Meitu Visual Me

Meitu Visual Me

> Always respond in the user's language.

> Data Disclaimer: > - Local reads: Meitu Visual Me may read local files such as MEMORY.md, USER.md, and ./visual/PROFILE.md for personalized generation. These files remain stored locally, but excerpts, summaries, or derived details may be incorporated into generated prompts. > - Uploaded to meitu API: Images you provide (photos, reference images), generated prompts, and any local profile/memory/project details included in those prompts are sent to the Meitu OpenAPI for processing, subject to the Meitu privacy policy. > - First-use authorization: On first use, the skill will explain the above data usage, including possible prompt inclusion of local profile/memory/project context. You can choose not to provide photos or not to use memory/profile context.

Overview

Memory-driven AI visual assistant. Reads user profiles and daily memories to generate personalized images and videos. Supports 17 scenario workflows and 28 trending styles. Play first, give feedback, and the system automatically learns your preferences.

仅在用户明确要求使用这类个性化视觉工作流时执行；不要把泛化的“任何视觉内容创作需求”都路由到本 Skill。首次使用或涉及 profile/memory 内容时，应先说明哪些本地资料会被读取，以及哪些摘要可能进入发往 Meitu OpenAPI 的 prompt。

| You say | What it does | |------|------| | "帮我画" (draw something) | Automatically selects a scenario based on your current state and generates | | Send a selfie + "换个赛博背景" (swap to cyber background) | Swaps the background, instant result | | "头像系列" (avatar series) | Generates multiple style avatars from one face | | "帮我做张 ID 卡" (make me an ID card) | Four trendy styles to choose from, exclusive collectible card | | "微缩场景" (miniature scene) | Turns your current state into a toy miniature world | | "一键换风格" (one-click style remix) | One image, multiple style variants | | "今日卡" (daily card) | City + weather + mood, generates a daily info card | | "帮我试穿这件" (try this on) | Send a clothing image for virtual try-on | | "把这张图动起来" (bring this image to life) | Turns an image into a short video | | "合个影" (take a photo together) | A photo with you and OpenClaw together |

Dependencies

• tools: meitu-cli (npm install -g meitu-cli@latest)
• credentials: prefer environment variables or a pre-provisioned ~/.meitu/credentials.json; if the operator explicitly wants persistent local setup, run meitu config set-ak --value + meitu config set-sk --value
• user knowledge (optional): ./visual/ (if absent, knowledge reads are skipped until the user chooses to start a local visual workspace; reference photos and profile/memory files are then stored under ./visual/)
• project files: ./ (cwd, containing openclaw.yaml, DESIGN.md, etc.)
• platform context (optional): USER.md, MEMORY.md, memory/今日.md, SOUL.md, IDENTITY.md (platform-injected; skip if absent)
• user config (optional): $VISUAL/config/defaults.yaml

> Path alias: In the text below, $VISUAL = {OPENCLAW_HOME}/workspace/visual/. When ./visual/ exists, it points to that directory; on the OpenClaw platform it auto-resolves to ~/.openclaw/workspace/visual/.

Platform Setup

OpenClaw: User data is located at ~/.openclaw/workspace/visual/. The platform injects it automatically; ./visual/ resolves to that path at runtime.

Claude Code / Other Agents: User data is located at ./visual/ under the current working directory. Only create or update this local workspace when the user continues with profile-based personalization or long-lived local memory; otherwise skip knowledge writes.

> All ./visual/ paths in the workflows below are resolved to the corresponding actual path by each platform at runtime.

Preflight

Must pass before every generation; if it fails, stop generation:

1. meitu --version — if not installed, prompt: npm install -g meitu-cli@latest
2. Verify credentials: meitu auth verify --json — if credentials are invalid, this will return an auth error. For credential setup, read [references/setup.md](references/setup.md)
3. Check whether the ./visual/ directory exists (if not, prompt first-time user; do not block)
4. Did the user send an image or a video? — Videos are not supported; prompt user to send a screenshot
5. One-time workspace resolution (results persist throughout the workflow):

Detect mode: cwd has openclaw.yaml → project mode; else → one-off 检查 ./visual/ 目录 → 确定 capabilities

1. Minimization rule: only read or persist the profile, memory, or project fields needed for the current request. Do not create or update long-lived memory/profile records for name, gender, or identity details unless the user explicitly wants that personalization behavior.

Variables available after resolution (referenced directly in subsequent steps): `` mode = "project" (cwd has openclaw.yaml) | "one-off" visual = ./visual/ path (value if exists, otherwise null) can_read_knowledge = visual exists can_record = mode == "project" AND visual exists output_dir = Resolve output_dir: openclaw.yaml → "./output/" | else → "$VISUAL/output/meitu-visual-me/" ` mkdir -p {outputdir}` Hard constraint: outputdir MUST NOT point to inside the skill folder

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Core Workflow

Preflight → [Context] → Execute → [Refine] → Deliver → [Record]
              ↑ creative tasks run this       ↑ creative     ↑ project mode
              ↑ tool tasks skip               ↑ tool skip    ↑ one-off skip

Context

Part 1: Routing

Route first, read later — match the workflow first, then decide what to read and how to proceed.

First: Match Workflow

Compare the user's input against the trigger words in the Workflows section below to match a specific workflow. Read [references/workflows.md](references/workflows.md) for scenario details.

When no match is found, there are three cases:

• Clear description (e.g., "帮我画一只猫在月球上") — no workflow match needed; determine the style per prompt building rules, then generate directly with text-to-image
• Only sent an image without text — recommend 4 suitable options based on image content (e.g., swap background, style remix, make ID card, image-to-video), let user choose
• Vague instruction (e.g., "帮我画", "帮我生张图") — recommend scenarios:

1. Check ./visual/memory/global.md for preferred scenarios — if found, prioritize those
2. No memory — pick 4 from the 17 workflows based on time of day and context, let user choose

Second: Determine Read Level and Style Handling

| Workflow type | Read level | Style handling | Included workflows | |-------------|---------|---------|--------------| | Scenario generation | Standard read | Follow style rules (use memory if available, otherwise offer 4 options) | persona-diorama, daily-card, morning-grid, ootd, data-diorama, emotion-grid, relationship-board, memory-collage, real-toon | | Guided generation | Standard read | Workflow includes built-in style selection step (e.g., ID card pick 1 of 4) | id-card, style-remix, avatar-series | | Background swap | Light read | No style selection needed (scene described by user) | swap-bg | | Tool operation | Zero read | No style involved, execute directly | beauty, virtual-tryon | | Video | Light read | No style involved | to-video, motion-transfer |

For image generation, read [references/style-library.md](references/style-library.md) for style keywords and auto-matching rules.

Third: Determine CLI Command

Each workflow's corresponding CLI command is listed in the Workflows section table. For detailed command parameters, read [references/models.md](references/models.md). image-edit requires selecting a sub-model: praline_pro (general editing) / gummy_pro (portrait/pet photography, hairstyle adjustment); stylization defaults to image-style-transfer, and only explicit high-quality editing paths should use image-edit --model nougat.

Routing decision output (passed from Context to Execute):

{
  "workflow": "persona-diorama",
  "command": "text-to-image",
  "read_level": "standard",
  "has_reference_image": true,
  "style": "微缩世界",
  "style_resolved": true
}

Part 2: Load Context

The read level is determined by the routing table above. Zero-read and light-read workflows do not need this step.

Standard read checklist (highest to lowest priority):

1. ./DESIGN.md — project context (if present, prioritize it; only supplement what it doesn't cover)
2. ./openclaw.yaml — resolve short-name references (e.g., brand: "acme" → ./visual/assets/brands/acme/)
3. ./visual/rules/quality.yaml — forbidden styles
4. ./visual/PROFILE.md — visual identity
5. ./visual/memory/global.md (first 30 lines) — preferences
6. Platform context (read if available, skip if not): USER.md, MEMORY.md, memory/今日.md, SOUL.md, Weather API

Use whatever you find; never fabricate user experiences.

Part 3: Build Prompt

Formula: [composition/camera angle] + [subject] + [action/expression] + [scene/background] + [style reference] + [lighting] + [user-specific details]

Hard rules:

1. Pure narrative style; keyword stacking is forbidden

• ❌ woman, dress, red background, fashion, 8K, masterpiece
• ✅ A young woman in a tailored brown dress, posing with confidence against a cherry red backdrop, fashion editorial feel with warm film grain

1. When a reference image is provided, explicitly state its purpose in the prompt (preserve facial likeness / reference composition / reference style); don't just attach an image without explanation

1. Embed style keywords into the narrative — pick 1-2 and weave them naturally into sentences; stacking at the end is forbidden

1. When user context is available, prioritize embedding personal details; when unavailable, generate directly based on user description without blocking.

1. Every generated image must have a clear style direction. Style determination priority:

• User specified a style this time → use it directly
• User didn't specify, but ./visual/memory/global.md has a recorded preferred style → use the memorized style directly without asking again
• User didn't specify, and there's no memory → based on the day's state and scenario, pick 4 suitable styles from [references/style-library.md](references/style-library.md)'s style matching guide, let user choose before generating

Never generate a "generic" image without a style direction. If the user consistently picks the same style, remember it through the observation pipeline in the Record phase, and use it directly next time.

Example 1 (persona-diorama — from memory/今日.md, the user completed an important delivery today): > An isometric miniature workspace on a wooden desk. A tiny figure surrounded by [items extracted from memory]. A celebratory confetti cannon mid-burst on the desk corner. Warm afternoon light through a window, the whole scene feels like a lovingly crafted toy shop diorama.

Example 2 (swap-bg — user sent a selfie and said "换成东京街头"): > Keep the person exactly the same, change only the background to a vibrant Tokyo street scene with neon signs, busy crosswalks, and the warm glow of shop fronts lining both sides of the road, evening atmosphere with soft city lights

Example 3 (ID card — with reference image, user chose Cyber Neon style): > FACE LIKENESS IS THE #1 PRIORITY. Preserve exact facial structure. A young man with short black hair, black-framed glasses, and an oversized hoodie. Apply 3D collectible figure rendering LIGHTLY — keep proportions close to real. Generate a vertical ID CARD in Cyber Neon style: brushed carbon fiber base, neon cyan and magenta glow border, monospaced terminal font. Info area: "小明" / "独立开发者" / "杭州" — EST. 2000. Side strip: "卫衣收集者". Slight tilt, studio-lit product shot feel.

Execute

CLI command decision table:

| Task type | Command | Key parameters | Notes | |---------|------|---------|------| | Text-to-image | meitu text-to-image | --prompt --size --ratio | No reference image | | Generation with reference image | meitu text-to-image | --image_list --prompt --size | Stylization / group photo | | Background swap / content editing | meitu image-edit | --image_list --prompt --model | See model selection table | | Face swap | meitu image-face-swap | --head_image_url --sence_image_url --prompt | Avatar series | | Beauty enhance | meitu image-edit --model gummy_pro | --image_list --prompt --model gummy_pro | Single-person portrait retouch | | Image-to-video | meitu image-to-video | --image_list --prompt --video_duration | Async task | | Motion transfer | meitu video-motion-transfer | --image_list --reference_video_list --prompt | Async task | | Virtual try-on | meitu image-outfit-swap | --image_url --prompt [--clothes_image_url] | |

image-edit model selection:

| Priority | Model | Use case | Output style | |--------|-------|---------|---------| | 1 | text-to-image --image_list | Stylization: cartoon, 3D figure, anime, sketch, artistic recreation | Artistic (NOT realistic face) | | 2 | gummypro | Portrait/pet photography, hairstyle adjustment | Realistic portrait | | 3 | pralinepro (default) | Everything else: text manipulation, background swap, color grading, add/remove elements, multi-image fusion | General editing |

> Quick rule: "变画风" (style change, output doesn't look like real person) → text-to-image --image_list; "拍写真/换发型" (portrait photo) → gummy_pro; "改内容" (modify content) → praline_pro

Reference image routing:

| Intent mode | Reference image handling | Command | |---------|-----------|------| | Stylization (photo → figurine/anime/etc.) | Pass via --image_list, prompt describes target style | text-to-image --image_list | | Background swap | Pass via --image_list, prompt explicitly says "keep person unchanged" | image-edit --model praline_pro | | Group photo (multiple references) | Pass multiple via --imagelist, prompt describes group photo scene | text-to-image --image_list img1 img2 | | Analyze reference only (not passed to tool) | Extract composition/style info into prompt | text-to-image (pure text-to-image) | | Face swap | Pass separately via --headimageurl and --senceimage_url | image-face-swap |

Output directory: Use output_dir resolved in Preflight. Ensure the directory exists (mkdir -p). Add --json --download-dir {output_dir} to all meitu commands.

Result field references:

• With --download-dir → use downloaded_files[0].saved_path for local path (media_urls also available but local path is more reliable)
• Without --download-dir → use media_urls[0] for result URL
• Error response → first read CLI raw fields code, hint, error_name, and action_url; when presenting the failure to the user, map them to the Agent-enhanced fields error_type, user_hint, next_action, and action_link using the meitu-tools contract

Image input: Pass user-provided images via URL or local path. When a reference image is needed, check ./visual/assets/references/user.jpg.

Command examples:

# Text-to-image
meitu text-to-image --prompt "..." --size 2K --ratio 1:1 --json --download-dir {output_dir} --skill_name skill_meitu-visual-me

# Image editing
meitu image-edit --image_list  --prompt "..." --model praline_pro --json --download-dir {output_dir} --skill_name skill_meitu-visual-me

# Image-to-video (async)
meitu image-to-video --image_list  --prompt "..." --video_duration 5 --json --skill_name skill_meitu-visual-me

Error degradation (try each level in order):

| Level | Action | Example | |------|------|------| | L1 | Remove low-priority modifiers | Drop lighting/material descriptions, keep subject + scene + style | | L2 | Downgrade enum parameters | --size 4K → --size 2K (text-to-image); --size 2K → --size 1K (image-poster-generate); --ratio 9:16 → `--r

…

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: meitu
• Source: meitu/meitu-skills
• License: MIT

Install and usage instructions live in the source repository linked above.