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。

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:

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

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

6. 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

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):

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

2. 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

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

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

5. 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:

image-edit model selection:

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:

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):

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:

1. Present result — show the generated image + briefly explain design rationale (why this style/composition/color palette)
2. 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
3. Adjust and regenerate — modify prompt or parameters based on feedback → re-run CLI command → back to step 1
4. 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:

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:

1. Read $VISUAL/memory/observations/observations.yaml (create if it doesn't exist)
2. Scan for semantically similar keys → merge or create new
3. Write back to file
4. 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

Editing

Video & Try-on

References