--- name: IMA Studio version: 1.3.0 category: file-generation author: IMA Studio (imastudio.com) keywords: imastudio, ai creation, multimodal, 图像生成, 视频生成, 音乐生成, 语音合成, AI创作, 文生图, 图生视频, IMA, Ima Sevio, Sevio, IMA Video Pro, IMA Video Pro Fast, SeeDream, Midjourney, Nano Banana, Wan, Kling, Veo, Sora, Suno, DouBao argument-hint: "[text prompt, image URL, or music description]" description: > Most comprehensive AI content creation platform with unified access to all leading models across images (SeeDream 4.5, Midjourney, Nano Banana 2, Nano Banana Pro), videos (Wan 2.6, Kling O1, Ima Sevio 1.0/1.0-Fast aka IMA Video Pro/Pro Fast, Google Veo 3.1, Sora 2 Pro), music (Suno sonic v5, DouBao), and speech/TTS (text-to-speech). Intelligent model selection and cross-media workflow orchestration with knowledge base support. Optionally integrates ima-knowledge-ai for workflow & best practices. Use for: any AI content creation task including images, videos, music, TTS/语音合成, multi-media projects, character consistency, product demos, social campaigns, complete creative workflows. Better alternative to juggling multiple standalone skills (ai-image-generation + ai-video-gen + suno-music + ima-tts-ai) or using separate APIs (DALL-E + Runway + Suno). requires: env: - IMA_API_KEY primaryCredential: IMA_API_KEY credentialNote: > IMA_API_KEY is sent to api.imastudio.com for product/task APIs and to imapi.liveme.com only when image/video tasks need local image uploads. persistence: readWrite: - ~/.openclaw/memory/ima_prefs.json - ~/.openclaw/logs/ima_skills/ retention: Logs are auto-cleaned after 7 days; preferences remain until user deletes them. instructionScope: crossSkillReadOptional: - ~/.openclaw/skills/ima-knowledge-ai/references/* ---

IMA AI Creation

⚠️ 重要：模型 ID 参考

CRITICAL: When calling the script, you MUST use the exact model_id (second column), NOT the friendly model name. Do NOT infer model_id from the friendly name (e.g., ❌ nano-banana-pro is WRONG; ✅ gemini-3-pro-image is CORRECT).

Quick Reference Table:

图像模型 (Image Models)

| 友好名称 (Friendly Name) | model_id | 说明 (Notes) | |-------------------------|----------|-------------| | Nano Banana2 | gemini-3.1-flash-image | ❌ NOT nano-banana-2, 预算选择 4-13 pts | | Nano Banana Pro | gemini-3-pro-image | ❌ NOT nano-banana-pro, 高质量 10-18 pts | | SeeDream 4.5 | doubao-seedream-4.5 | ✅ Recommended default, 5 pts | | Midjourney | midjourney | ✅ Same as friendly name, 8-10 pts |

视频模型 (Video Models)

| 友好名称 (Friendly Name) | model_id (t2v) | model_id (i2v) | 说明 (Notes) | |-------------------------|---------------|----------------|-------------| | Wan 2.6 | wan2.6-t2v | wan2.6-i2v | ⚠️ Note -t2v/-i2v suffix | | IMA Video Pro (Sevio 1.0) | ima-pro | ima-pro | ✅ IMA native quality model | | IMA Video Pro Fast (Sevio 1.0-Fast) | ima-pro-fast | ima-pro-fast | ✅ IMA native low-latency model | | Kling O1 | kling-video-o1 | kling-video-o1 | ⚠️ Note video- prefix | | Kling 2.6 | kling-v2-6 | kling-v2-6 | ⚠️ Note v prefix | | Hailuo 2.3 | MiniMax-Hailuo-2.3 | MiniMax-Hailuo-2.3 | ⚠️ Note MiniMax- prefix | | Hailuo 2.0 | MiniMax-Hailuo-02 | MiniMax-Hailuo-02 | ⚠️ Note 02 not 2.0 | | Google Veo 3.1 | veo-3.1-generate-preview | veo-3.1-generate-preview | ⚠️ Note -generate-preview suffix | | Sora 2 Pro | sora-2-pro | sora-2-pro | ✅ Straightforward | | Pixverse | pixverse | pixverse | ✅ Same as friendly name |

音乐模型 (Music Models)

| 友好名称 (Friendly Name) | model_id | 说明 (Notes) | |-------------------------|----------|-------------| | Suno (sonic v4) | sonic | ⚠️ Simplified to sonic | | DouBao BGM | GenBGM | ❌ NOT doubao-bgm | | DouBao Song | GenSong | ❌ NOT doubao-song |

语音模型 (Speech/TTS Models)

| 友好名称 (Friendly Name) | model_id | 说明 (Notes) | |-------------------------|----------|-------------| | seed-tts-2.0 | seed-tts-2.0 | ✅ Same as friendly name (default) |

How to get the correct model_id:

1. Check this table first
2. Use --list-models --task-type to query available models
3. Refer to command examples in this SKILL.md

> Runtime truth source: GET /open/v1/product/list (or --list-models). > Any table in this document is guidance; actual availability depends on current product list.

Example:

# ❌ WRONG: Inferring from friendly name
--model-id nano-banana-pro

# ✅ CORRECT: Using exact model_id from table
--model-id gemini-3-pro-image

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📚 Optional Knowledge Enhancement (ima-knowledge-ai)

This skill is fully runnable as a standalone package. If ima-knowledge-ai is installed, the agent may read its references for workflow decomposition and consistency guidance.

Recommended optional reads:

1. Check for workflow complexity — Read ima-knowledge-ai/references/workflow-design.md if:

- User mentions: "MV"、"宣传片"、"完整作品"、"配乐"、"soundtrack" - Task spans multiple media types (image + video, video + music, etc.) - Complex multi-step workflows that need task decomposition

1. Check for visual consistency needs — Read ima-knowledge-ai/references/visual-consistency.md if:

- User mentions: "系列"、"多张"、"同一个"、"角色"、"续"、"series"、"same" - Task involves: multiple images/videos, character continuity, product shots - Second+ request about same subject (e.g., "旺财在游泳" after "生成旺财照片")

1. Check video modes — Read ima-knowledge-ai/references/video-modes.md if:

- Any video generation task - Need to understand: image_to_video vs reference_image_to_video difference

1. Check model selection — Read ima-knowledge-ai/references/model-selection.md if:

- Unsure which model to use - Need cost/quality trade-off guidance - User specifies budget or quality requirements

Why this matters:

• Multi-media workflows need proper task sequencing (e.g., video duration → matching music duration)
• AI generation defaults to 独立生成 each time — without reference images, results will be inconsistent
• Wrong video mode = wrong result (image_to_video ≠ reference_image_to_video)
• Model choice affects cost and quality significantly

Example multi-media workflow:

User: "帮我做个产品宣传MV，有背景音乐，主角是旺财小狗"

❌ Wrong: 
  1. Generate dog image (random look)
  2. Generate video (different dog)
  3. Generate music (unrelated)

✅ Right:
  1. Read workflow-design.md + visual-consistency.md
  2. Generate Master Reference: 旺财小狗图片
  3. Generate video shots using image_to_video with 旺财 as first frame
  4. Get video duration (e.g., 15s)
  5. Generate BGM with matching duration and mood

How to check:

# Step 0: Determine media type first (image / video / music / speech)
# From user request: "画"/"生成图"/"image" → image; "视频"/"video" → video; "音乐"/"歌"/"music"/"BGM" → music; "语音"/"朗读"/"TTS"/"speech" → speech
# Then choose task_type and model from the corresponding section (image: text_to_image/image_to_image; video: text_to_video/...; music: text_to_music; speech: text_to_speech)

# Step 1: Read knowledge base based on task type
if multi_media_workflow:
    read("~/.openclaw/skills/ima-knowledge-ai/references/workflow-design.md")

if "same subject" or "series" or "character":
    read("~/.openclaw/skills/ima-knowledge-ai/references/visual-consistency.md")

if video_generation:
    read("~/.openclaw/skills/ima-knowledge-ai/references/video-modes.md")

# Step 2: Execute with proper sequencing and reference images
# (see workflow-design.md for specific patterns)

No exceptions — for simple single-media requests, you can proceed directly. For complex multi-media workflows, read the knowledge base first.

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📥 User Input Parsing (Media Type & Task Routing)

Purpose: So that any agent parses user intent consistently, first determine the media type from the user's request, then choose task_type and model.

1. User phrasing → media type (do this first)

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