAdapter interface (`validateSecrets` → `buildContainerConfig` → `parseResult`), a Docker/Kubernetes container runtime abstraction, ticket provider adapters (GitHub, GitLab, Jira, Linear, Notion), and shared types/utilities (task state machines, repo URL parsing, SSRF guards, cost arithmetic). Workspace-scoped multi-tenancy with admin/member/viewer RBAC, AES-256-GCM secret encryption with AAD binding, WebSocket upgrade tokens, HMAC-signed internal calls, and a security-focused audit trail (optio_actions) are enforced throughout. Engineering strengths: the test suite is unusually thorough for a project this size — nearly every route, service, worker, and shared utility has a unit test, with per-file Postgres/Redis isolation for integration tests, real-server E2E tests using a deterministic fake container runtime (with mock directives like [[mock:pr]]), and Playwright smoke tests. The reconcile pattern, generic state machine utilities, and pure decision functions in `packages/shared/src/reconcile/` are the highest-craftsmanship code. Known debt: five near-duplicate NDJSON event parsers (one per agent, with drift risk), hardcoded model pricing tables in the Codex/Gemini adapters, scattered duplicated workspace-scoping checks, a 60+ file migration history with historical prefix collisions (now script-enforced), and raw SQL in the analytics service. The project moves fast (v0.1 → v0.4.1 within months, last commit 2026-08) and some surface area (OpenCode/OpenClaw adapters, skills marketplace) is explicitly experimental, but the core architecture is coherent and production-oriented.
The repository manages the infrastructure and applications for the Kubernetes project (k8s.io). It includes multiple community applications (such as codesearch, gcsweb, and elekto) deployed via Kubernetes manifests, infrastructure-as-code using Terraform for GCP and AWS, automation scripts for provisioning and validation, a specialized log enrichment pipeline (public-log-asn-matcher) for processing traffic logs, and audit tooling for GCP resources. The architecture is a collection of loosely coupled tools and applications, with an emphasis on automation and reproducibility.
decK is a CLI tool for declarative configuration management of Kong API Gateway and Konnect SaaS. It enables users to manage Kong entities (services, routes, plugins, consumers, certificates, etc.) via YAML/JSON state files, supporting sync, diff, dump, apply, reset, and validate operations. The architecture is a single binary with multiple subcommands organized under `gateway`, `file`, `ai`, and `konnect` namespaces, built on top of Kong's go-database-reconciler library for state reconciliation. Key entry points include `main.go` (signal handling), `cmd/root.go` (Cobra CLI), and command handlers in `cmd/gateway_*.go`, `cmd/file_*.go`, and `cmd/ai_*.go`. Data flows involve reading declarative state files, fetching current state from Kong Admin API or Konnect, computing diffs, and applying CRUD operations. External dependencies include Kong Gateway (>=1.x), Kong Enterprise, Konnect, and various Go libraries for Kubernetes, Terraform, and AI Gateway integrations. The repository also includes converters for generating Kubernetes Ingress Controller manifests (`kong2kic`), Terraform resources (`kong2tf`), and version migration rulesets (`convert/`).
This repository implements a Kubernetes operator for managing egress traffic in Kubernetes clusters. It provides stable egress IP addresses for pods through a control plane/data plane architecture consisting of a controller (managing cluster state via custom resources) and an agent (applying network configuration on each node). The system uses VXLAN tunneling to encapsulate traffic between nodes and employs iptables/IP sets for policy enforcement. It includes features like IP allocation, failover mechanisms, dual-stack support, and comprehensive observability via Prometheus metrics.