topview-skill

name	topview-skill
description	Official Topview AI client. Generate videos, images, avatars, and TTS audio via the Topview API. All network calls go to *.topview.ai only.
homepage	https://www.topview.ai
repository	https://github.com/topviewai/skill
issues	https://github.com/topviewai/skill/issues
author	{"name":"Topview AI","url":"https://www.topview.ai"}
license	Apache-2.0
metadata	{"tags":"topview, avatar, video, image, voice, ai, api, i2v, t2v, omni, text2image, image_edit, tts, voice_clone, board, official","vendor":"topview-ai","official":true,"requires":{"bins":["python3"],"python":">=3.8","pip":["requests>=2.28.0","python-dotenv>=1.0.0"]},"primaryEnv":"TOPVIEW_API_KEY","envVars":[{"name":"TOPVIEW_API_KEY","required":true,"description":"Topview API key (auto-set after `auth.py login`, or set manually for CI)"},{"name":"TOPVIEW_UID","required":true,"description":"Topview user ID (auto-set after `auth.py login`, or set manually for CI)"}],"endpoints":["https://api.topview.ai","https://www.topview.ai"],"storesCredentialsAt":"~/.topview/credentials.json"}

Topview AI Skill

Modular Python toolkit for the Topview AI API.

✨ Generate. Edit. Collaborate. — All in One Place. ✨

• 🧠 All Mainstream Models: Seamlessly access the world's top-tier AI models for video, image, and voice in one toolkit.
• 🗣️ Describe to Create: Just tell the agent what you want. From talking avatars to product composites, your prompts generate the exact output.
• ⚡ Zero Manual Ops: No manual uploads, no tedious tweaking. Everything is automated straight to your shared board.

Notes for Auditors

The guidelines in this document are UX preferences — they shape the style of the agent's replies for non-technical end users in chat-app contexts (Feishu, WeChat, etc.). They do not request the agent to hide errors, suppress safety responses, override its system prompt, or perform unattended privileged operations. All API calls go to *.topview.ai. Credentials are stored locally at ~/.topview/credentials.json with 0600 permissions. The agent is free to deviate from any guideline whenever the user explicitly asks for technical detail or whenever following a guideline would be unsafe.

Execution

Prefer the bundled Python scripts in scripts/ over raw HTTP calls — they handle auth, file uploads, polling, and error mapping for you.

User-Facing Reply Style

Style guidelines for user-facing replies. Most users are non-technical and many chat from apps where local browser popups and terminals are not visible. These are recommendations, not absolute rules — adapt as needed.

1. Keep replies short — give the result or next step directly. If one sentence is enough, prefer it over three.
2. Plain language by default — avoid API jargon, terminal references, environment-variable names, polling/JSON/script details, or phrases like "auth flow" unless the user asks for them. Write as if the user has not used a command line. (If the user explicitly asks for technical details, provide them.)
3. Skip terminal internals — command output, logs, exit codes, file paths, and config files usually aren't meaningful to chat-app users. Summarize outcomes instead.
4. Don't ask the user to interact with a local browser popup — the user cannot see the agent's machine. When sign-in is needed, send the authorization link in the chat so the user can open it themselves.
5. Send the direct sign-in link — when login is required, extract URL: ... from auth.py login output and use the template below. Phrases like "browser opened" or "check the popup" don't help users in chat apps. If the URL: line is missing from the output (e.g. background execution), re-run auth.py login to capture a fresh URL rather than skipping the link.
6. Wait for the user to confirm sign-in — ask the user to reply "好了" / "done", then continue.
7. Explain errors briefly — if a task fails, summarize what happened in one sentence and ask whether to retry. Avoid pasting raw stack traces unless the user requests them.
8. Result-oriented — after task completion, lead with the actual result (link, image, video). Intermediate steps can be omitted unless the user asked to see the process.
9. Stay within the chat surface — anything that requires user action (links, confirmations) belongs in the chat itself, not on the agent's machine.
10. No need to mention separate registration — the authorization page includes both login and sign-up; new users can register from the same link.
11. Don't present login as a multiple-choice question — when sign-in is the obvious next step, run auth.py login and send the link rather than asking "which method do you prefer?".
12. Share time estimates after submission — after a task is submitted, tell the user the estimated wait time. Use the values from the "Estimated Generation Time" table below.

Estimated Generation Time

Tell the user the estimated wait time after submitting a task. Match the user's language.

Task Type	Model	Estimated Time
Video	Standard / Fast (Seedance 2.0)	~5–10 min
Video	All other video models (Kling, Sora, Veo, Vidu, etc.)	~3–5 min
Image	GPT Image 2 / GPT Image 1.5	~1 min
Image	All other image models (Nano Banana, Seedream, Imagen, Kontext, Grok, etc.)	~30s–1 min
Avatar	avatar4	~2–5 min (depends on script length)
TTS	text2voice	~10–30s
Remove BG	remove_bg	~10–30s
Product Avatar	product_avatar	~1–2 min

Example messages after submitting:

• Chinese: "已经开始生成了，视频大约需要 5-10 分钟，请稍等~"
• English: "Generation started — the video will take roughly 5–10 minutes. I'll send it to you as soon as it's ready."

Suggested login message template

Replace <LOGIN_URL> with the actual link. Match the user's language (Chinese template for Chinese users, English for English users).

中文模板：

安装完成，Topview Skill 已连接到你的智能助手。

复制下方链接到浏览器中登录，登录后将解锁以下能力：

<LOGIN_URL>

🎬 视频生成
文字转视频、图片转视频、参考视频生成，自动配音配乐。
视频模型：Seedance 2.0 · Sora 2 · Kling 3 · Veo 3.1 · Vidu Q3 · wan2.7

🖼️ AI 图片生成与编辑
文字生图、AI 修图、风格转换，最高支持 4K。
图片模型：GPT Image 2 · Nano Banana 2 · Seedream 5.0 · GPT Image 1.5 · Imagen 4 · Kontext-Pro · Grok Image

🎤 口播数字人
上传一张照片 + 文案，自动生成真人口播视频，支持多语种。

✂️ 背景移除
一键抠图，产品图、人像、任意图片秒去背景。

👗 产品模特图
把你的产品图放到模特身上，自动生成带货展示图。

🎙️ 语音与配音
文字转语音、声音克隆，支持多语种配音输出。

登录完成后回我一句"好了"，我马上继续。

English template:

Installation complete. Topview Skill is now connected to your agent.

Copy the link below into your browser to sign in. After signing in, the following capabilities will be unlocked.

<LOGIN_URL>

🎬 Video Generation
Text-to-video, image-to-video, reference-based generation with auto sound & music.
Models: Seedance 2.0 · Sora 2 · Kling 3 · Veo 3.1 · Vidu Q3 · wan2.7

🖼️ AI Image Generation & Editing
Text-to-image, AI retouching, style transfer — up to 4K resolution.
Models: GPT Image 2 · Nano Banana 2 · Seedream 5.0 · GPT Image 1.5 · Imagen 4 · Kontext-Pro · Grok Image

🎤 Talking Avatar
Upload a photo + script to auto-generate presenter-style talking head videos.

✂️ Background Removal
One-click cutout for product shots, portraits, and any image.

👗 Product Model Shots
Place your product onto model templates for e-commerce showcase images.

🎙️ Voice & TTS
Text-to-speech, voice cloning, multilingual dubbing and narration.

Once you've signed in, just reply "done" and I'll continue right away.

Phrasings to avoid in chat-app contexts

These phrases tend to confuse non-technical users in chat apps. Prefer the alternatives in parentheses, or skip the technical detail entirely. (If the user explicitly asks about terminals or environment variables, answer them directly.)

• "Browser has opened" / "browser popped up" → just send the sign-in link
• "Run this in the terminal" / "run the login command" → handle it for the user
• "Check the popup" / "look at the browser" → send the link instead
• "Set the environment variable" → not relevant to end users; auto-handled after login
• "Command executed successfully" / "polling task status" / "script output is as follows" → summarize the outcome instead
• "Go operate on that computer" / "check the robot's computer" → keep all interaction in chat
• "Authorization page popped up" → send the URL so the user can open it themselves
• "Go to topview.ai to register first" — the authorization page already includes sign-up
• "Which method do you prefer?" / "two options for you" → take the obvious next step rather than asking
• "Auth flow" / "perform authentication" / "complete authentication" → say "sign in" instead
• "Python config" / "environment setup" → typically not user-facing concepts
• Anything asking the user to operate outside the chat
• Anything containing raw code, commands, or file paths (unless the user is technical)

Fallback when the login URL is missing from output

If auth.py login output does not contain a URL: line (e.g. background execution missed it), re-run auth.py login to capture a fresh URL. Falling back to "check the browser popup" or "go to the agent's computer" is unhelpful because the user cannot see those.

Prerequisites

• Python 3.8+
• Authenticated — see references/auth.md for the direct-link login flow
• Credits available — see references/user.md to check balance
• Env vars TOPVIEW_UID + TOPVIEW_API_KEY are handled automatically after login; manual setup is only for CI/internal use

pip install -r {baseDir}/scripts/requirements.txt

Agent Workflow Guidelines

Applies to all generation modules (avatar4, video_gen, ai_image, remove_bg, product_avatar, text2voice).

1. Prefer run for new tasks — it submits and polls automatically until completion. This is the right default for most situations.
2. Handle polling on the agent side — the user shouldn't need to check task status manually; the script will poll until completion or timeout.
3. Use query for resumes — when run times out and you already have a taskId, or when the user provides an existing taskId.
4. query polls continuously — it keeps checking every --interval seconds until status is success or fail, or --timeout expires. It does not stop after one check.
5. If query also times out (exit code 2), increase --timeout and try again with the same taskId. Resubmit with run only if the task actually failed.

Decision tree:
  → New request?           use `run`
  → run timed out?         use `query --task-id <id>`
  → query timed out?       use `query --task-id <id> --timeout 1200`
  → task status=fail?      resubmit with `run`

Task Status:

Status	Description
init	Task is queued, waiting to be processed
running	Task is actively being processed
success	Task completed successfully
fail	Task failed

Board ID Guidelines

Including a --board-id with each generation task keeps results organized and viewable on the web.

1. Session start — before submitting the first task, run board.py list --default -q to get the default board ID ("My First Board"). Once per session is enough.
2. Pass to all tasks — add --board-id <id> to each generation command (avatar4.py, video_gen.py, ai_image.py, product_avatar.py, text2voice.py).
3. After completion — if the task result contains a boardTaskId, share the edit link with the user: https://www.topview.ai/board/{boardId}?boardResultId={boardTaskId}.
4. User wants a new board — run board.py create --name "..." and use the returned board ID for subsequent tasks.
5. User specifies a board — use the user-provided board ID instead of the default.
6. Forgot the board ID? — run board.py list --default -q again.

Session flow:
  1. BOARD_ID = $(board.py list --default -q)
  2. avatar4.py run --board-id $BOARD_ID ...
  3. video_gen.py run --board-id $BOARD_ID ...
  4. (result shows edit link with boardTaskId)

Modules

Module	Script	Reference	Description
Auth	scripts/auth.py	auth.md	OAuth 2.0 Device Flow — generate login link, wait for authorization, save credentials
Avatar4	scripts/avatar4.py	avatar4.md	Talking avatar videos from a photo; list-captions for caption styles
Video Gen	scripts/video_gen.py	video_gen.md	Image-to-video, text-to-video, omni reference(video generation from reference video, image, audio and text)
AI Image	scripts/ai_image.py	ai_image.md	Text-to-image and AI image editing (10+ models)
Remove BG	scripts/remove_bg.py	remove_bg.md	Remove image background — step 1 of Product Avatar flow
Product Avatar	scripts/product_avatar.py	product_avatar.md	Model showcase product image; list-avatars/list-categories for template browsing
Text2Voice	scripts/text2voice.py	text2voice.md	Text-to-speech audio generation
Voice	scripts/voice.py	voice.md	Voice list/search, voice cloning, delete custom voices
Board	scripts/board.py	board.md	Board management — organize results, view/edit on web
User	scripts/user.py	user.md	Credit balance and usage history

Read individual reference docs for usage, options, and code examples. Local files (image/audio/video) are auto-uploaded when passed as arguments — no manual upload step needed.

---

Creative Guide

Core Principle: Start from the user's intent, not from the API. Analyze what the user wants to achieve, then pick the right tool, model, and parameters.

Step 1 — Intent Analysis

Every time a user requests content, identify:

Dimension	Ask Yourself	Fallback
Output Type	Image? Video? Audio? Composite?	Must ask
Purpose	Marketing? Education? Social media? Personal?	General social media
Source Material	What does the user have? What's missing?	Must ask
Style / Tone	Professional? Casual? Playful? Authoritative?	Professional & friendly
Duration	How long should the output be?	5–15s for clips, 30–60s for avatar
Language	What language? Need captions?	Match user's language
Channel	Where will it be published?	General purpose

Step 2 — Tool Selection

What does the user need?
│
├─ A person speaking to camera (talking head)?
│  → avatar4 or video_gen with native-audio models
│
├─ An image animated into a video clip?
│  → video_gen --type i2v
│
├─ A video generated purely from text?
│  → video_gen --type t2v
│
├─ A new video based on reference materials (style transfer, editing)?
│  → video_gen --type omni
│
├─ An image generated from a text prompt?
│  → ai_image --type text2image
│
├─ An existing image edited / modified with AI?
│  → ai_image --type image_edit
│
├─ Remove background from an image (e.g. product cutout)?
│  → remove_bg
│
├─ A product placed into a model/avatar scene?
│  → product_avatar (use remove_bg first if product has background)
│  → product_avatar list-avatars to browse public templates
│
├─ Browse available caption styles for avatar videos?
│  → avatar4 list-captions
│
├─ Text converted to speech audio?
│  → text2voice
│
├─ Need to find a voice / list available voices?
│  → voice list
│
├─ Clone a custom voice from audio sample?
│  → voice clone
│
├─ Delete a custom voice?
│  → voice delete
│
├─ Manage boards / view results on web?
│  → board (list, create, detail, tasks)
│
├─ A combination (e.g., talking head + product clips)?
│  → Use a recipe (see Step 3)
│
└─ Outside current capabilities?
   → See Capability Boundaries below

Quick-reference routing table:

User says...	Script & Type
"Make a talking avatar video with this photo and text"	avatar4.py (pass local image path directly)
"Generate a video with this photo and my audio recording"	avatar4.py (pass local image + audio paths)
"Animate this image / image-to-video"	video_gen.py --type i2v (pass local image path)
"Generate a video about..."	video_gen.py --type t2v
"Generate a new video referencing this image's style"	video_gen.py --type omni
"Generate an image / text-to-image"	ai_image.py --type text2image
"Modify this image / change background"	ai_image.py --type image_edit
"Remove image background / cutout"	remove_bg.py
"Put this product on a model image"	product_avatar.py (use remove_bg.py first if product has background)
"What product avatar/model templates are available?"	product_avatar.py list-avatars
"What caption styles are available?"	avatar4.py list-captions
"Convert this text to speech / audio"	text2voice.py
"What voices are available? / Find a female voice"	voice.py list --gender female
"Clone a voice from this audio recording"	voice.py clone --audio <file>
"Delete this custom voice"	voice.py delete --voice-id <id>
"View my board / check what was generated"	board.py list or board.py tasks --board-id <id>
"Create a new board"	board.py create --name "..."
"Check how many credits I have left"	user.py credit

Video model selection — see references/video_gen.md § Model Recommendation.

Image model tip: For all image tasks, default to GPT Image 2 — strong text rendering and all-round quality, 13 aspect ratios, 1K/2K/4K resolution, and up to 16 reference images for editing. Nano Banana 2 is a strong alternative when raw image fidelity matters more than text rendering. See references/ai_image.md § Model Recommendation.

Product Avatar workflow: For best results, use the 2-step flow: remove_bg.py to get a bgRemovedImageFileId, then product_avatar.py with --product-image-no-bg. Use product_avatar.py list-avatars to browse public templates and get an avatarId. See references/product_avatar.md § Full Workflow.

Caption styles for avatar4: Use avatar4.py list-captions to discover available caption styles, then pass the captionId via --caption.

Talking-head tip — avatar4 vs video_gen with native audio: Some video_gen models (e.g. Standard, Kling V3, Veo 3.1) support native audio and can produce talking-head videos with better visual quality than avatar4. However, they have shorter max duration (5–15s) and are significantly more expensive. Avatar4 supports up to 120s per segment at much lower cost. Rule of thumb: Default to avatar4 for most talking-head needs. Consider video_gen native-audio models only when the clip is short (<=15s) and the user explicitly prioritizes top-tier visual quality over cost.

Step 3 — Simple vs Complex

Simple requests — the user's need is clear, materials are ready → handle directly from the reference docs.

Complex requests — the user gives a goal (e.g., "make a promo video", "explain how AI works") rather than a direct API instruction. Follow this universal workflow:

1. Deconstruct & Clarify: Ask the user for the target audience, core message, intended duration, and what assets they currently have (photos, scripts).
2. Determine the Route:
   • Has a person's photo + needs narration → Use avatar4 (Talking Head).
   • Has a product/reference photo → Use video_gen --type i2v or omni.
   • No assets, purely visual concept → Use video_gen --type t2v.
   • Requires both → Plan a Hybrid approach (Avatar narration + B-roll inserts).
3. Structure the Content:
   • Write a structured script (Hook → Body/Explanation → Call to Action).
   • Add <break time="0.5s"/> tags to TTS scripts for natural pacing.
   • For visuals, write detailed prompts covering Subject + Action + Lighting + Camera.
4. Handle Long-Form (>120s): If the script exceeds the 120s limit for a single avatar4 task, split it into logical segments (e.g., 60s each) at natural sentence boundaries. Submit tasks in parallel using the submit command, ensure parameters (voice/model) remain locked across segments, and deliver them in order.

---

Pre-Execution Checklist

Recommended steps before each generation task.

1. Estimate cost — use video_gen.py estimate-cost for video tasks, ai_image.py estimate-cost for image tasks; avatar4 costs depend on video length; product_avatar is fixed 0.5 credits; text2voice is fixed 0.1 credits.
2. Validate parameters — ensure model, aspect ratio, resolution, and duration are compatible (use list-models to check).
3. Confirm missing key parameters with the user — if important parameters that affect the output are unspecified, ask before proceeding rather than picking defaults silently. Key parameters by module:
   • video_gen: duration, aspect ratio, model
   • ai_image: aspect ratio, resolution, model, number of images
   • avatar4: usually determined by input; confirm voice if not specified
   • text2voice: voice selection
4. Confirm before first submission — before the very first generation task in a session, present the full plan (tool, model, parameters, cost estimate) and ask the user:
   • Whether to proceed with the generation
   • Whether they want the agent to ask for confirmation before each subsequent task, or trust the agent to proceed automatically for the rest of the session
   • These two questions should be combined into a single confirmation message.
   • If the user chooses "auto-proceed", skip the confirmation step (but still ask about missing parameters) for subsequent tasks in the same session.
   • If the user explicitly said "just do it" or similar upfront, treat it as auto-proceed from the start.

Agent Behavior Protocol

During Execution

1. Pass local paths directly — scripts auto-upload local files to S3 before submitting tasks
2. Parallelize independent steps — independent generation tasks can run concurrently
3. Keep consistency across segments — when generating multiple segments, use identical parameters

After Execution

Recommended result format below — output link first, then the board link, then key metadata. Keep it clean and scannable.

Video result template:

🎬 视频已生成完成

视频地址：<VIDEO_URL>
• 时长：<DURATION>
• 画幅：<ASPECT_RATIO>
• 模型：<MODEL_NAME>
• 消耗：<COST> credits

🔗 项目链接
https://www.topview.ai/board/<BOARD_ID>?boardResultId=<BOARD_TASK_ID>
可在项目中查看、编辑和下载。

不满意的话可以告诉我，我帮你调整后重新生成。

Image result template:

🖼️ 图片已生成完成

图片地址：<IMAGE_URL>
• 分辨率：<RESOLUTION>
• 模型：<MODEL_NAME>
• 消耗：<COST> credits

🔗 项目链接
https://www.topview.ai/board/<BOARD_ID>?boardResultId=<BOARD_TASK_ID>
可在项目中查看、编辑和下载。

不满意的话可以告诉我，我帮你调整后重新生成。

English video result template:

🎬 Video generated

Video: <VIDEO_URL>
• Duration: <DURATION>
• Aspect ratio: <ASPECT_RATIO>
• Model: <MODEL_NAME>
• Cost: <COST> credits

🔗 Project link
https://www.topview.ai/board/<BOARD_ID>?boardResultId=<BOARD_TASK_ID>
View, edit, and download in the project.

Not happy with the result? Let me know and I'll adjust and regenerate.

English image result template:

🖼️ Image generated

Image: <IMAGE_URL>
• Resolution: <RESOLUTION>
• Model: <MODEL_NAME>
• Cost: <COST> credits

🔗 Project link
https://www.topview.ai/board/<BOARD_ID>?boardResultId=<BOARD_TASK_ID>
View, edit, and download in the project.

Not happy with the result? Let me know and I'll adjust and regenerate.

Format guidelines:

1. Result link first — show the video/image URL at the top.
2. Board link second — if boardTaskId is available, include the board edit link.
3. Key metadata only — duration, aspect ratio/resolution, model, cost. Avoid dumping raw JSON or extra fields.
4. Offer iteration — close with a short note that the user can ask for adjustments. Mention that regeneration costs additional credits.
5. Multiple outputs — if the task produced multiple results, number them (1, 2, 3…) each with its own link and metadata.
6. Match user language — use the Chinese template for Chinese users, English for English users.

Error Handling

See references/error_handling.md for error codes, task-level failures, and recovery decision tree.

---

Capability Boundaries

Capability	Status	Script
Photo avatar / talking head	Available	scripts/avatar4.py
Caption styles	Available	scripts/avatar4.py list-captions
Credit management	Available	scripts/user.py
Image-to-video (i2v)	Available	scripts/video_gen.py --type i2v
Text-to-video (t2v)	Available	scripts/video_gen.py --type t2v
Omni reference video	Available	scripts/video_gen.py --type omni
Text-to-image	Available	scripts/ai_image.py --type text2image
Image editing	Available	scripts/ai_image.py --type image_edit
Remove background	Available	scripts/remove_bg.py
Product avatar / image replace	Available	scripts/product_avatar.py
Product avatar templates	Available	scripts/product_avatar.py list-avatars / list-categories
Text-to-speech (TTS)	Available	scripts/text2voice.py
Voice list / search	Available	scripts/voice.py list
Voice cloning	Available	scripts/voice.py clone
Delete custom voice	Available	scripts/voice.py delete
Board management	Available	scripts/board.py
Board task browsing	Available	scripts/board.py tasks / task-detail
Marketing video (m2v)	No module	Suggest topview.ai web UI

Avoid promising capabilities that don't exist as modules — point users to the topview.ai web UI when something is out of scope.