Skill: zoucdr/hydraulic-crush-video (version 0.1.0)

--- name: hydraulic-crush-video version: 1.0.0 description: "Generate vertical hydraulic-press crushing shorts (WeryAI): text or object image to flatten/burst motion—destruction aesthetics and material contrast for short-video feeds. Use when you need hydraulic press crushing clips, satisfying crush video, crushing ASMR, or the user asks to crush lipstick, glass marbles, or gadgets. SEO: hydraulic crush video; hydraulic press crush video generation." tags: [satisfying, destruction, curiosity, short-video, hydraulic-press] metadata: { "openclaw": { "emoji": "🔧", "primaryEnv": "WERYAI_API_KEY", "paid": true, "network_required": true, "requires": { "env": ["WERYAI_API_KEY"], "bins": ["node"], "node": ">=18" } } } user-invocable: true --- # Hydraulic press crush video generation Built for destruction-aesthetic and satisfying creators. Lipstick smear bursts, glass marbles shattering, gadget shells tearing—one line of copy or one image → 9:16 vertical. Core hooks: material contrast, unexpected internals, tension between machine force and fragile objects. **Dependencies**: `scripts/video_gen.js` in this directory + `WERYAI_API_KEY` + Node.js 18+. No dependency on other Cursor skills. ## Prerequisites - `WERYAI_API_KEY` **must be set** in the environment before running `video_gen.js`. - Node.js **18+** is required. Image inputs **must** be public `https` URLs (no local file paths). - Each successful `wait` run consumes WeryAI credits; re-running creates new paid tasks. ## Security, secrets, and API hosts - **`WERYAI_API_KEY`**: Treat as a secret. Only configure it if you trust this skill's source; it is listed in OpenClaw metadata as **`requires.env`** / **`primaryEnv`** so installers know it is mandatory at runtime (never commit it inside the skill package). - **API hosts (fixed in `video_gen.js`)**: Video tasks use **`https://api.weryai.com`**; the models list uses **`https://api-growth-agent.weryai.com`**. Only **`WERYAI_API_KEY`** is read from the environment—do not rely on URL-related environment variables. - **Higher assurance**: Run generation in a short-lived or isolated environment (separate account or container), and review `scripts/video_gen.js` (HTTPS submit + poll loop) before production use. ## Prompt expansion (mandatory) `video_gen.js` does **not** expand prompts. Before every `wait --json`, turn the user's short or vague brief into a **full English production `prompt`**. **When:** The user gives only keywords, one line, or loose intent—or asks for richer video language. **Exception:** They paste a finished long prompt within the model's `prompt_length_limit` and ask you **not** to rewrite; still show the **full** text in the confirmation table. **Always add (video language):** shot scale and angle; camera move or lock-off; light quality and motivation; subject action paced to `duration`; **one clear payoff** for this niche; state **9:16 vertical** when this skill defaults to vertical. **Length:** Obey `prompt_length_limit` for the chosen `model_key` when this doc lists it; trim filler adjectives before removing core action, lens, or light clauses. **Confirmation:** The pre-submit table **must** include the **full expanded `prompt`** (never a one-line summary). Wait for **confirm** or edits. ### Niche checklist - **Crush read:** compression progress, material yield (bend, burst, powder), **one peak squeeze**; object identity clear. - **Camera:** locked macro or slow push; industrial plate framing; stylized destruction only. - **Audio:** metal groan, pop, hiss if `generate_audio` true. **`### Example prompts`** at the top of this file are **short triggers only**—always expand from the user's actual request. ## Workflow 1. Confirm the user request matches this skill's scenario (text-to-video and/or image-to-video as documented). 2. Collect the user's **brief**, optional image URL(s), tier (**best** / **good** / **fast**) or an explicit `model` key. 3. **Expand prompt (mandatory):** Unless the user supplied a finished long prompt and explicitly asked not to rewrite it, expand the brief into a full English production `prompt` using `## Prompt expansion (mandatory)` below. **Do not** call the API with only the user's minimal words. 4. Check the **expanded** `prompt` against the selected model's `prompt_length_limit` in the frozen tables in this document (when present); shorten if needed. 5. Verify `duration`, `aspect_ratio`, `resolution`, `generate_audio`, `negative_prompt`, and other fields against the frozen tables and API notes in this SKILL.md. 6. Show the pre-submit parameter table including the **full expanded `prompt`**; wait for **confirm** or edits. 7. After confirmation, run `node {baseDir}/scripts/video_gen.js wait --json '...'` with the **expanded** prompt. 8. Parse stdout JSON and return video URLs; on failure, surface `errorCode` / `errorMessage` and suggest parameter fixes. ## CLI reference ```sh node {baseDir}/scripts/video_gen.js wait --json '{"model":"…","prompt":"…","duration":5,"aspect_ratio":"9:16"}' node {baseDir}/scripts/video_gen.js wait --json '…' --dry-run node {baseDir}/scripts/video_gen.js status --task-id <id> ``` ## Definition of done Done when the user receives at least one playable video URL from the API response, or a clear failure explanation with next steps. All parameters used **must** fall within the selected model's allowed sets in this document. The submitted `prompt` **must** be the **expanded** production prompt unless the user explicitly supplied a finished long prompt and asked not to rewrite it. ## Boundaries (out of scope) - Does not review platform compliance, copyright, or portrait rights; does not guarantee commercial usability of outputs. - Does not provide non-WeryAI offline rendering, traditional edit timelines, or API field combinations not documented here. - Does not hard-code absolute paths in the skill doc; `{baseDir}` means the skill package root (same level as `SKILL.md`). ### Example prompts - `9:16 hydraulic press crushing a glass marble: slow motion and burst close-ups` - `Use this image: lipstick crushed flat with color burst, vertical` - `Destruction-aesthetic short: crush electronic shell, stress material tear and internal contrast` - `Generate a vertical hydraulic press crush clip with cold lighting and macro deformation` --- ## Default parameters | Field | Value | |-------|-------| | Model | KLING_V3_0_PRO | | Aspect ratio | 9:16 (fixed vertical) | | Duration | Short (`duration`: 5—minimum for KLING_V3_0_PRO) | | Audio | On (press + break sounds are core satisfaction) | | Visual style | Extreme macro, cool white light, slow motion, minimal background—deformation and detail first | > **API validity (default `KLING_V3_0_PRO`):** Text-to-video `duration` only **5 / 10 / 15**; `aspect_ratio` only **9:16, 1:1, 16:9**. Image-to-video `aspect_ratio` only **9:16, 16:9, 1:1**. **No `resolution` field—do not send it.** For **fast** tier with VEO: text-to-video **`VEO_3_1_FAST`**, image-to-video **`CHATBOT_VEO_3_1_FAST`**, with `duration` **fixed at 8**, `aspect_ratio` only **9:16** or **16:9**. When switching `model_key`, follow the allowed sets in this section’s model/API constraints and the API validity note above; do not send `resolution` to models that do not support it. --- ## Text-to-video crush User names the object and how it should fail—good for batch ideas, testing hooks, or quick look dev. **The user should provide:** - Object (lipstick, glass marble, figure, phone case, macaron, etc.) - Optional failure mode (slow squash / instant burst / contents jet / deform-then-crack) - Optional visual emphasis (internals, colored splash, etc.) **Generation flow:** 1. Collect object and failure mode; ask if missing. 2. Prompt: press descent, deformation detail, material contrast, slow motion. 3. After user confirmation, run (`{baseDir}` is skill root): ```sh node {baseDir}/scripts/video_gen.js wait --json '{"model":"KLING_V3_0_PRO","prompt":"（full English prompt this run）","aspect_ratio":"9:16","duration":5,"generate_audio":true}' ``` Replace JSON fields with confirmed values; add `resolution` only if the model supports it. Parse stdout `videos`. 4. Return URLs and note what to tweak next (hook angle, material, speed). **Parameter configuration:** | Field | Value | |-------|-------| | model | KLING_V3_0_PRO | | aspect_ratio | 9:16 | | duration | 5 | | generate_audio | true | **Expanded prompt:** Compose at generation time per `## Prompt expansion (mandatory)` from the user's actual brief—do not reuse fixed sample paragraphs. **Expected outcome:** Ram enters from top, object deforms/bursts in macro, generated mechanical + material audio, strong tension and satisfaction. --- ## Image-to-video crush Public **HTTPS** image of an object → that object crushed slowly or bursting. Reuse assets or brand-specific product shots. **The user should provide:** - Image URL (public `https`, not a local path) - Desired failure (slow flatten / burst / crack spread / contents squeeze-out) **Generation flow:** 1. Confirm URL starts with `https://` (not local). 2. Infer material; tailor deformation/break language. 3. After confirmation: ```sh node {baseDir}/scripts/video_gen.js wait --json '{"model":"KLING_V3_0_PRO","prompt":"（full English prompt）","image":"（user HTTPS image URL）","aspect_ratio":"9:16","duration":5,"generate_audio":true}' ``` Match the confirmation table. Parse stdout for URLs. 4. Return URLs. **Parameter configuration:** | Field | Value | |-------|-------| | model | KLING_V3_0_PRO | | aspect_ratio | 9:16 | | duration | 5 | | generate_audio | true | | image | User-supplied image URL | **Expanded prompt:** Compose at generation time per `## Prompt expansion (mandatory)` from the user's actual brief—do not reuse fixed sample paragraphs. **Expected outcome:** Motion centered on the uploaded object, consistent look, readable material detail. --- ## Prompt building tips **Pressure:** `descends with immense force`, `hydraulic ram presses down`, `irresistible mechanical pressure`, `slow inevitable crush` **Materials:** - Brittle (glass/ceramic): `shatters into fragments`, `hairline fractures propagate`, `crystalline shards cascade` - Soft (lipstick/food): `deforms and smears`, `internal contents squeeze out`, `viscous material splays` - Hard shells (electronics/toys): `casing buckles and splits`, `internal structure exposed`, `plastic fractures with resistance` **Slow motion:** `240fps extreme slow motion`, `time dilation effect`, `every deformation frozen in detail` **Contrast hooks:** `unexpectedly satisfying internal structure`, `reveals hidden contents`, `luxury exterior destroyed` > **Note:** Image URLs must be publicly reachable over HTTPS or the API errors. Upload to an image host first if needed.

path: eval.yaml
content: version: "1"
defaults:
  trials: 3
  timeout: 600
  threshold: 0.75
skill: hydraulic-crush-video
trigger_tests:
  - id: explicit-invoke
    should_trigger: true
    prompt: "Use the $hydraulic-crush-video skill to create a sample vertical video."
  - id: implicit-domain
    should_trigger: true
    prompt: "生成一段 9:16 液压机压碎玻璃球的解压短视频，要慢动作和爆裂特写"
  - id: negative-control
    should_trigger: false
    prompt: "Refactor my React dashboard and add unit tests."
outcome_tests:
  - id: skill-package-layout
    type: deterministic
    checks:
      - description: "SKILL.md exists with name matching folder slug"
      - file_exists: "SKILL.md"
      - file_exists: "scripts/video_gen.js"
  - id: optional-quality
    type: llm_rubric
    rubric: |
      Does the assistant stay within WeryAI video_gen constraints (model, duration, aspect_ratio)?
      Does it refuse local image paths and ask for https URLs?


path: scripts/video_gen.js
content: #!/usr/bin/env node
/**
 * WeryAI 视频生成 — 通用 CLI（零 npm 依赖，需 Node.js 18+）
 *
 * 命令:
 *   wait | submit-text | submit-image | submit-multi-image | status | models
 *
 * 示例:
 *   node video_gen.js models
 *   node video_gen.js models --mode text_to_video
 *   node video_gen.js wait --json '{"model":"WERYAI_VIDEO_1_0","prompt":"...","duration":5}'
 *   node video_gen.js wait --json '...' --dry-run
 *
 * 环境变量:
 *   WERYAI_API_KEY（models / 生成 / status 必填；--dry-run 除外）— 敏感凭据，勿写入仓库；registry 元数据声明见同目录 SKILL.md。
 *   本脚本仅读取上述密钥；API 主机与轮询间隔为固定常量，勿用其他 env 覆盖。
 */

const DEFAULT_MODEL = 'WERYAI_VIDEO_1_0';
const BASE_URL = 'https://api.weryai.com';
const MODELS_BASE_URL = 'https://api-growth-agent.weryai.com';
const MODELS_API_PATH = '/growthai/v1/video/models';
const POLL_INTERVAL_MS = 6000;
const POLL_TIMEOUT_MS = 600000;

const STATUS_MAP = {
  waiting: 'waiting',
  WAITING: 'waiting',
  pending: 'waiting',
  PENDING: 'waiting',
  processing: 'processing',
  PROCESSING: 'processing',
  succeed: 'completed',
  SUCCEED: 'completed',
  success: 'completed',
  SUCCESS: 'completed',
  failed: 'failed',
  FAILED: 'failed',
};

const ERROR_MESSAGES = {
  400: 'Bad request — check your request parameters.',
  403: 'Invalid API key or IP access denied — verify WERYAI_API_KEY is correct.',
  500: 'WeryAI server error. Please try again later.',
  1001: 'Request rate limit exceeded — slow down and retry after a moment.',
  1002: 'Parameter error — check prompt, image URLs, aspect_ratio, model key, and duration.',
  1003: 'Resource not found — the task_id may not exist or has expired.',
  1006: 'Model not supported — check the model key or try a different model.',
  1007: 'Queue full — the service is busy, please try again later.',
  1011: 'Insufficient credits — recharge at weryai.com.',
  2003: 'Content flagged by safety system — revise your prompt or input image.',
  2004: 'Image format not supported — use jpg, png, or webp.',
  6004: 'Generation failed — please try again later.',
  6010: 'Concurrent task limit reached (max 20) — wait for existing tasks to complete.',
};

function log(msg) {
  process.stderr.write(`[weryai] ${msg}\n`);
}

function sleep(ms) {
  return new Promise((r) => setTimeout(r, ms));
}

async function httpJson(method, fullUrl, body, apiKey) {
  const headers = {
    Authorization: `Bearer ${apiKey}`,
    'Content-Type': 'application/json',
  };
  const controller = new AbortController();
  const timer = setTimeout(() => controller.abort(), 30_000);
  try {
    const res = await fetch(fullUrl, {
      method,
      headers,
      body: body != null ? JSON.stringify(body) : undefined,
      signal: controller.signal,
    });
    clearTimeout(timer);
    let data;
    try {
      data = await res.json();
    } catch {
      data = { status: res.status, msg: `Non-JSON response (HTTP ${res.status})` };
    }
    return { httpStatus: res.status, ...data };
  } catch (err) {
    clearTimeout(timer);
    if (err.name === 'AbortError') {
      throw new Error(`Request timeout: ${method} ${fullUrl}`);
    }
    throw err;
  }
}

async function apiRequest(method, path, body, apiKey) {
  return httpJson(method, BASE_URL + path, body, apiKey);
}

async function fetchModelsRegistry(apiKey) {
  return httpJson('GET', MODELS_BASE_URL + MODELS_API_PATH, null, apiKey);
}

function isApiSuccess(res) {
  const httpOk = res.httpStatus >= 200 && res.httpStatus < 300;
  const bodyOk = res.status === 0 || res.status === 200;
  return httpOk && bodyOk;
}

function formatApiError(res) {
  const httpStatus = res.httpStatus || 0;
  const code = res.status;
  const msg = res.msg || '';

  if (httpStatus === 403) {
    return {
      ok: false,
      phase: 'failed',
      errorCode: '403',
      errorMessage: ERROR_MESSAGES[403] + (msg ? ` (${msg})` : ''),
    };
  }
  if (httpStatus === 429) {
    return {
      ok: false,
      phase: 'failed',
      errorCode: 'RATE_LIMIT',
      errorMessage: 'Rate limited by WeryAI API. Please wait and try again.',
    };
  }
  if (httpStatus >= 500) {
    return {
      ok: false,
      phase: 'failed',
      errorCode: '500',
      errorMessage: `${ERROR_MESSAGES[500]} (HTTP ${httpStatus})`,
    };
  }
  if (httpStatus === 400) {
    return {
      ok: false,
      phase: 'failed',
      errorCode: '400',
      errorMessage: ERROR_MESSAGES[400] + (msg ? ` (${msg})` : ''),
    };
  }

  const friendly = ERROR_MESSAGES[code] || '';
  const message =
    friendly && msg
      ? `${friendly} (${msg})`
      : friendly || msg || `API error (status: ${code}, HTTP ${httpStatus})`;

  return {
    ok: false,
    phase: 'failed',
    errorCode: code != null ? String(code) : null,
    errorMessage: message,
  };
}

function detectMode(params) {
  if (Array.isArray(params.images) && params.images.length > 0) return 'multi_image';
  if (typeof params.image === 'string' && params.image) return 'image';
  return 'text';
}

function coerceBool(v, defaultVal = false) {
  if (v == null) return defaultVal;
  if (typeof v === 'boolean') return v;
  return String(v).toLowerCase() === 'true';
}

function buildRequestBody(params) {
  const model = params.model || DEFAULT_MODEL;
  const body = {
    prompt: params.prompt,
    model,
    duration: Number(params.duration) || 5,
  };
  if (params.aspect_ratio) body.aspect_ratio = params.aspect_ratio;
  if (params.resolution) body.resolution = params.resolution;
  if (params.generate_audio != null) body.generate_audio = coerceBool(params.generate_audio, false);
  if (params.negative_prompt) body.negative_prompt = params.negative_prompt;
  if (params.image) body.image = params.image;
  if (params.images) body.images = params.images.slice(0, 3);
  return body;
}

async function submitTask(params, apiKey) {
  const mode = detectMode(params);
  const pathMap = {
    text: '/v1/generation/text-to-video',
    image: '/v1/generation/image-to-video',
    multi_image: '/v1/generation/multi-image-to-video',
  };
  const body = buildRequestBody(params);
  let res;
  try {
    res = await apiRequest('POST', pathMap[mode], body, apiKey);
  } catch (err) {
    return {
      ok: false,
      phase: 'failed',
      errorCode: 'NETWORK_ERROR',
      errorMessage: err.message || String(err),
    };
  }

  if (!isApiSuccess(res)) return formatApiError(res);

  const data = res.data || {};
  const taskIds = data.task_ids ?? (data.task_id ? [data.task_id] : []);
  return {
    ok: true,
    phase: 'submitted',
    batchId: data.batch_id ?? null,
    taskIds,
    taskId: taskIds[0] ?? null,
    taskStatus: null,
    videos: null,
    errorCode: null,
    errorMessage: null,
  };
}

function extractVideos(taskData) {
  const tr = taskData.task_result || {};
  const raw = tr.videos || taskData.videos || [];
  return raw.map((v) => {
    if (typeof v === 'string') {
      return { url: v, cover_url: '' };
    }
    return {
      url: v?.url || v?.video_url || '',
      cover_url: v?.cover_image_url || v?.cover_url || '',
    };
  });
}

async function pollUntilDone(taskId, batchId, taskIds, apiKey) {
  const start = Date.now();
  while (true) {
    const elapsed = (Date.now() - start) / 1000;
    if (elapsed * 1000 >= POLL_TIMEOUT_MS) {
      return {
        ok: false,
        phase: 'failed',
        errorCode: 'TIMEOUT',
        errorMessage: `Poll timeout after ${Math.floor(elapsed)}s.`,
        batchId,
        taskIds,
        taskId,
        taskStatus: 'unknown',
        videos: null,
      };
    }

    await sleep(POLL_INTERVAL_MS);

    let res;
    try {
      res = await apiRequest('GET', `/v1/generation/${taskId}/status`, null, apiKey);
    } catch (err) {
      log(`Warning: poll failed (${err.message}), retrying...`);
      continue;
    }

    if (!isApiSuccess(res)) {
      log('Warning: poll returned non-success status, retrying...');
      continue;
    }

    const taskData = res.data || {};
    const rawStatus = taskData.task_status || '';
    const normalized = STATUS_MAP[rawStatus] || 'unknown';
    const elapsedSec = Math.floor((Date.now() - start) / 1000);
    log(`Polling ${taskId}... status: ${rawStatus} (${elapsedSec}s elapsed)`);

    if (normalized === 'completed') {
      const videos = extractVideos(taskData);
      return {
        ok: true,
        phase: 'completed',
        batchId,
        taskIds,
        taskId,
        taskStatus: rawStatus,
        videos: videos.length ? videos : null,
        errorCode: null,
        errorMessage: null,
      };
    }

    if (normalized === 'failed') {
      const tr = taskData.task_result || {};
      const errMsg = tr.message || taskData.msg || 'Task failed';
      return {
        ok: false,
        phase: 'failed',
        batchId,
        taskIds,
        taskId,
        taskStatus: rawStatus,
        videos: null,
        errorCode: 'TASK_FAILED',
        errorMessage: errMsg,
      };
    }
  }
}

async function cmdWait(params, apiKey) {
  const submitResult = await submitTask(params, apiKey);
  if (!submitResult.ok) return submitResult;
  const taskId = submitResult.taskId;
  const batchId = submitResult.batchId;
  const taskIds = submitResult.taskIds || [taskId];
  log(`Submitted task ${taskId}. Polling for completion...`);
  return pollUntilDone(taskId, batchId, taskIds, apiKey);
}

async function cmdStatus(taskId, apiKey) {
  let res;
  try {
    res = await apiRequest('GET', `/v1/generation/${taskId}/status`, null, apiKey);
  } catch (err) {
    return {
      ok: false,
      phase: 'failed',
      errorCode: 'NETWORK_ERROR',
      errorMessage: err.message || String(err),
    };
  }

  if (!isApiSuccess(res)) return formatApiError(res);

  const taskData = res.data || {};
  const rawStatus = taskData.task_status || '';
  const normalized = STATUS_MAP[rawStatus] || 'unknown';
  const videos = extractVideos(taskData);
  const phase =
    normalized === 'completed' ? 'completed' : normalized === 'failed' ? 'failed' : 'running';
  const tr = taskData.task_result || {};
  return {
    ok: phase !== 'failed',
    phase,
    batchId: null,
    taskIds: [taskId],
    taskId,
    taskStatus: rawStatus,
    videos: videos.length ? videos : null,
    errorCode: phase === 'failed' ? 'TASK_FAILED' : null,
    errorMessage: phase === 'failed' ? tr.message || taskData.msg || null : null,
  };
}

const VALID_MODEL_MODES = ['text_to_video', 'image_to_video', 'multi_image_to_video'];

async function cmdModels(modeFilter, apiKey) {
  let res;
  try {
    res = await fetchModelsRegistry(apiKey);
  } catch (err) {
    return {
      ok: false,
      phase: 'failed',
      errorCode: 'NETWORK_ERROR',
      errorMessage: err.message || String(err),
    };
  }

  if (!isApiSuccess(res)) return formatApiError(res);

  const data = res.data || {};
  const out = { ok: true, phase: 'completed' };

  if (modeFilter) {
    if (!VALID_MODEL_MODES.includes(modeFilter)) {
      return {
        ok: false,
        phase: 'failed',
        errorCode: 'VALIDATION',
        errorMessage: `Invalid --mode. Use: ${VALID_MODEL_MODES.join(', ')}`,
      };
    }
    out[modeFilter] = data[modeFilter] || [];
  } else {
    out.text_to_video = data.text_to_video || [];
    out.image_to_video = data.image_to_video || [];
    out.multi_image_to_video = data.multi_image_to_video || [];
  }

  return out;
}

function parseArgs(argv) {
  const cmd = argv[0];
  let jsonStr = null;
  let taskId = null;
  let dryRun = false;
  let modelsMode = null;
  for (let i = 1; i < argv.length; i++) {
    if (argv[i] === '--json') jsonStr = argv[++i];
    else if (argv[i] === '--task-id') taskId = argv[++i];
    else if (argv[i] === '--dry-run') dryRun = true;
    else if (argv[i] === '--mode') modelsMode = argv[++i];
  }
  return { command: cmd, jsonStr, taskId, dryRun, modelsMode };
}

function printJson(obj) {
  process.stdout.write(`${JSON.stringify(obj, null, 2)}\n`);
}

async function main() {
  const argv = process.argv.slice(2);
  const valid = new Set([
    'wait',
    'submit-text',
    'submit-image',
    'submit-multi-image',
    'status',
    'models',
  ]);
  const { command, jsonStr, taskId: cliTaskId, dryRun, modelsMode } = parseArgs(argv);

  if (!command || !valid.has(command)) {
    printJson({
      ok: false,
      phase: 'failed',
      errorCode: 'USAGE',
      errorMessage:
        'Usage: node video_gen.js <wait|submit-text|submit-image|submit-multi-image|status|models> [--json \'...\'] [--task-id id] [--dry-run] [--mode text_to_video|image_to_video|multi_image_to_video]',
    });
    process.exit(1);
  }

  let params = {};
  if (jsonStr) {
    try {
      params = JSON.parse(jsonStr);
    } catch (e) {
      printJson({
        ok: false,
        phase: 'failed',
        errorCode: 'INVALID_JSON',
        errorMessage: `Invalid JSON: ${e.message}`,
      });
      process.exit(1);
    }
  }

  if (command === 'models') {
    const [REDACTED_SECRET] || '').trim();
    if (!apiKey) {
      printJson({
        ok: false,
        phase: 'failed',
        errorCode: 'NO_API_KEY',
        errorMessage: 'Missing WERYAI_API_KEY environment variable.',
      });
      process.exit(1);
    }
    const result = await cmdModels(modelsMode, apiKey);
    printJson(result);
    process.exit(result.ok ? 0 : 1);
  }

  if (dryRun) {
    const mode = detectMode(params);
    const pathMap = {
      text: '/v1/generation/text-to-video',
      image: '/v1/generation/image-to-video',
      multi_image: '/v1/generation/multi-image-to-video',
    };
    const body = 'prompt' in params ? buildRequestBody(params) : {};
    printJson({
      ok: true,
      phase: 'dry-run',
      dryRun: true,
      requestBody: body,
      requestUrl: BASE_URL + (pathMap[mode] || pathMap.text),
    });
    process.exit(0);
  }

  const [REDACTED_SECRET] || '').trim();
  if (!apiKey) {
    printJson({
      ok: false,
      phase: 'failed',
      errorCode: 'NO_API_KEY',
      errorMessage: 'Missing WERYAI_API_KEY environment variable.',
    });
    process.exit(1);
  }

  let result;
  if (command === 'status') {
    const tid = cliTaskId || params.task_id || params.taskId;
    if (!tid) {
      printJson({
        ok: false,
        phase: 'failed',
        errorCode: 'MISSING_PARAM',
        errorMessage: 'status command requires --task-id <id>',
      });
      process.exit(1);
    }
    result = await cmdStatus(tid, apiKey);
  } else {
    if (!params.prompt) {
      printJson({
        ok: false,
        phase: 'failed',
        errorCode: 'MISSING_PARAM',
        errorMessage: "'prompt' is required in JSON parameters.",
      });
      process.exit(1);
    }
    result = command === 'wait' ? await cmdWait(params, apiKey) : await submitTask(params, apiKey);
  }

  printJson(result);
  process.exit(result.ok ? 0 : 1);
}

main().catch((err) => {
  printJson({
    ok: false,
    phase: 'failed',
    errorCode: 'FATAL',
    errorMessage: err.message || String(err),
  });
  process.exit(1);
});


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)