* Export videos for named platforms and check each file against its limits Exports only knew aspect ratios, so a cut could still be rejected or degraded on upload: too long for the platform, over its size limit, or far off its loudness. export_video now takes platforms (TikTok, Instagram Reels and Feed, YouTube Shorts and YouTube, Facebook Reels, LinkedIn, X, Pinterest, Snapchat Spotlight), up to six targets with the presets. Each platform is rendered to its frame with the export's fit, its frame rate cap, a bitrate cap that also keeps the file under the size limit, and its loudness target in one loudnorm pass. Every render is probed and measured afterwards. A file over the size limit is encoded once more at a bitrate scaled from what it averaged, and audio more than 2 LU off the target gets one linear loudness pass. The result is stored on the output as a pass, warn or fail check with language-neutral issue codes. A platform the video is too long or too short for is not rendered: the card lists it with the reason, the other targets still render, and the bot is told to trim first. The limits come from the earlier profile research, with its sources, review date and unverified flag; content, disclosure and caption rules are left out because they need inference or a review flow. Preset definitions move to their own module so the platform schemas can use their aspect ratios without an import cycle. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01MRtGG9ARfqqhma3t85Ro2u * Add remove_silence step to edit_video Talking-head clips are mostly cut by hand to drop pauses, and the bot had no way to find them: it could only keep segments it guessed. The new remove_silence step finds every pause quieter than threshold_db (default -35 dB) and at least min_silence long (default 0.6 s), keeps padding (default 0.12 s) on each side so word onsets survive, and cuts the rest through the existing keep-segments graph, so picture and sound stay in sync. Steps run in order, so silence is detected on the timeline the steps before it leave: the planner gets an analysis mode that sends that audio through silencedetect into a null output, and ametadata prints the marks to a file in the job directory. The detected speech is handed back to the planner per step. Nothing silent makes the step a no-op; nothing left fails with a clear error. At most 200 pieces are kept, bridging the shortest pauses first, so the filter graph stays a sensible size. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01MRtGG9ARfqqhma3t85Ro2u * Offer platforms in the media card's export menu and show their checks The card's Export menu now lists the platforms first, by brand name and untranslated, then the aspect ratios after a separator; the platform an output was made for is left out, like its own shape. A platform export shows the platform's name under the player, or, when its check did not pass, one line for the first issue instead ("Too long for TikTok (max 10:00)"), in the warning or destructive color. Skipped platforms get the same kind of line, and they stand in for the error of a job that skipped every platform, so the card does not say the same thing twice in two languages. The E2E spec seeds a platform export with a failed check and a skipped platform, and captures the card and the open menu. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01MRtGG9ARfqqhma3t85Ro2u * Add analyze_video to find cut points for the bot To cut a social video well the bot has to know where the cuts fit, and it could not look at a video at all. analyze_video is a read-only media job: one ffmpeg pass prints scene changes (scene score above 0.3, one per cut), pauses (silencedetect with the remove_silence defaults) and short-term loudness (ebur128) to files in the job directory, and a transcript made from the video by transcribe (an SRT whose parent is the video) adds when each line is spoken. The job stores nothing; its card just says done. The result reaches the bot through the existing media job path: the job always wakes the bot that started it (the tool has no continue_after) and the summary carries a short legend plus compact JSON. The runtime's summary limit grows to 12,000 characters and the summary is fitted into it: at most 200 entries per list, then every list shrinks evenly, keeping the strongest cuts, the longest pauses and the start of the transcript, with omitted counts. The wake prompt no longer claims the card shows the result when a job made no files. Jobs read derived artifacts through a new fetchDerivedArtifact, scoped to the job's user like fetchArtifact. A migration admits the operation in the media_jobs check and adds the tool to existing Studio specialists that still edit video. The web card gets an icon; no new copy. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01MRtGG9ARfqqhma3t85Ro2u * fix(media): keep one skipped entry per platform on the card A job that recovers after a crash runs again and reports the same skipped platforms; the newest reason per platform now replaces the earlier one instead of listing it twice. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01MRtGG9ARfqqhma3t85Ro2u * chore(db): add the widened media job operation check as NOT VALID and validate it separately Follows the repository's migration convention: the constraint commits without scanning media_jobs, and a second migration validates it with a lock that lets reads and writes continue. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01MRtGG9ARfqqhma3t85Ro2u * Brand videos with a logo overlay and an intro before the cut edit_video gains an overlay step that lays a PNG, JPEG or WebP image over the video at a corner or the center, sized to a share of the frame width, with optional opacity and time range; tall frames keep the 9:16 safe areas clear. concat takes at: "start" so an intro can go before the video. The image is authorized like other inputs, read with its own demuxer and refused above 8192 pixels on a side. New Studio specialists are told to brand with the user's logo and intro. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01MRtGG9ARfqqhma3t85Ro2u --------- Co-authored-by: Claude <noreply@anthropic.com>
452 lines
18 KiB
TypeScript
452 lines
18 KiB
TypeScript
import { readFile, rm } from "node:fs/promises";
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import { join } from "node:path";
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import { MEDIA_PLATFORM_PROFILES, MediaPlatformIdSchema } from "@rakazo/contracts";
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import { afterAll, beforeAll, describe, expect, it } from "vitest";
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import { builtinAgentTools } from "../builtin-tools.js";
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import { createMediaOperations } from "../media-operations.js";
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import { MEDIA_CONTINUE_AFTER_PROPERTY } from "../media-tools.js";
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import { parametersFor } from "../pi-runtime.js";
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import { detectFfmpeg, measureLoudness, probeMedia, runFfmpeg } from "./ffmpeg.js";
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import type { TestArtifact, TestClips } from "./media-test-support.js";
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import { createTestClips, fakeMediaJobContext } from "./media-test-support.js";
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import { LOUDNESS_TOLERANCE_LU } from "./platform-export.js";
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import { cleanFileName, createVideoOperations, subtitleFormat } from "./video-operations.js";
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const ffmpeg = await detectFfmpeg();
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function operation(name: "edit_video" | "export_video", deps = {}) {
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const registration = createVideoOperations(deps).find((entry) => entry.operation === name);
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if (!registration) throw new Error(`missing ${name}`);
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return registration;
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}
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describe("video operation registrations", () => {
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it("are registered for the runtime and unavailable without ffmpeg", async () => {
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const names = createMediaOperations({ loadSettings: async () => ({}) as never }).map(
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(entry) => entry.operation,
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);
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expect(names).toEqual(expect.arrayContaining(["edit_video", "export_video"]));
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const edit = operation("edit_video", { detect: async () => null });
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expect(await edit.available({ spaceId: "space-1" })).toBe(false);
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const { context } = await fakeMediaJobContext({}, {});
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await expect(edit.handler.run(context)).rejects.toThrow(
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"ffmpeg and ffprobe are not installed on this server.",
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);
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});
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it("names every artifact the input reads so start() can authorize them", () => {
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const edit = operation("edit_video");
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const input = edit.inputSchema.parse({
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artifact_id: "src",
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title: "Reel",
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steps: [
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{ op: "concat", artifact_ids: ["clip-1", "clip-2"] },
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{ op: "subtitles", artifact_id: "subs" },
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{ op: "overlay", artifact_id: "logo" },
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],
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});
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expect(edit.artifactIds?.(input)).toEqual(["src", "clip-1", "clip-2", "subs", "logo"]);
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const exported = operation("export_video");
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const exportInput = exported.inputSchema.parse({ artifact_id: "src", presets: ["vertical"] });
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expect(exported.artifactIds?.(exportInput)).toEqual(["src"]);
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});
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it("titles exports by platform and aspect ratio unless the bot gave a title", () => {
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const exported = operation("export_video");
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expect(
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exported.title(
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exported.inputSchema.parse({ artifact_id: "a", presets: ["vertical", "square"] }),
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),
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).toBe("9:16 · 1:1");
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expect(
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exported.title(
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exported.inputSchema.parse({
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artifact_id: "a",
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platforms: ["tiktok", "youtube-shorts"],
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presets: ["square"],
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}),
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),
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).toBe("TikTok · YouTube Shorts · 1:1");
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expect(
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exported.title(
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exported.inputSchema.parse({ artifact_id: "a", presets: ["vertical"], title: "Reel" }),
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),
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).toBe("Reel");
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});
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it("defines tools named like the operations, with continue_after, that the runtime can encode", () => {
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for (const name of ["edit_video", "export_video"]) {
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const tool = builtinAgentTools.find((entry) => entry.name === name);
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expect(tool).toBeDefined();
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expect(tool?.inputSchema.properties).toMatchObject(MEDIA_CONTINUE_AFTER_PROPERTY);
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expect(() => parametersFor(tool!)).not.toThrow();
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}
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});
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it("tells the model every platform id with its longest video", () => {
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const tool = builtinAgentTools.find((entry) => entry.name === "export_video")!;
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const properties = tool.inputSchema.properties as Record<
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string,
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{ items?: { enum?: string[] } }
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>;
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expect(properties.platforms?.items?.enum).toEqual(MediaPlatformIdSchema.options);
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for (const profile of Object.values(MEDIA_PLATFORM_PROFILES)) {
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expect(tool.description).toContain(`${profile.id} ${profile.duration.hardMaxMs / 1000} s`);
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}
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});
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});
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describe("subtitle and file name helpers", () => {
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it("recognizes SRT and VTT by content only", () => {
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expect(subtitleFormat("WEBVTT\n\n00:00.000 --> 00:01.000\nHi")).toBe("vtt");
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expect(subtitleFormat("1\r\n00:00:01,000 --> 00:00:02,000\r\nHi")).toBe("srt");
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expect(subtitleFormat("00:00:01.000 --> 00:00:02.000\nHi")).toBe("srt");
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expect(subtitleFormat("#EXTM3U\n#EXTINF:1,\nfile:/etc/passwd")).toBeNull();
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expect(subtitleFormat("WEBVTTX")).toBeNull();
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});
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it("cleans file names without losing the user's language", () => {
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expect(cleanFileName("Reel: Sommer/Urlaub 2026", "video")).toBe("Reel Sommer Urlaub 2026");
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expect(cleanFileName("../..\u0000", "video")).toBe("video");
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expect(cleanFileName("x".repeat(200), "video")).toHaveLength(80);
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});
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});
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// CI runners may not have ffmpeg; these run wherever it is installed.
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describe.skipIf(!ffmpeg)("video operations with ffmpeg", () => {
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let clips: TestClips;
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let artifacts: Record<string, TestArtifact>;
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beforeAll(async () => {
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clips = await createTestClips();
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artifacts = {
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land: { path: clips.landscape, name: "Landscape clip.mp4", mimeType: "video/mp4" },
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silent: { path: clips.silentPortrait, name: "silent.mp4", mimeType: "video/mp4" },
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srt: { path: clips.srt, name: "captions.srt", mimeType: "application/x-subrip" },
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vtt: { path: clips.vtt, name: "captions.vtt", mimeType: "text/vtt" },
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notes: { path: clips.landscape, name: "notes.txt", mimeType: "text/plain" },
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logo: { path: clips.logo, name: "logo.png", mimeType: "image/png" },
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};
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});
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afterAll(async () => {
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if (clips) await rm(clips.dir, { recursive: true, force: true });
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});
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async function run(name: "edit_video" | "export_video", input: Record<string, unknown>) {
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const registration = operation(name);
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const job = await fakeMediaJobContext(registration.inputSchema.parse(input), artifacts);
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try {
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const result = await registration.handler.run(job.context);
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return { ...job, result };
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} finally {
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await rm(job.tempDir, { recursive: true, force: true });
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}
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}
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it("cuts, reframes to 9:16 with a blurred fill, burns in subtitles and speeds up", async () => {
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const { outputs, progress, result } = await run("edit_video", {
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artifact_id: "land",
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title: "Sommer-Reel",
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steps: [
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{ op: "trim", start: 0.5, end: 2.5 },
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{ op: "reframe", preset: "vertical", fit: "blur" },
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{ op: "subtitles", artifact_id: "srt" },
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{ op: "speed", factor: 2 },
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{ op: "poster", at: 0.5 },
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],
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});
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expect(outputs).toHaveLength(1);
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const [output] = outputs;
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expect(output).toMatchObject({
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name: "Sommer-Reel.mp4",
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mimeType: "video/mp4",
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width: 1080,
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height: 1920,
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parentArtifactId: "land",
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});
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expect(output!.posterSize).toBeGreaterThan(0);
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expect(Math.abs(output!.durationMs! - 1000)).toBeLessThan(150);
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expect(progress.at(-1)).toBe(1);
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expect(progress).toEqual([...progress].sort((a, b) => a - b));
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expect(result.summary).toContain("Sommer-Reel.mp4 (1080×1920, ");
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});
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it("keeps segments, appends a silent clip with generated silence, and changes volume", async () => {
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const registration = operation("edit_video");
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const job = await fakeMediaJobContext(
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registration.inputSchema.parse({
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artifact_id: "land",
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title: "Cut",
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steps: [
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{
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op: "keep",
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segments: [
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{ start: 0, end: 0.5 },
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{ start: 2, end: 2.8 },
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],
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},
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{ op: "concat", artifact_ids: ["silent"] },
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{ op: "volume", gain: 0.5 },
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{ op: "subtitles", artifact_id: "vtt" },
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],
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}),
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artifacts,
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);
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await registration.handler.run(job.context);
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const [output] = job.outputs;
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const probe = await probeMedia(output!.path, { mimeType: "video/mp4" });
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expect(probe.hasAudio).toBe(true);
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expect(probe.video).toMatchObject({ width: 320, height: 180, rotation: 0 });
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expect(Math.abs(probe.durationMs - 3300)).toBeLessThan(200);
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await rm(job.tempDir, { recursive: true, force: true });
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});
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it("plays rotated phone video upright without rotating it twice", async (context) => {
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const rotated = join(clips.dir, "rotated.mp4");
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try {
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await runFfmpeg([
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...["-display_rotation", "90", "-i", `file:${clips.landscape}`],
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...["-c", "copy", `file:${rotated}`],
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]);
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} catch {
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context.skip("this ffmpeg cannot write rotation metadata");
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}
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const registration = operation("edit_video");
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const job = await fakeMediaJobContext(
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registration.inputSchema.parse({
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artifact_id: "rotated",
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title: "Upright",
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steps: [{ op: "trim", start: 0, end: 1 }],
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}),
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{ rotated: { path: rotated, name: "rotated.mp4", mimeType: "video/mp4" } },
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);
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await registration.handler.run(job.context);
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const probe = await probeMedia(job.outputs[0]!.path, { mimeType: "video/mp4" });
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expect(probe.video).toMatchObject({ width: 180, height: 320, rotation: 0 });
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await rm(job.tempDir, { recursive: true, force: true });
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});
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it("puts an intro first and a translucent logo on the main part", async () => {
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const red = join(clips.dir, "red.mp4");
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const green = join(clips.dir, "green.mp4");
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for (const [path, color] of [
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[red, "red"],
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[green, "green"],
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] as const) {
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await runFfmpeg([
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...["-f", "lavfi", "-i", `color=c=${color}:size=320x180:rate=25:duration=1`],
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...["-c:v", "libx264", "-preset", "ultrafast", "-pix_fmt", "yuv420p", `file:${path}`],
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]);
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}
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const registration = operation("edit_video");
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const job = await fakeMediaJobContext(
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registration.inputSchema.parse({
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artifact_id: "red",
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title: "Branded",
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steps: [
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{ op: "overlay", artifact_id: "logo", position: "top_left", width: 0.25, opacity: 0.5 },
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{ op: "concat", artifact_ids: ["green"], at: "start" },
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],
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}),
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{
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...artifacts,
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red: { path: red, name: "red.mp4", mimeType: "video/mp4" },
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green: { path: green, name: "green.mp4", mimeType: "video/mp4" },
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},
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);
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try {
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await registration.handler.run(job.context);
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const output = job.outputs[0]!.path;
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expect((await probeMedia(output, { mimeType: "video/mp4" })).durationMs).toBeCloseTo(
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2000,
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-2,
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);
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// The logo is 80×40 at 13, 7: half blue over red there, plain red elsewhere, none on the intro.
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const [introR, introG] = await pixel(output, 0.5, 30, 20);
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expect(introG).toBeGreaterThan(100);
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expect(introR).toBeLessThan(40);
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const [r, g, b] = await pixel(output, 1.5, 50, 25);
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expect(r).toBeGreaterThan(90);
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expect(r).toBeLessThan(170);
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expect(b).toBeGreaterThan(90);
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expect(b).toBeLessThan(170);
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expect(g).toBeLessThan(40);
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const [plainR, , plainB] = await pixel(output, 1.5, 200, 120);
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expect(plainR).toBeGreaterThan(200);
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expect(plainB).toBeLessThan(40);
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} finally {
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await rm(job.tempDir, { recursive: true, force: true });
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}
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});
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async function pixel(path: string, at: number, x: number, y: number): Promise<number[]> {
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const out = join(clips.dir, `pixel-${at}-${x}-${y}.rgb`);
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await runFfmpeg([
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...["-ss", String(at), "-i", `file:${path}`, "-frames:v", "1"],
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...["-vf", `format=rgb24,crop=1:1:${x}:${y}`, "-f", "rawvideo", `file:${out}`],
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]);
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return [...(await readFile(out))];
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}
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it("extracts the audio as M4A", async () => {
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const { outputs } = await run("edit_video", {
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artifact_id: "land",
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title: "Ton",
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steps: [{ op: "trim", start: 1, end: 2 }, { op: "extract_audio" }],
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});
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expect(outputs).toEqual([
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expect.objectContaining({ name: "Ton.m4a", mimeType: "audio/mp4", parentArtifactId: "land" }),
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]);
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expect(outputs[0]?.posterPath).toBeUndefined();
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expect(Math.abs(outputs[0]!.durationMs! - 1000)).toBeLessThan(100);
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});
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it("saves a single frame when poster is the only step", async () => {
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const { outputs } = await run("edit_video", {
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artifact_id: "land",
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title: "Cover",
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steps: [{ op: "poster", at: 1 }],
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});
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expect(outputs).toEqual([
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expect.objectContaining({
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name: "Cover.jpg",
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mimeType: "image/jpeg",
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width: 320,
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height: 180,
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}),
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]);
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});
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it("exports one output per distinct preset with posters and batch progress", async () => {
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const { outputs, progress } = await run("export_video", {
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artifact_id: "land",
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presets: ["square", "portrait", "square"],
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fit: "pad",
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});
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expect(
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outputs.map((output) => [output.name, output.preset, output.width, output.height]),
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).toEqual([
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["Landscape clip-1x1.mp4", "square", 1080, 1080],
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["Landscape clip-4x5.mp4", "portrait", 1080, 1350],
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]);
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for (const output of outputs) {
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expect(output.parentArtifactId).toBe("land");
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expect(output.posterSize).toBeGreaterThan(0);
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}
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expect(progress).toContain(0.5);
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expect(progress.at(-1)).toBe(1);
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});
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it("exports for a platform at its frame, frame rate and loudness, and skips one it is too short for", async () => {
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const fast = join(clips.dir, "fast.mp4");
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await runFfmpeg([
|
||
...["-f", "lavfi", "-i", "testsrc=size=320x180:rate=60:duration=4"],
|
||
...["-f", "lavfi", "-i", "sine=frequency=330:sample_rate=48000:duration=4"],
|
||
...["-c:v", "libx264", "-preset", "ultrafast", "-pix_fmt", "yuv420p"],
|
||
...["-c:a", "aac", "-shortest", `file:${fast}`],
|
||
]);
|
||
const registration = operation("export_video");
|
||
const job = await fakeMediaJobContext(
|
||
registration.inputSchema.parse({
|
||
artifact_id: "fast",
|
||
platforms: ["tiktok", "snapchat-spotlight"],
|
||
}),
|
||
{ fast: { path: fast, name: "Fast clip.mp4", mimeType: "video/mp4" } },
|
||
);
|
||
try {
|
||
const result = await registration.handler.run(job.context);
|
||
expect(job.skipped).toEqual([
|
||
{
|
||
platform: "snapchat-spotlight",
|
||
issues: [{ code: "too_short", minMs: 5000, actualMs: expect.any(Number) }],
|
||
},
|
||
]);
|
||
expect(job.outputs).toHaveLength(1);
|
||
const [output] = job.outputs;
|
||
expect(output).toMatchObject({
|
||
name: "Fast clip-tiktok.mp4",
|
||
platform: "tiktok",
|
||
width: 1080,
|
||
height: 1920,
|
||
parentArtifactId: "fast",
|
||
check: { status: "pass", issues: [] },
|
||
});
|
||
expect(output!.posterSize).toBeGreaterThan(0);
|
||
const probe = await probeMedia(output!.path, { mimeType: "video/mp4" });
|
||
expect(probe.video).toMatchObject({ codec: "h264", width: 1080, height: 1920, fps: 30 });
|
||
expect(Math.abs(probe.durationMs - 4000)).toBeLessThan(100);
|
||
const loudness = await measureLoudness(
|
||
output!.path,
|
||
{ integratedLufs: -14, truePeakDb: -1 },
|
||
{ mimeType: "video/mp4" },
|
||
);
|
||
expect(Math.abs(loudness.integratedLufs + 14)).toBeLessThanOrEqual(LOUDNESS_TOLERANCE_LU);
|
||
expect(result.summary).toContain("Fast clip-tiktok.mp4 for TikTok (1080×1920, 4 s)");
|
||
expect(result.summary).toContain(
|
||
"Not exported for Snapchat Spotlight: 4 s is shorter than its 5 s minimum; use a longer video.",
|
||
);
|
||
expect(job.progress.at(-1)).toBe(1);
|
||
} finally {
|
||
await rm(job.tempDir, { recursive: true, force: true });
|
||
}
|
||
});
|
||
|
||
it("fails without rendering when the video fits none of the platforms", async () => {
|
||
const registration = operation("export_video");
|
||
const job = await fakeMediaJobContext(
|
||
registration.inputSchema.parse({ artifact_id: "land", platforms: ["snapchat-spotlight"] }),
|
||
artifacts,
|
||
);
|
||
await expect(registration.handler.run(job.context)).rejects.toThrow(
|
||
"Not exported for Snapchat Spotlight: 3 s is shorter than its 5 s minimum; use a longer video.",
|
||
);
|
||
expect(job.skipped.map((target) => target.platform)).toEqual(["snapchat-spotlight"]);
|
||
expect(job.outputs).toEqual([]);
|
||
await rm(job.tempDir, { recursive: true, force: true });
|
||
});
|
||
|
||
it("fails clearly on ranges past the end, non-subtitle files and non-videos", async () => {
|
||
await expect(
|
||
run("edit_video", {
|
||
artifact_id: "land",
|
||
title: "x",
|
||
steps: [{ op: "trim", start: 5, end: 6 }],
|
||
}),
|
||
).rejects.toThrow("trim 5–6 s is outside the 3 s video.");
|
||
await expect(
|
||
run("edit_video", {
|
||
artifact_id: "land",
|
||
title: "x",
|
||
steps: [{ op: "subtitles", artifact_id: "land" }],
|
||
}),
|
||
).rejects.toThrow("Landscape clip.mp4 is not an SRT or VTT file.");
|
||
await expect(
|
||
run("edit_video", {
|
||
artifact_id: "land",
|
||
title: "x",
|
||
steps: [{ op: "overlay", artifact_id: "srt" }],
|
||
}),
|
||
).rejects.toThrow("captions.srt is not a PNG, JPEG or WebP image.");
|
||
await expect(
|
||
run("export_video", { artifact_id: "notes", presets: ["square"] }),
|
||
).rejects.toThrow("notes.txt is not a video.");
|
||
});
|
||
|
||
it("stops ffmpeg and stores nothing when the job is cancelled", async () => {
|
||
const registration = operation("export_video");
|
||
const job = await fakeMediaJobContext(
|
||
registration.inputSchema.parse({ artifact_id: "land", presets: ["cinema"], fit: "blur" }),
|
||
artifacts,
|
||
);
|
||
const progress = job.context.progress;
|
||
job.context.progress = async (fraction) => {
|
||
await progress(fraction);
|
||
job.controller.abort(new Error("cancelled"));
|
||
};
|
||
await expect(registration.handler.run(job.context)).rejects.toThrow("cancelled");
|
||
expect(job.outputs).toEqual([]);
|
||
await rm(job.tempDir, { recursive: true, force: true });
|
||
});
|
||
});
|