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SkillsMP has collected 27 skills from lichtblick-suite/lichtblick. Open a skill to review its source and details.
Showing 27 of 27 collected skills.
Release pipeline knowledge covering the stable release flow (release.yml -> post-release.yml -> release-sync.yml), the manual pre-release/RC flow (prerelease.yml), branch-naming to version-bump mapping, and NPM/GHCR publishing. Use when cutting a release,…
Deep implementation details of HTTP-layer caching for remote file access: CachedFilelike, VirtualLRUBuffer, connection management algorithm, BrowserHttpReader, FetchReader streaming, and RequestQueue concurrency control.
Deep implementation details of the IterablePlayer state machine, tick loop, and data source iteration patterns.
Deep THREE.js rendering knowledge for the 3D panel: WebGL pipeline, buffer management, instanced rendering, shader considerations, and scene optimization techniques.
Deep implementation details of caching strategies, memory budgets, block eviction, and buffered reading in the Lichtblick preloading subsystem.
Deep deserialization knowledge: parseChannel schema dispatch, ros1msg/ros2msg/ros2idl/jsonschema/protobuf/flatbuffer decoders, WASM decompression handlers, and the DeserializingIterableSource wrapping pattern.
Deep Electron implementation knowledge: main/renderer process communication, contextBridge patterns, BrowserWindow lifecycle, native menu integration, and security considerations.
Deep extension system implementation knowledge: IExtensionLoader interface contracts, IndexedDB storage schema, version-compare cache strategy, contribution point registration, extension sandbox, and the .foxe packaging format.
Deep layout system implementation knowledge: ILayoutStorage contracts, IndexedDB schema, sync operation computation, mutex-locked LayoutManager, conflict resolution, WriteThroughLayoutCache, NamespacedLayoutStorage, and CurrentLayoutProvider reducers.
MCAP file format specification knowledge: binary structure, record types, indexing strategies, compression options, and best practices for creating MCAP files that optimize Lichtblick reading performance.
Deep knowledge about the message-path package: path syntax, parsing grammar, data extraction from nested messages, and React hook integration. Shared across Plot, RawMessages, StateTransitions, and general panel agents.
Deep MessagePipeline implementation knowledge: zustand store shape, React context provider, subscription merging/memoization, render-state flow, and the Player→panel data bus.
Deep panel infrastructure knowledge: PanelExtensionAdapter, RenderState incremental building, PanelExtensionContext API, pauseFrame backpressure, panel lifecycle, and the PanelAPI internal hooks.
Deep Image panel knowledge: the panel is ThreeDeeRender's ImageMode SceneExtension, WorkerImageDecoder pipeline, camera-model projection (pinhole/fisheye), and annotation overlays.
Deep Log panel knowledge: react-window VariableSizeList virtualization, dynamic measured row heights, autoscroll/tail behavior, multi-format log normalization, and level/text filtering.
Deep Map panel knowledge: Leaflet lifecycle, NavSatFix/LocationFix/GeoJSON handling, tile layers, and the FilteredPointLayer pixel-deduplication grid that bounds rendering cost.
Deep RawMessages panel knowledge: legacy (react-json-tree) vs virtual (@tanstack/react-virtual) implementations, flattenTreeData, TreeNode shape, expansion-state management, shared logic hook, and diff mode.
Deep StateTransitions panel knowledge: TimeBasedChart segments, message-path extraction, preloaded-range subscription with 250ms batch flush, block+currentFrame merge, and messagesToDataset.
Deep UserScripts panel knowledge: Monaco editor integration, UserScriptPlayer wrapper, TypeScript compilation in the transformer Worker, sandboxed runtime Worker execution, diagnostics, and the script API.
Deep performance optimization knowledge for the Lichtblick codebase. Covers profiling techniques, common bottlenecks, memory management patterns, and optimization strategies specific to real-time data visualization.
Deep Chart.js integration knowledge for the Plot panel: Worker-based rendering, dataset management, downsampling strategies, scale handling, and interaction patterns.
Deep theme system knowledge: createMuiTheme factory, dark/light palette tokens, typography scale, ThemeProvider color-scheme application, and tss-react/mui styling conventions.
Unit testing patterns, mock builder usage, and test data construction strategies for the Lichtblick monorepo.
Web Worker patterns used throughout the Lichtblick codebase: Comlink integration, ComlinkWrap lifecycle, transfer handlers, OffscreenCanvas, SharedWorker isolation, and testing utilities.
Domain knowledge for Playwright MCP-assisted E2E test development in Lichtblick. Covers test architecture, fixture reference, selector strategy, page objects, MCP usage, and source instrumentation patterns.
Deep WebSocket connection knowledge: FoxgloveWebSocketPlayer state machine, WorkerSocketAdapter postMessage protocol, the Foxglove WebSocket protocol handshake, RAF-based state emission, and reconnection.
Shared test conventions for all testing agents in the Lichtblick repo. Defines GWT pattern, core quality rules, and the standard test-writing workflow.