Gitframes

Compositing, motion graphics and 3D for code-first video — one npm package that AI agents drive with code. ⚠️ Beta: gitframes is under active development. APIs may change between releases and some features may be incomplete or unstable. ⚠️ Beta: gitframes is under active development. APIs may change between releases and some features may be incomplete or unstable. Gitframes is built for coding agents. It packs the work people usually split across three desktop apps (Photoshop-inspired compositing and VFX, After Effects-style motion, typography and keyframing, and Blender-style 3D scenes, cameras and models) into one lightweight npm package. Your agent writes a TypeScript composition, checks frames, and renders an MP4, and nobody has to install or license a multi-gigabyte creative suite. Code-first video as pure software engineering — no headless browser, no DOM reflow, no screenshot pipeline. Renders directly on GPU hardware via Dawn / WebGPU / Metal / Vulkan in Node.js and modern WebGPU browsers. Made with gitframes Every frame of these films is rendered by gitframes from TypeScript in examples/. Click a still to watch it on YouTube. Note Using an AI coding agent? Install the gitframes skills in one line. Claude Code /plugin install gitframes Any other agent (Codex, Cursor, Hermes, Gemini CLI, Copilot, and more) npx skills add gatewai-dev/gitframes See Agent Skills & Plugins for details. Table of Contents Why Gitframes Architectural Comparison Key Features & Capabilities 1. Slug GPU Vector Typography & After Effects Animators 2. Photoshop-Inspired WebGPU 2D VFX 3. Unified 3D Scene Graph, Camera & Mesh Shading 4. Audio Layers, Procedural SFX & Reactive Signals 5. Animated Charts 6. On-Device Vision & Tracking 7. Headless Conformance & FrameGrid Testing 8. Live Preview in the Browser 1. Slug GPU Vector Typography & After Effects Animators 2. Photoshop-Inspired WebGPU 2D VFX 3. Unified 3D Scene Graph, Camera & Mesh Shading 4. Audio Layers, Procedural SFX & Reactive Signals 5. Animated Charts 6. On-Device Vision & Tracking 7. Headless Conformance & FrameGrid Testing 8. Live Preview in the Browser Monorepo Architecture Quickstart Guide 1. Basic Composition & Kinetic Auto-Layout 2. Unified 3D Scene with Camera & 3D Model 3. Audio Soundtrack, Procedural SFX & Reactive Signals 4. Chained WebGPU Post-Processing VFX 5. Vision: Pin, Matte & Reframe 6. Headless Video & FrameGrid Rendering 1. Basic Composition & Kinetic Auto-Layout 2. Unified 3D Scene with Camera & 3D Model 3. Audio Soundtrack, Procedural SFX & Reactive Signals 4. Chained WebGPU Post-Processing VFX 5. Vision: Pin, Matte & Reframe 6. Headless Video & FrameGrid Rendering Engineering Doctrines & Best Practices Agent Skills & Plugins Reference Showcase Examples Development & Building Community License Why Gitframes Modern automated video generation is usually constrained by the architectures of general-purpose web browsers: process overhead, non-deterministic DOM layout reflows, and slow screenshot capture. Gitframes treats video composition as software engineering: Architectural Comparison: Gitframes vs. Remotion vs. Hyperframes Developers generating video programmatically commonly weigh Remotion (React/Chromium) or Hyperframes (Canvas2D/SVG web animation). The matrix below compares the fundamental engineering dimensions. Detailed Comparison Matrix Key Features & Capabilities 1. Slug GPU Vector Typography & After Effects Animators Traditional text relies on CPU rasterization or low-res SDF atlases that soften under 3D camera sweeps. Gitframes integrates the Slug algorithm (SlugPipeline): Analytic GPU evaluation — WGSL fragment shaders solve exact cubic/quadratic Béziers per-pixel. Glyphs stay sharp at 10 px or 10,000 px with zero CPU re-rasterization. After Effects–style animators — range selectors (square, ramp_up, ramp_down, triangle, smooth), easeHigh/easeLow curves, and seeded PRNG character shuffling (TextAnimator). Human typing cadence — weighted punctuation delays (commas 3×, sentence ends 5.5×, newlines 7×) and trailing scramble resolution (TypewriterAnimator). 3D volumetric formations — map text onto cylindrical drums, logarithmic vortex spirals, and double-helix ribbons with surface-normal banking (evaluateVolumetricFormation). Dynamic leading & skew — area-preserving unimodular shear and accordion line-leading anchored to baseline, center, or top. 2. Photoshop-Inspired WebGPU 2D VFX (50+ Shaders) A comprehensive suite of professional image/video shader nodes in nodes/ and packages/webgpu-renderers: Tonal grading — Curves (RGB/R/G/B spline), Levels (black/white point, gamma, output), Shadows/Highlights, Selective Color (CMYK gamut isolation), 3D Cube LUT (ApplyLUT). Stylization & grain — Film Grain (Gaussian emulsion with spatial seed variation), Halftone (mono/RGB/CMYK, adjustable dot shape & angle), Gradient Map, High Pass. Optics & lens — Bilateral Gaussian Blur, Unsharp Mask, Vignette, Refraction Caustics, PBR Glassmorphism with chromatic dispersion (PBRGlass). Distortion & warping — Displacement Maps, Liquify, Mesh Warp, Corner Pin homography. 3. Unified 3D Scene Graph, Camera & Mesh Shading Calibrated camera rig — LookAt and Turntable cameras (Camera3D) calibrated so z = 0 matches 2D canvas pixel coordinates 1:1. 3D layout primitives — Layer3D.cube, carousel, prism, plane, grid with unified depth-buffer testing. Zero-dependency model parsers — OBJ, FBX, glTF/GLB, STL, PLY, VOX, 3DS, OFF. Skeletal animation & shading — 128-bone Linear Blend Skinning, Blinn-Phong & PBR multi-light shading, PCSS/Poisson contact shadows, SSAO, and optical DoF. Physical motion blur — 180° shutter motion blur with per-vertex velocity vectors packed into rg16float MRT buffers. 4. Audio Layers, Procedural SFX & Reactive Signals Soundtrack layers — .audio media nodes with frame-exact lifecycle control. Procedural SFX — deterministic CPU-synthesized whooshes, impacts, risers, downshifters, and glitches placed on the bar/beat grid (renderSfx, mixSfxInto, softLimit). Multi-track mixing — master tracks headlessly with mixAudioTracks and encodeStereoWav. Reactive signals — drive transforms, scale, borders, or shader uniforms from tempo signals (Signal.builder) or audio analysis. 5. Animated Charts Layer.chart builds line, area, bar (grouped or stacked), scatter, candlestick, pie and donut charts. d3 computes the scales, ticks and geometry; every bar, line, slice and label is an ordinary box, path or text node: Labels use the composition's registered fonts and the same GPU text renderer as the rest of the film. A built-in reveal draws lines on, grows bars from the baseline and staggers points and slices (animate: { start, duration, stagger, ease }, or animate: false). The chart is one box, so it positions, animates, grades and tilts into 3D like any other layer. Layer.chart( { type: "bar", width: 900, height: 480, categories: ["Q1", "Q2", "Q3", "Q4"], series: [ { name: "Revenue", data: [12, 19, 24, 31] }, { name: "Costs", data: [8, 11, 13, 15] }, ], yAxis: { format: "$,.0f" }, animate: { start: 10, duration: 30 }, }, { position: "absolute", x: 120, y: 200 }, ); 6. On-Device Vision & Tracking @gitframes/vision runs ONNX models via onnxruntime-node (CPU) or onnxruntime-web (WebGPU) and wires every result into the same reactive signal surface the rest of Gitframes consumes. Tip Lazy by construction. VisionRunner.create(), comp.withVision(...) and VisionNode.attach(...) perform zero I/O — no downloads, no sessions, no file probes. A model is fetched the first time a task actually runs. To warm up ahead of time, call await runner.preload(["detect", "pose"]) (or await vision.ready() on an attached node). Tasks and models Every model is Apache-2.0, pinned to an immutable Hugging Face revision, and verified by SHA-256 after download. Variants — variant: "t" | "s" | "m" (default "s"; ~24 / 43 / 116 MB for RTMDet-Ins). Tune confidence and a COCO classes filter per composition. On CPU, a 2K frame takes roughly 200–340 ms to detect + segment, ~120 ms for pose and ~20 ms for the matte with "s". One pass, two tasks — detection and segmentation share a single RTMDet-Ins inference per frame. Picking a matte — the Selfie Segmenter is tuned for a person filling much of the frame: it misses distant figures and can report "person" on close-ups with nobody in them. For anything else, cut out with instance masks (matteSource: "instance", the default). Whole-subject cutouts — mask / matte / crop modes merge every comparably sized instance that overlaps the main subject, so a flowing dress or a held instrument stays attached to the person, while a tunnel or window framing them does not. One-frame delay — vision reads each layer's previous rendered frame, so results trail the plate by one frame and frame 0 has none. Verify vision layers with the exported video or consecutive frames, not frame grids. Model cache & mirrors — models are cached atomically (temp + rename) in $GITFRAMES_MODELS_DIR (default ~/.cache/gitframes/models). Point baseUrl or GITFRAMES_MODELS_BASE_URL at your own mirror for air-gapped or CI renders. GPU helpers OpenPose-style skeleton textures — rasterize COCO-17 keypoints into a VRAM conditioning texture (PoseSkeletonRenderer). GPU segmentation texture pool — reusable silhouette textures (SegmentationTexturePool). Temporal tracking & analysis Multi-object tracker (TemporalObjectTracker) assigns stable trackIds via IoU association, with configurable minHits, positionSmoothing, and velocity-based coasting for up to maxMissedFrames (default 15) so a transient miss holds the track instead of flashing. Pose↔track matching (pose-track-matcher) binds keypoints to the right track by id, then by spatial IoU fallback. One-shot sequence analysis — comp.analyzeVisionSequence(src, { tasks, categories }) decodes frames through the mediabunny pipeline, tracks them, and returns a zod-serializable report (per-track frame ranges, mean speed, sampled center paths, per-class presence/confidence, mean mask coverage, model download bytes/timing) (analyzeSequence). Reactive vision signals Every tracked entity is exposed as reactive ProgrammaticSignals that animate layers and shader uniforms: Spatial pinning Project normalized landmarks to screen space with a configurable camera FOV, then bind any node to a track or landmark (SpatialLandmarkTransformer, spatial-pin): pinToObject(track, { anchor, offsetX/Y/Z, matchWidth, matchHeight, smoothFrames, hideWhenLost }) pinToLandmark(coord, { offsetX/Y/Z }) High-level composition helpers Subject Sandwich — comp.addSubjectSandwich({ source, behind, feather, fit }) cuts the foreground subject out and places typography/graphics behind them. Smart Reframing — comp.addSmartFraming({ source, target, targetAspect, damping, leadHeadroom }) auto-crops 16:9 → 9:16 while tracking target. Subject Outline — comp.addSubjectOutline(vision.segmentation.subject, { source, color, width, blur }) strokes the segmented boundary as an audio-reactive contour glow. Tracked Region Blur — layer.blurRegion(track, { strength }) blurs faces, plates, or any detected class. Node modes — passthrough, mask, matte, crop, skeleton, boxes, tracking; pick the cutout alpha with matteSource: "instance" | "selfie", and optionally keyBackground to grow the subject into connected foreground. Agent-first DX Runtime config is zod-validated and available from a zod-only entry (@gitframes/vision/schemas) so the hot path stays zod-free. Unknown or removed options are rejected, not silently ignored. vision.summary(frame) returns a deterministic, serializable snapshot (objects, classes, masks) safe to call inside a frame hook. Clear failures — a model that is the wrong size, fails its checksum, or lacks an expected output raises an error naming the model and its source. Browser entry — @gitframes/vision/web re-exports the engine plus createWebGPUProvider() / hasWebGPU(); onnxruntime-web is an optional lazy peer. 7. Headless Conformance & FrameGrid Testing Pixel-sampling invariant assertions — test compositions in Vitest with skia-canvas to verify shader math, font coverage, and Mean Squared Error (MSE) temporal deltas. FrameGrid contact sheets — comp.renderFrameGrid(...) outputs sequential-frame contact sheets for instant review of easing, kinetic type, and transitions. 8. Live Preview in the Browser Runs your composition, not a video — startPreview({ entry, export }) serves a localhost WebGPU player that loads the composition's own module and renders every frame live in the browser. Nothing is streamed: the server only hands over the bundle, the project's assets, and the soundtrack mixed by the export engine. Timeline, waveform & frame stepping — play/pause, scrub, step frame by frame, and read resolution, FPS, duration, and audio status at a glance. One stable URL per project — the port is derived from the working directory, so re-running the preview replaces the running server and any open tab reloads into the new version by itself. Close the tab and the server shuts down about five seconds later. Shown where you are — startPreview serves the page and returns its URL instead of opening a browser, so an agent can show it in its own pane (Claude Code, Codex); pass open: true to open the system browser. import { startPreview } from "gitframes"; const session = await startPreview( { entry: new URL("./film.ts", import.meta.url), export: "buildFilm" }, { title: "gitframes film" }, ); console.log(`Preview at ${session.url}`); await session.closed; // serves until its tab closes or a newer preview takes over Monorepo Architecture Managed with pnpm workspaces and turbo: gitframes/ ├── packages/ │ ├── gitframes/ # Unified SDK (Composition, Layer, LayerAnimation, Signal, effects) │ ├── core/ # Core AST, Effect base class, VirtualMediaData, vision types │ ├── compositions/ # Layout engine, Flex/Box AST compiler, timeline evaluator │ ├── webgpu-renderers/ # WGSL shaders, Slug text engine, 3D renderer, camera, lights, materials │ ├── tensor-webgpu/ # WebGPU compute pipelines (Canny, depth-to-normals, flow, deflicker, landmarks) │ ├── vision/ # ONNX vision engine: detect, segment, pose, matte, tracking, signals │ ├── renderer/ # Headless Node.js WebGPU renderer via Dawn, WebCodecs, skia-canvas │ ├── renderers/ # Higher-level render orchestration │ ├── media/ # Media decoding / encoding adapters │ ├── node-sdk/ # Node renderer contracts and result schemas │ ├── server-utils/ # Server infrastructure, storage, asset caches │ └── client-utils/ # Shared browser utilities ├── nodes/ # 58+ specialized domain nodes (VFX, audio, layout, node-vision) ├── apps/ │ └── renderer-service/ # Production HTTP / gRPC rendering microservice container ├── examples/ # Reference compositions and films ├── plugins/gitframes/ # Agent plugin: skills only (setup, compose, effects, render) └── scripts/ # Build, release, and plugin validation tooling Quickstart Guide Installation pnpm add gitframes Requirements: Node.js ≥ 22. Gitframes uses native GPU acceleration via Dawn / WebGPU or Vulkan. Requirements: Node.js ≥ 22. Gitframes uses native GPU acceleration via Dawn / WebGPU or Vulkan. 1. Basic Composition & Kinetic Auto-Layout import { Composition, Layer, LayerAnimation } from "gitframes"; // 1. Initialize a 1080p60 composition const comp = new Composition({ width: 1920, height: 1080, fps: 60, durationFrames: 180, // 3 seconds backgroundColor: "#090a0f", fonts: ["assets/fonts/Inter.ttf", "assets/fonts/SpaceGrotesk.ttf"], }); // 2. Define physical snap-overshoot animations const cardEntrance = LayerAnimation.create() .fadeIn(0, 20, "power2.out") .fromTo("y", 60, 0, { start: 0, end: 35, ease: "back.out(1.5)" }) .fromTo("scale", 0.92, 1.0, { start: 0, end: 35, ease: "back.out(1.2)" }); // 3. Assemble a responsive flex-layout card const heroCard = Layer.box({ width: 720, height: 380, background: "#141721", borderRadius: 24, borderColor: "#262b3d", borderWidth: 1.5, padding: 32, children: [ Layer.flex({ dir: "column", gap: 16, children: [ Layer.text("GITFRAMES ENGINE", { fontSize: 16, fontWeight: 700, fill: "#6366f1", letterSpacing: 2.0, }), Layer.text("Next-Gen WebGPU Motion", { fontSize: 48, fontWeight: 700, fill: "#f8fafc", fontFamily: "SpaceGrotesk", }), Layer.text("Direct hardware video composition without headless browser overhead.", { fontSize: 20, fill: "#94a3b8", lineHeight: 28, }), ], }), ], }).animate(cardEntrance); comp.add(heroCard); 2. Unified 3D Scene with Camera & 3D Model import { Composition, Layer, Layer3D, CameraAnimation, Light } from "gitframes"; const comp = new Composition({ width: 1920, height: 1080, fps: 60, durationFrames: 300 }); // 1. LookAt 3D camera with a continuous orbit const cameraAnim = CameraAnimation.camera().orbit({ azimuth: { from: -30, to: 30 }, elevation: { from: 15, to: 15 }, radius: { to: 1200 }, start: 0, end: 300, }); comp.add( Layer.camera({ x: 960, y: 540, z: -1000, targetX: 960, targetY: 540, targetZ: 0 }).animate(cameraAnim) ); // 2. Studio lighting comp.add(Light.ambient("#ffffff", 0.4)); comp.add(Light.directional({ color: "#e0e7ff", intensity: 1.2, x: 500, y: -800, z: -600 })); // 3. 3D model with skeletal animation comp.add( Layer.glb("assets/models/character.glb", { x: 960, y: 640, z: 0, scale: 2.5, material: "lit", loop: true, }) ); // 4. 3D prism layout carousel comp.add( Layer3D.carousel({ radius: 400, items: [ Layer.box({ width: 280, height: 180, background: "#1e293b", borderRadius: 16 }), Layer.box({ width: 280, height: 180, background: "#334155", borderRadius: 16 }), Layer.box({ width: 280, height: 180, background: "#0f172a", borderRadius: 16 }), ], }) ); 3. Audio Soundtrack, Procedural SFX & Reactive Signals import { Composition, Layer, LayerAnimation, Signal, renderSfx, mixSfxInto, softLimit } from "gitframes"; const comp = new Composition({ width: 1920, height: 1080, fps: 60 }); const totalFrames = 240; // 1. Soundtrack layer comp.addAudio(Layer.audio("assets/score.mp3", { volume: 0.9, durationFrames: totalFrames })); // 2. Frame-accurate procedural SFX on the beat grid const bed: [Float32Array, Float32Array] = [ new Float32Array(Math.ceil((totalFrames / 60) * 48000)), new Float32Array(Math.ceil((totalFrames / 60) * 48000)), ]; mixSfxInto(bed, [ renderSfx({ type: "whoosh", atBar: 0.79, volume: 0.5 }, { sampleRate: 48000, secondsPerBar: 2.0, seed: 1 }), renderSfx({ type: "impact", atBar: 1.0, volume: 0.8 }, { sampleRate: 48000, secondsPerBar: 2.0, seed: 2 }), ]); softLimit(bed); // 3. Tempo signal (120 BPM = 2 Hz) const beatPulse = Signal.builder({ type: "sawtooth", frequency: 2, amplitude: 0.08, offset: 1.0 }); // 4. Bind it to visuals const reactiveCard = Layer.box({ width: 400, height: 250, background: "#1c202e", borderRadius: 20 }) .animate( LayerAnimation.create() .signal("scale", beatPulse, { multiplier: 1.0, offset: 0.0 }) .fromTo("opacity", 0, 1, { start: 0, end: 15, ease: "power2.out" }), ); comp.add(reactiveCard); 4. Chained WebGPU Post-Processing VFX import { Composition, FilmGrain, Vignette, ColorBalance } from "gitframes"; const comp = new Composition({ width: 1920, height: 1080, fps: 60 }); // Whole-composition cinematic grade + film emulsion comp.apply(new Vignette({ strength: 0.28, radius: 0.85 })); comp.apply(new FilmGrain({ strength: 0.06, size: 1.5, animated: true })); comp.apply( new ColorBalance({ shadows: { cyanRed: 0, magentaGreen: 2, yellowBlue: 6 }, highlights: { cyanRed: 4, magentaGreen: 1, yellowBlue: -2 }, }), ); 5. Vision: Pin, Matte & Reframe import { Composition, Layer, Vignette } from "gitframes"; const comp = new Composition({ width: 1920, height: 1080, fps: 30 }); // Run vision on the whole composition. Models download lazily on first use. const vision = comp.withVision({ enableDetection: true, enableSegmentation: true, enablePose: true, variant: "s", confidence: 0.35, }); // Pin a caption to the primary tracked subject (smoothing + auto-hide when lost) comp.add( Layer.text("SUBJECT 01", { fontSize: 40, fill: "#f8fafc" }).pinToObject( vision.objects.primary, { anchor: "topCenter", offsetY: -48, smoothFrames: 5, hideWhenLost: true }, ), ); // Drive a shader uniform from a reactive signal — here, subject mask coverage comp.add( Layer.box({ width: 1920, height: 1080, background: "#000000" }).withEffect( new Vignette({ strength: vision.segmentation.subject.coverage, radius: 0.9 }), ), ); // Or use the one-liners for the common editorial moves: // comp.addSubjectSandwich({ source: "assets/dancer.mp4", behind: [headline], feather: 4 }); // comp.addSmartFraming({ source: "assets/action.mp4", target: vision.objects.primary, targetAspect: 9 / 16 }); // comp.addSubjectOutline(vision.segmentation.subject, { source: "assets/character.mp4", color: "#FF5A1F", width: 6 }); // Inspect a source before authoring: one-shot, ffmpeg-free, zod-serializable report const report = await comp.analyzeVisionSequence("assets/street.mp4", { tasks: ["detect", "pose"], categories: ["person"], }); console.log(report.tracks.map((t) => `${t.category}#${t.trackId} ${t.frames.join("–")}`)); Standalone runner (no composition): import { VisionRunner } from "@gitframes/vision"; const runner = VisionRunner.create({ variant: "s", confidence: 0.3 }); // zero I/O const frame = { data: rgba, width: 1920, height: 1080 }; const boxes = await runner.detect(frame); // downloads RTMDet-Ins on first call const { masks } = await runner.segment(frame); // same forward pass, no second inference const { people } = await runner.pose(frame); // RTMO, COCO-17 keypoints runner.close(); In the browser (WebGPU EP): import { VisionRunner, createWebGPUProvider, hasWebGPU } from "@gitframes/vision/web"; if (hasWebGPU()) { const runner = VisionRunner.create({ provider: createWebGPUProvider() }); } 6. Headless Video & FrameGrid Rendering import { buildMyComposition } from "./my-composition.js"; const comp = await buildMyComposition(); // 1. Single frame to a PNG buffer for visual inspection const frameBuffer = await comp.renderFrame({ frame: 45 }); // 2. Contact-sheet grid of 12 sequential frames const gridBuffer = await comp.renderFrameGrid({ startFrame: 0, endFrame: 120, stepFrames: 10, cellWidth: 320, showLabels: true, }); // 3. Final hardware-encoded MP4 with mixed audio const { filePath } = await comp.renderVideo({ outputPath: "output/final-product-film.mp4", quality: "high", concurrency: 4, }); console.log(`Video rendered successfully to: ${filePath}`); Engineering Doctrines & Best Practices Design tokens & theme contracts — define a centralized THEME for colors, type, radii, and spacing. Never hardcode magic hex values or ad-hoc margins. WebGPU premultiplied-alpha invariant — fragment shaders outputting premultiplied alpha (color * opacity * alpha) must use srcFactor: "one" in their blend state ({ srcFactor: "one", dstFactor: "one-minus-src-alpha", operation: "add" }). Never use srcFactor: "src-alpha" for premultiplied output — squaring alpha darkens fades into murky gray. Carrier match cuts — carry a visual element (badge, card, cursor, container) across scene boundaries with continuous velocity and position to avoid jarring cuts. Physical easing vocabulary — back.out(1.4–1.7) for snap-overshoot entrances, spring / expo.out for decelerating motion, power2.in for exits. Reserve linear for infinite spinners and time counters. Headless invariant verification — verify shader transforms, glyph coverage, and temporal MSE deltas with skia-canvas pixel sampling in Vitest before shipping. Agent Skills & Plugins Gitframes ships agent skills that teach Claude, Codex, and other coding agents how to write, render, and check compositions. The plugin (gitframes) is listed in Anthropic's official plugin directory and contains only skills — no MCP servers, hooks, or commands. Every other agent gets the same skills through the skills CLI. Once installed, skills load automatically when a task matches (e.g. "add a film-grain pass to this scene" or "render a frame grid of intro.ts"). What the plugin runs and sends The plugin is instructions only. It bundles no executables, MCP servers, hooks, or package launchers, and it sends no data anywhere. The skills tell your agent to add the gitframes npm package to your project and how to use it. When that code uses on-device vision, the SDK downloads the pinned model weights from Hugging Face on first use (see On-Device Vision). Nothing else leaves your machine. Claude Code /plugin install gitframes Or from your shell: claude plugin install gitframes@claude-plugins-official It installs from Anthropic's official marketplace, which Claude Code adds for you, so there is no marketplace step, and plugins from it update automatically. Afterwards, restart Claude Code or run /reload-plugins. /plugin commands need an interactive claude terminal; in the desktop app's Code tab, use the shell form or + > Plugins > Add plugin and pick Gitframes. Add --scope project to the shell form to record the plugin in .claude/settings.json for the whole team. Enable it for everyone in your repo. Commit this to .claude/settings.json; Claude Code prompts teammates to install it when they trust the folder: { "enabledPlugins": { "gitframes@claude-plugins-official": true } } Straight from this repository (tracks main instead of the directory release): /plugin marketplace add gatewai-dev/gitframes /plugin install gitframes@gitframes-plugins Codex, Cursor, Hermes, and other agents The skills CLI installs the skills into any of 70+ agents, including Codex, Cursor, Hermes, Gemini CLI, GitHub Copilot, Windsurf, OpenCode, and Goose: npx skills add gatewai-dev/gitframes It detects the agents on your machine and asks where to install. To choose them yourself, pass -a once per agent, add -g to install for your user instead of this project, and -y to skip the prompts: npx skills add gatewai-dev/gitframes -a codex -a cursor -a hermes-agent -g -y Keep them current with npx skills update, and remove them with npx skills remove. Or copy the folders by hand: put plugins/gitframes/skills/ / into .claude/skills/, .agents/skills/, or ~/.agents/skills/. VS Code / Copilot / Cursor / Kiro can load the portable plugin.json through their plugin UI. Maintaining the plugin The plugin lives in plugins/gitframes/ so installs carry only the skills; users get the engine from npm. Two manifests there describe it: plugin.json (portable Agent Plugins 1.0, which also carries the OpenAI listing metadata) and .claude-plugin/plugin.json. The marketplace catalog is .claude-plugin/marketplace.json. The portable field set is closed — client-specific fields go in that client's manifest, not in plugin.json. The version in both follows the gitframes package: pnpm run version:packages syncs it after changeset version (or run pnpm run sync:plugin-version on its own), since clients use it to decide when to update. Inside this repository, Claude Code and other agents pick up skills through the symlinks in .agents/skills/ and .claude/skills/. Skills live only under plugins/gitframes/skills/; never copy them elsewhere. pnpm run check:plugins validates manifests, skill frontmatter, marketplace catalogs, symlinks, and the generated effects catalog. pnpm run sync:effects-catalog regenerates the gitframes-effects catalog after any Effect class change. Reference Showcase Examples The examples/ directory holds production-grade reference compositions: Development & Building Gitframes uses pnpm (10+) and turbo for orchestration. # Install pnpm install # Build all packages pnpm build # Run conformance tests pnpm test # Check the vision models end to end (downloads ~380 MB of weights once) pnpm --filter @gitframes/vision test:models # Render a specific showcase example pnpm --filter @gitframes/example-21-full-circle render # Render the master brand film cd examples/19_gitframes_film && pnpm render Docker Container for Production Rendering An optimized Dockerfile.renderer deploys the renderer service into cloud GPU clusters: docker build -t gitframes-renderer -f Dockerfile.renderer . Community Discord: ask questions, share renders, and follow development at discord.gg/cbqMGGme5. YouTube: watch films made with gitframes on @gatewai.studio. License Gitframes is open-source software licensed under Apache-2.0. The vision models it downloads on demand — RTMDet-Ins and RTMO (OpenMMLab) and the Selfie Segmenter (Google) — are also Apache-2.0; see registry.ts for exact sources and checksums.