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.

Execute only when the user explicitly requests this kind of personalized visual workflow; do not route a generalized "any visual content creation need" to this Skill. On first use or when profile/memory content is involved, first explain which local materials will be read, and which summaries may enter the prompt sent to the Meitu OpenAPI.

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 <AK> + meitu config set-sk --value <SK>
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:

meitu --version — if not installed, prompt: npm install -g meitu-cli@latest
Verify credentials: meitu auth verify --json — if credentials are invalid, this will return an auth error. For credential setup, read references/setup.md
Check whether the ./visual/ directory exists (if not, prompt first-time user; do not block)
Did the user send an image or a video? — Videos are not supported; prompt user to send a screenshot
One-time workspace resolution (results persist throughout the workflow): Detect mode: cwd has openclaw.yaml → project mode; else → one-off Check ./visual/ directory → determine capabilities
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 {output_dir} Hard constraint: output_dir MUST NOT point to inside the skill folder

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 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 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. 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):

./DESIGN.md — project context (if present, prioritize it; only supplement what it doesn't cover)
./openclaw.yaml — resolve short-name references (e.g., brand: "acme" → ./visual/assets/brands/acme/)
./visual/rules/quality.yaml — forbidden styles
./visual/PROFILE.md — visual identity
./visual/memory/global.md (first 30 lines) — preferences
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:

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
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
Embed style keywords into the narrative — pick 1-2 and weave them naturally into sentences; stacking at the end is forbidden
When user context is available, prioritize embedding personal details; when unavailable, generate directly based on user description without blocking.
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'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	gummy_pro	Portrait/pet photography, hairstyle adjustment	Realistic portrait
3	praline_pro (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 --image_list, 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 --head_image_url and --sence_image_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 <url> --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 <url> --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` → `--ratio 1:1`
L3	Remove optional inputs	Drop reference image, switch to pure text-to-image
L4	Minimize to core elements	Keep only subject + style, remove everything else
L5	Stop and report error	Inform user of the specific error, suggest checking credentials or contacting support

Escalate one level after 2 consecutive failures. For other errors, see references/troubleshooting.md.

Refine (MUST for creative tasks, skip for tool tasks)

Tool tasks (beauty, virtual-tryon, image-superres-enhance) → skip this step and go directly to Deliver.

Iterative refinement loop for creative tasks:

Present result — show the generated image + briefly explain design rationale (why this style/composition/color palette)
Wait for feedback — three possible directions:
- User approves ("好" / "不错") → proceed to Deliver
- User requests modifications ("背景换一下" / "颜色太亮") → step 3
- User rejects entirely ("完全不对" / "重新来") → go back to Execute and regenerate
Adjust and regenerate — modify prompt or parameters based on feedback → re-run CLI command → back to step 1
Iteration cap — recommend at most 3 rounds. After 3 rounds, proactively suggest: adjust the requirement direction, split into sub-tasks, or try a different workflow

Note: Do not generate multiple images consecutively without presenting results. Wait for user feedback after each generation.

Deliver

Files are already in the correct directory (Execute uses Preflight's output_dir); only rename is needed:

File renaming: mv {downloaded} {output_dir}/{YYYY-MM-DD}_{description}.{ext}

Path display rule: When presenting file paths to the user, prefer ~/.openclaw/... format. Some chat platforms (e.g., WeChat Work MEDIA) only recognize ~/ prefix paths; absolute paths will prevent images/files from displaying. Internal processing can use absolute paths.

WeChat delivery: For the WeChat channel (channel=openclaw-weixin), use MEDIA:visual/output/meitu-visual-me/{filename}.jpg (relative path).

After delivery: After delivery, wait for user feedback before the next generation. If user gives style/preference feedback, proceed to Record. When the user says "适配各平台", read references/channel-presets.md.

DESIGN.md maintenance: If DESIGN.md Iteration Log > 5 entries → compact: keep the most recent 5, archive older entries to ./drafts/design-history.md.

Record

can_record = false → skip this entire section. In one-off mode, all feedback is only effective for the current conversation.

Zero-write default — write nothing after generation; only write when user gives active feedback:

User says	What to do
"好" / "喜欢这个风格"	Append observation to `./visual/memory/observations/observations.yaml` (auto, no user confirmation needed). If that observation's `projects` >= 2, mention non-blockingly at end of reply: "By the way, you've preferred X across N projects. Want to save it as a universal preference?" User confirms → write to `memory/global.md` or `memory/scenes/{scene}.md`, delete the observation; user ignores → do nothing
"不要 XX 风格"	Has `openclaw.yaml` → ask: "Only skip XX for this project, or never use XX for all future projects?" Project-only → append to `./DESIGN.md` Constraints; all future → append to `./visual/rules/quality.yaml` (requires user confirmation). No `openclaw.yaml` → applies only to current task, no write
"我是男的" (identity facts)	Write to `./visual/PROFILE.md`
Sends a new reference photo	Save as `./visual/assets/references/user.jpg`
"换个背景重生"	Re-run current task, no write

If the user says nothing, write nothing and don't read observations.yaml (zero overhead).

One-off mode exception: When a user repeatedly expresses the same preference in one-off mode (e.g., says "不要渐变" multiple times), the Agent MAY proactively suggest:

"You've mentioned no gradients several times. Want to add it to the global forbidden list?" → User agrees → write to $VISUAL/rules/quality.yaml (requires confirmation) → User disagrees → effective only for the current conversation

Positive feedback recording path:

Read $VISUAL/memory/observations/observations.yaml (create if it doesn't exist)
Scan for semantically similar keys → merge or create new
Write back to file
If len(projects) >= 2 → propose promotion (non-blocking)

Promotion proposal template:

"You've preferred X across N projects. Want to save it? → Save to {scope_hint} scene [default] → Save to global preferences → Don't save"

If scope_hint is not null → default to scenes/{scope_hint}.md; if null → default to global.md. User confirms → write to target file + delete observation entry. User ignores → do nothing.

For write format details, read references/feedback-loop.md when handling feedback; for observation lifecycle and classification, read references/memory-protocol.md

First-Time User Guide

When the ./visual/ directory doesn't exist, this indicates a new user. Read references/first-time-guide.md to execute the onboarding flow.

Workflows (17)

For all workflow details, read references/workflows.md

Generation

Trigger words	Workflow	Command
微缩场景 (miniature scene)、个人场景 (personal scene)	persona-diorama	`text-to-image`
今日卡 (daily card)、城市打卡 (city check-in)	daily-card	`text-to-image`
早安 (good morning)、晨间四宫格 (morning grid)	morning-grid	`text-to-image`
OOTD、今天穿什么 (what to wear today)	ootd	`text-to-image`
数据场景 (data scene)、今日数据 (today's data)	data-diorama	`text-to-image`
九宫格 (nine-grid)、情绪九宫格 (emotion grid)	emotion-grid	`text-to-image`
relationship board, relationship collage	relationship-board	`text-to-image`
memory collage	memory-collage	`text-to-image`
group photo, take a photo together	real-toon	`text-to-image`
ID card, collectible card	id-card	`text-to-image`
one-click style remix, change style, style kaleidoscope	style-remix	`text-to-image --image_list`(stylized) / `image-edit --model praline_pro`(structure-preserving edit)

Editing

Trigger words	Workflow	Command
swap background, change background	swap-bg	`image-edit --model praline_pro`
avatar series, change avatar	avatar-series	`text-to-image` → `image-face-swap`
beauty, skin smoothing	beauty	`image-edit --model gummy_pro`

Video & Try-on

Trigger words	Workflow	Command
bring to life, image to video	to-video	`image-to-video`
do this motion	motion-transfer	`video-motion-transfer`
try on, try clothes	virtual-tryon	`image-outfit-swap`

References

File	When to read
references/workflows.md	When executing a specific scenario workflow
references/models.md	When detailed command parameters are needed
references/style-library.md	When generating images (user-specified style or auto-matching needed)
references/channel-presets.md	When adapting for multiple platforms
references/feedback-loop.md	When handling user feedback write formats
references/memory-protocol.md	When handling observation lifecycle and preference classification
references/troubleshooting.md	When encountering errors
references/setup.md	For first-time configuration
references/first-time-guide.md	When `./visual/` doesn't exist (new user onboarding)