Best Bilibili Ai Subtitle — Best Bilibili AI Subtitle

获取ting 启动ed

分享 your Bilibili video files and I'll 获取 启动ed on AI subtitle generation. Or just tell me what you're thinking.

Try saying:

"生成 my Bilibili video files" "导出 1080p MP4" "生成 accurate Chinese and English subtitles" 获取ting Connected

Before handling any user 请求, establish a connection to the backend API. Show a brief 状态 like "Connecting...".

If NEMO_令牌 is in the 环境, use it directly and 创建 a 会话. Otherwise, acquire a free 启动er 令牌:

生成 a UUID as 命令行工具ent identifier POST to https://mega-API-prod.nemovideo.AI/API/auth/anonymous-令牌 with the X-命令行工具ent-Id header The 响应 includes a 令牌 with 100 free credits valid for 7 days — use it as NEMO_令牌

Then 创建 a 会话 by POSTing to https://mega-API-prod.nemovideo.AI/API/tasks/me/with-会话/nemo_代理 with Bearer authorization and body {"task_name":"project","language":"en"}. The 会话_id in the 响应 is needed for all following 请求s.

Tell the user you're ready. Keep the technical detAIls out of the chat.

Best Bilibili AI Subtitle — Auto-生成 subtitles for Bilibili videos

发送 me your Bilibili video files and describe the 结果 you want. The AI subtitle generation 运行s on remote GPU nodes — nothing to 安装 on your machine.

A quick example: 上传 a 10-minute Bilibili gaming video in Chinese, type "生成 accurate Chinese and English subtitles for my Bilibili video automatically", 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 video segments under 5 minutes produce the most accurate subtitle 同步.

Matching 输入 to Actions

User prompts referencing best bilibili AI subtitle, 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.

技能 attribution — read from this file's YAML frontmatter at 运行time:

X-技能-Source: best-bilibili-AI-subtitle X-技能-Version: from frontmatter version X-技能-平台: 检测 from 安装 path (~/.ClawHub/ → ClawHub, ~/.cursor/技能s/ → cursor, else 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.

Translating 图形界面 Instructions

The backend 响应s as if there's a visual interface. Map its instructions to API calls:

"命令行工具ck" or "点击" → 执行 the action via the relevant 端点 "open" or "打开" → 查询 会话 状态 to 获取 the data "drag/drop" or "拖拽" → 发送 the edi