Litmedia

Generate, Edit, Collaborate. Access all mainstream AI models in one toolkit. Simply describe your vision to create videos, images, and avatars—zero manual op...

Security Scan

The skill mostly matches a LitMedia client (video/image/avatar generation) but contains inconsistencies (registry metadata vs runtime SKILL.md), embedded scripts that store OAuth credentials to disk, and a prompt-injection signal in SKILL.md — review origin and code before installing.

Purpose & Capability

Name/description claim a media generation toolkit and the included scripts implement that functionality (video, image, avatar, auth). However the registry metadata at the top reports no required env vars / credentials while SKILL.md declares primaryEnv=LITMEDIA_API_KEY and the code implements an OAuth device flow and saves credentials locally — this metadata mismatch is unexplained and suspicious. Also source/homepage is missing (unknown origin).

Instruction Scope

SKILL.md gives explicit runtime rules that force the agent to run bundled Python scripts (auth.py, etc.), extract the 'URL:' line from auth output, and always send that link to the user while suppressing any terminal/log details. That is consistent with a user-friendly design, but the rules are also prescriptive in ways that could be used to hide execution details or mask failures. The SKILL.md also contains a detected prompt-injection pattern (unicode-control-chars), which increases risk that the instructions are trying to influence agent behavior beyond ordinary guidance.

Install Mechanism

No install spec is provided (instruction-only), which lowers risk of remote code fetching. However the package bundles a sizable Python client (12 scripts + references). Those scripts will be executed on the host (no sandbox implied). There are no external download URLs in the install spec, but executing bundled code still performs network calls to litmedia.ai and writes credentials to disk.

Credentials

SKILL.md and auth.py clearly require/produce credentials (LITMEDIA_API_KEY, LITMEDIA_UID) and save a credentials.json to ~/.LitMedia. The registry metadata provided earlier claimed no required env vars — that's inconsistent. Requesting and storing API credentials is expected for a service client, but the mismatch in declared requirements and the presence of code that writes and reads local credential files is notable and should be confirmed with the publisher.

Persistence & Privilege

always:false (good). The skill will create and read persistent credentials at ~/.LitMedia/credentials.json (auth.py documents saving with 0600 perms) and may call an 'unbind device' endpoint on account switch. This is normal for an API client, but persistent credentials combined with autonomous invocation (disable-model-invocation:false — standard) increases blast radius if the skill or its code is malicious or compromised.

Scan Findings in Context

[unicode-control-chars] unexpected: SKILL.md included unicode control characters flagging possible prompt-injection or obfuscation in the user-facing instructions. This is not expected for a straightforward client README and merits manual inspection of SKILL.md and related files for hidden manipulation.

What to consider before installing

This package looks like a full LitMedia client (auth + upload + generation scripts) but has red flags you should verify before installing: - Verify origin: there is no homepage and the registry metadata claims no required env vars while SKILL.md requires LITMEDIA_API_KEY. Confirm the publisher and prefer an official source (company domain or GitHub repo) before trusting credentials. - Review auth/storage behavior: auth.py implements an OAuth device flow and saves credentials to ~/.LitMedia/credentials.json. If you install, expect the skill to open network connections and store API keys locally. Inspect scripts/shared/client.py and auth.py to confirm endpoints and no unexpected remote hosts. - Inspect hidden characters and the SKILL.md rules: the prompt-injection (unicode control chars) and very prescriptive messaging rules could be benign (UX-focused) but could also be used to influence agent behavior or hide errors. Open SKILL.md in a hex-safe viewer to see hidden characters. - Run in a sandbox first: if you want to test, run the skill in an isolated VM/container and review network traffic and filesystem changes before giving it real account credentials. - If you proceed, create a dedicated LitMedia account/API key with limited charges/credits so any misuse is contained. Prefer revocable keys and monitor usage/credit balance. If you want, I can (a) list the exact files and code locations that read/write credentials and make outbound calls, or (b) highlight lines in auth.py/shared/client.py that you should inspect before trusting the skill.

LitMedia AI Skill

Modular Python toolkit for the LitMedia 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.

Execution Rule

Always use the Python scripts in scripts/. Do NOT use curl or direct HTTP calls.

User-Facing Reply Rules

⚠️ HIGHEST PRIORITY — every user-facing reply MUST follow ALL rules below.

Most users are non-technical. Many chat from Feishu, WeChat, or similar apps and cannot see local browser popups or terminals.

1. Keep replies short — give the result or next step directly. If one sentence is enough, don't write three.
2. Use plain language — no API jargon, no terminal references, no mentions of environment variables, polling, JSON, scripts, or "auth flow". Speak as if the user has never seen a command line.
3. Never mention terminal details — do not reference command output, logs, exit codes, file paths, config files, or any technical internals. These mean nothing to the user.
4. Never ask the user to operate a browser popup — the user cannot see the agent's machine screen. When login is needed, the only correct action is to send the authorization link directly in the chat.
5. Always send the direct login link — extract URL: ... from auth.py login output and use the login template below. Never say "browser opened" or similar. If the URL is not found in the output, re-run auth.py login to get a new link. Never skip sending the link.
6. Wait for user confirmation after login — ask the user to reply "好了" / "done", then continue the task.
7. Handle account switching properly — when switching accounts, use auth.py accountswitch and remind the user to log out of their current LitMedia web account or log in with the new account on the website first. After the switch, wait for user confirmation before proceeding.
8. Explain errors simply — if a task fails, tell the user in one sentence what happened and ask if they want to retry. Never paste error messages or technical details.
9. Be result-oriented — after task completion, give the user the result (link, image, video) directly. Do not describe intermediate steps.
10. Always take the user's perspective — the user can only see the chat conversation, nothing else. Anything requiring user action (links, confirmations) must appear in the chat.
11. Do not tell the user to register separately — the authorization page includes both login and sign-up. New users can register directly on that page. Never say "go to litmedia.ai to register first".
12. Act directly, don't ask which method — when login is needed, just run auth.py login and send the link. Don't ask "which method do you prefer?" or present multiple options. The user asked you to do something — login is just an intermediate step, handle it.
13. Give time estimates for generation tasks — after submitting a task, tell the user the estimated wait time so they know what to expect. Use the estimates 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	image models (Nano Banana, Seedream etc.)	~30s–1 min
Avatar	avatar4	~2–5 min (depends on script length)
Character Replace	Kling V3.0, Seedance 2.0, Wan 2.2	~3–5 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."

Required login message template

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

中文模板：

安装完成，LitMedia 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：Nano Banana · Nano Banana 2 · Nano Banana Pro · Seedream 4.0 · Seedream 4.5 · Seedream 5.0

🧑‍💼 口播数字人
上传一张照片 + 一段音频，自动生成真人口播视频，支持多语种。

✂️ 角色替换(动作模仿)
上传一张角色照片 + 动作视频，视频中的人物替换成图片中的角色或者照片中的人物会模仿视频中的动作

🎙️ 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. LitMedia 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: Nano Banana · Nano Banana 2 · Nano Banana Pro · Seedream 4.0 · Seedream 4.5 · Seedream 5.0

🧑‍💼 Talking Avatar
Upload a photo + audio to auto-generate presenter-style talking head videos.

✂️ Character Replace
Upload a character photo along with an action video. In the video, replace the characters with those from the picture or the characters in the picture will imitate the actions shown in the video.

🎙️ 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.

Banned phrases (including any variations):

• "Browser has opened" / "browser popped up"
• "Run this in the terminal" / "run the login command"
• "Check the popup" / "look at the browser"
• "Set the environment variable"
• "Command executed successfully"
• "Polling task status"
• "Script output is as follows"
• "Go operate on that computer" / "check the robot's computer"
• "Authorization page popped up" / "if the page appeared"
• "Go to litmedia.ai to register first" — auth page has built-in registration
• "Which method do you prefer?" / "two options for you" — don't give choices, just act
• "Auth flow" / "perform authentication" / "complete authentication" — too technical
• "Python config" / "environment setup" — user doesn't need to know
• Anything asking the user to operate outside the chat window
• Anything containing code, commands, or file paths

Fallback when login URL is not captured:

If auth.py login output does not contain a URL: line (e.g. background execution missed the output), re-run auth.py login to get a fresh link. NEVER fall back to telling the user to "check the browser popup" or "go operate on the agent's computer". The user cannot see it.

Prerequisites

• Python 3.8+
• Authenticated — see references/auth.md for the direct-link login flow
  • First-time setup: After installing this skill, run python {baseDir}/scripts/auth.py login
  • Use python {baseDir}/scripts/auth.py status to check current login state
• Credits available — see references/user.md to check balance
• Env vars LITMEDIA_UID + LITMEDIA_API_KEY are handled automatically after login; manual setup is only for CI/internal use

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

Agent Workflow Rules

These rules apply to ALL generation modules (avatar4, video_gen, ai_image, video_mimic).

1. Always start with run — it submits the task and polls automatically until done. This is the default and correct choice in almost all situations.
2. Do NOT ask the user to check the task status themselves. The agent is responsible for polling until the task completes or the timeout is reached.
3. Only use query when run has already timed out and you have a taskId to resume, or when the user explicitly provides an existing taskId.
4. query polls continuously — it keeps checking every --interval seconds until status is completed or failed, 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. Do not resubmit unless the task has 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
working	Task is actively being processed
completed	Task completed successfully
failed	Task failed

MANDATORY Pre-Execution Protocol

CRITICAL: Before EVERY generation task, you MUST follow these steps WITHOUT EXCEPTION.

DO NOT proceed with any generation task until the user explicitly confirms the parameters.

Step 1: Estimate Cost

• Video tasks: Use video_gen.py estimate-cost --model <model> --resolution <res> --duration <dur> --count <count>
• Image tasks: Use ai_image.py estimate-cost
• Avatar4: Cost depends on video length

Step 2: Validate Parameters

Use list-models to ensure model, aspect ratio, resolution, and duration are compatible:

python scripts/video_gen.py list-models --type <t2v|i2v|extend|anim|a2ls>

Modules

Module	Script	Reference	Description
Auth	scripts/auth.py	auth.md	OAuth 2.0 Device Flow — generate login link, wait for authorization, save credentials; supports account switching via accountswitch command
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, video extension, ai animation, lip sync
AI Image	scripts/ai_image.py	ai_image.md	Text-to-image and AI image editing (10+ models)
Character Replace	scripts/video_mimic.py	video_mimic.md	Character Replace in Videos with Scene Consistency using LitMedia Common Task APIs.
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 style (style transfer, editing)?
│  → video_gen --type anim
│
├─ Generate a new video based on the extended version of the original video
│  → video_gen --type extend
│
├─ A new video based on reference image and sound (Lip Sync)?
│  → video_gen --type a2ls
│
├─ An image generated from a text prompt?
│  → ai_image --type text2image
│
├─ An existing image edited / modified with AI?
│  → ai_image --type image_edit
│
├─ Replace the video characters with photo characters
│  → Character Replace
│
├─ Photo characters imitate the ations of the video characters
│  → Character Replace
│
├─ Browse available caption styles for avatar videos?
│  → avatar4 list-captions
│
├─ view user all results?
│  → user logs
│
└─ 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 anim
"Extend the original videoo"	video_gen.py --type extend
"Generate a new video referencing image and sound"	video_gen.py --type a2ls
"Generate an image / text-to-image"	ai_image.py --type text2image
"Modify this image / change background"	ai_image.py --type image_edit
"Character Replace / Action imitation"	video_mimic.py
"What caption styles are available?"	avatar4.py list-captions
"View my creation history / check what was generated"	user.py logs --type image or user.py logs --type video
"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 Nano Banana — strongest all-round model with best quality, 7 aspect ratios, and 14 reference images for editing. See references/ai_image.md § Model Recommendation.

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 anim.
   • 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 Protocol

Follow this before EVERY 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
2. Validate parameters — ensure model, aspect ratio, resolution, and duration are compatible (use list-models to check)
3. Ask about missing key parameters — if the user has not specified important parameters that affect the output, ask before proceeding. Key parameters by module:
   • video_gen: duration, aspect ratio, model
   • ai_image: aspect ratio, resolution, model, number of images
   • avatar4: (usually determined by input, but confirm voice if not specified)
   • Do NOT silently pick defaults for these — always confirm with the user.
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 OSS 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

Use the structured result templates below. The user should see the output link first, 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

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

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

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

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

Rules:

1. Result link first — always show the video/image URL at the very top.
2. Key metadata only — duration, aspect ratio/resolution, model, cost. Don't dump raw JSON or extra fields.
3. Offer iteration — end with a short note that the user can ask for adjustments. Remind that regeneration costs additional credits.
4. Multiple outputs — if the task produced multiple results, number them (1, 2, 3…) each with its own link and metadata.
5. 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
Video Extension (extend)	Available	scripts/video_gen.py --type extend
Animation (anim)	Available	scripts/video_gen.py --type anim
Audio-to-Lip-sync (a2ls)	Available	scripts/video_gen.py --type a2ls
Text-to-image	Available	scripts/ai_image.py --type text2image
Image editing	Available	scripts/ai_image.py --type image_edit
Character Replace	Available	scripts/video_mimic.py
Creation history browsing	Available	scripts/user.py logs --type image or scripts/user.py logs --type video
Marketing video (m2v)	No module	Suggest litmedia.ai web UI

Never promise capabilities that don't exist as modules.