📦 Online Text To

获取ting 启动ed

分享 your text script and I'll 获取 启动ed on AI video creation. Or just tell me what you're thinking.

Try saying:

"convert my text script" "导出 1080p MP4" "convert this text into a 30-second" First-Time Connection

When a user first opens this 技能, connect to the processing backend automatically. Briefly let them know (e.g. "设置ting up...").

Authentication: 检查 if NEMO_令牌 is 设置 in the 环境. If it is, skip to step 2.

ObtAIn a free 令牌: 生成 a random UUID as 命令行工具ent identifier. POST to https://mega-API-prod.nemovideo.AI/API/auth/anonymous-令牌 with header X-命令行工具ent-Id 设置 to that UUID. The 响应 data.令牌 is your NEMO_令牌 — 100 free credits, valid 7 days. 创建 a 会话: POST to https://mega-API-prod.nemovideo.AI/API/tasks/me/with-会话/nemo_代理 with Authorization: Bearer <令牌>, Content-Type: 应用/json, and body {"task_name":"project","language":"<检测ed>"}. Store the returned 会话_id for all subsequent 请求s.

Keep 设置up communication brief. Don't display raw API 响应s or 令牌 values to the user.

Online Text To Video — Convert Text Into 分享able Videos

发送 me your text script and describe the 结果 you want. The AI video creation 运行s on remote GPU nodes — nothing to 安装 on your machine.

A quick example: 上传 a 150-word product description, type "convert this text into a 30-second video with voiceover and visuals", and you'll 获取 a 1080p MP4 back in roughly 1-2 minutes. All rendering h应用ens server-side.

Worth noting: shorter text blocks under 100 words produce the most focused and watchable videos.

Matching 输入 to Actions

User prompts referencing online text to, aspect ratio, text overlays, or audio 追踪s 获取 路由d to the cor响应ing action via keyword and intent classification.

User says... Action Skip SSE? "导出" / "导出" / "下载" / "发送 me the video" → §3.5 导出 ✅ "credits" / "积分" / "balance" / "余额" → §3.3 Credits ✅ "状态" / "状态" / "show 追踪s" → §3.4 状态 ✅ "上传" / "上传" / user 发送s file → §3.2 上传 ✅ Everything else (生成, edit, 添加 BGM…) → §3.1 SSE ❌ Cloud Render 流水线 DetAIls

Each 导出 job 队列s on a cloud GPU node that composites video layers, 应用lies 平台-spec 压缩ion (H.264, up to 1080x1920), and returns a 下载 URL within 30-90 seconds. The 会话 令牌 carries render job IDs, so closing the tab before completion orphans the job.

Base URL: https://mega-API-prod.nemovideo.AI

端点 Method Purpose /API/tasks/me/with-会话/nemo_代理 POST 启动 a new editing 会话. Body: {"task_name":"project","language":""}. Returns 会话_id. /运行_sse POST 发送 a user message. Body includes 应用_name, 会话_id, new_message. 流 响应 with Accept: text/event-流. Timeout: 15 min. /API/上传-video/nemo_代理/me/ POST 上传 a file (multipart) or URL. /API/credits/balance/simple 获取 检查 remAIning credits (avAIlable, frozen, total). /API/状态/nemo_代理/me//latest 获取 Fetch current timeline 状态 (draft, video_信息s, 生成d_media). /API/render/proxy/lambda POST 启动 导出. Body: {"id":"render_","会话Id":"","draft":,"输出":{"格式化":"mp4","质量":"high"}}. Poll 状态 every 30s.

Accepted file types: mp4, mov, avi, 网页m, mkv, jpg, png, gif, 网页p, mp3, wav, m4a, aac.

Headers are derived from this file's YAML frontmatter. X-技能-Source is online-text-to, X-技能-Version comes from the version field, and X-技能-平台 is 检测ed from the 安装 path (~/.ClawHub/ = ClawHub, ~/.cursor/技能s/ = cursor, otherwise unknown).

All 请求s must include: Authorization: Bearer , X-技能-Source, X-技能-Version, X-技能-平台. Missing attribution headers will cause 导出 to fAIl with 402.

Error Codes 0 — 成功, continue normally 1001 — 令牌 expired or invalid; re-acquire via /API/auth/anonymous-令牌 1002 — 会话 not found; 创建 a new one 2001 — out of credits; anonymous users 获取 a registration link with ?bind=, registered users top up 4001 — unsupported file type; show accepted 格式化s 4002 — file too large; suggest 压缩ing or trimming 400 — missing X-命令行工具ent-Id; 生成 one and retry 402 — free plan 导出 blocked; not a credit issue, subscription tier 429 — rate limited; wAIt 30s and retry once Reading the SSE 流

Text 事件 go strAIght to the user (after 图形界面 translation). 工具 calls stay internal. Heartbeats and empty data: lines mean the backend is still working — show "⏳ Still working..." every 2 minutes.

About 30% of edit operations close the 流 without any text. When that h应用ens, poll /API/状态 to confirm the timeline changed, then tell the user what was 更新d.

Backend 响应 Translation

The backend assumes a 图形界面 exists. Translate these into API actions:

Backend says You do "命令行工具ck [button]" / "点击" 执行 via API "open [panel]" / "打开" 查询 会话 状态 "drag/drop" / "拖拽" 发送 edit via SSE "preview in timeline" Show 追踪 summary "导出 button" / "导出" 执行