Skills IMA Sevio AI Generation

install

source · Clone the upstream repo

git clone https://github.com/openclaw/skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/allenfancy-gan/ima-sevio-ai" ~/.claude/skills/clawdbot-skills-ima-sevio-ai-generation && rm -rf "$T"

manifest: skills/allenfancy-gan/ima-sevio-ai/SKILL.md

source content

IMA Sevio AI Creation

🎯 Skill Capabilities

本技能是 Ima Sevio 视频生成专用入口。对外不是“模型 ID 映射器”，而是两档清晰的视频生成能力：

Ima Sevio 1.0（质量优先）
- 定位：正式出片、质感优先、镜头语言要求高的任务。
- 适合：广告镜头、角色叙事、风格化短片、需要更高一致性的镜头段落。
Ima Sevio 1.0-Fast（速度优先）
- 定位：快速打样、批量试风格、创意迭代。
- 适合：提案阶段 A/B 版本、镜头预演、低延迟验证。

Ability Positioning (Top-Tier Video Model Class)

在公开视频能力维度上，Sevio 系列可按以下能力理解（用于用户预期管理）：

时序与主体一致性：在连续动作和主体保持上具备高稳定性表现。
镜头语言控制：支持对推拉摇移、节奏和运动感的描述驱动。
多模态条件理解：可结合文本与参考素材（image/reference/first-last frame）进行生成。
高质量输出取向：面向高分辨率、高观感视频产出（以当次产品规则为准）。

Workflow Coverage

```
text_to_video
```
：文本直接生成视频。
```
image_to_video
```
：以首帧图驱动动态生成。
```
first_last_frame_to_video
```
：以首尾帧约束过渡与收束。
```
reference_image_to_video
```
：以参考图约束风格/主体特征。

Input & Reliability

```
prompt
```
负责主体、动作、镜头、风格与节奏描述。
```
--input-images
```
支持单个/多个输入，统一以字符串数组语义处理。
本地文件先上传再生成，远程 HTTP(S) 链接直接使用。
运行时动态匹配产品规则（credit_rules/form_config）并内置自动重试与降级策略。
轮询上限 40 分钟；若无明确报错但超时，会提示前往创作记录页查看。

Output

返回可直接分发的视频结果 URL（含封面信息），可直接用于消息卡片或播放器。

✨ Expected Outcomes & Boundaries (Outcomes, Timing, Scope Limits)

Expected Outcomes

质量预期（Sevio 1.0）：在画面稳定、主体一致、镜头控制上，目标体验达到行业同级高水平能力带。
速度预期（Sevio 1.0-Fast）：在保持可用画质与控制力的前提下，提供更快周转，适合多轮迭代。
模式预期：图生/参考/首尾帧模式，相比纯文生视频更有利于主体连续性与风格一致性。

Timing Expectations

模型（用户展示）	典型耗时
Ima Sevio 1.0（IMA Video Pro）	120~300s
Ima Sevio 1.0-Fast（IMA Video Pro Fast）	60~120s

轮询超时上限：

40 分钟（2400s）

。

Capability Boundaries (Avoid Misunderstanding)

本技能只做 视频生成链路，不负责后期剪辑、自动分镜编排、成片包装。
结果质量受提示词、参考素材质量、当次产品规则与积分策略影响，不保证每次一致。
仅支持本技能白名单模型；其他模型名会被拦截或映射后执行。

⚠️ 内部调用：模型 ID 参考（不对用户展示）

User-facing rule: In user messages, always use Ima Sevio 1.0 / Ima Sevio 1.0-Fast names.
Do not expose raw

model_id

unless the user explicitly asks for technical details.

CRITICAL: When calling the script, you MUST use exact

model_id

values. For

ima-sevio-ai

, only these two are allowed:

Friendly Name	model_id	Notes
IMA Pro	`ima-pro`	Default quality model
IMA Pro Fast	`ima-pro-fast`	Faster / lower-latency model
Ima Sevio 1.0	`ima-pro`	Display-name alias
Ima Sevio 1.0-Fast	`ima-pro-fast`	Display-name alias

模型中文介绍（可公开口径）

IMA Video Pro（Ima Sevio 1.0）

面向高质量视频创作的主力模型。
在时序一致性、镜头语言控制、多模态条件理解等核心维度上，能力定位达到行业同级高水平视频模型能力。
适合对质感、稳定性和镜头可控性要求更高的生产任务。

核心优势（公开可查）

高帧率时序一致性
精准镜头语言控制
图像 / 音频 / 文本多模态输入
2K 级输出画质

IMA Video Pro Fast（Ima Sevio 1.0-Fast）

面向高频迭代场景的加速模型版本。
在保持主体可辨识与镜头可控的基础上，优先缩短生成时延，适合提案打样、快速试风格和实时创作流程。

Rules:

Do NOT infer model IDs from other IMA skills.
Do NOT use any model outside this allowlist.
If user asks for other models, map to one of the two allowed models with explanation.
Alias input
```
Ima Sevio 1.0
```
is auto-mapped to
```
ima-pro
```
.
Alias input
```
Ima Sevio 1.0-Fast
```
is auto-mapped to
```
ima-pro-fast
```
.

📚 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 better mode selection and consistency guidance.

📥 User Input Parsing (Model & Parameter Recognition)

1) User phrasing →

task_type

User intent	task_type
Only text	`text_to_video`
One image as first frame	`image_to_video`
One image as reference	`reference_image_to_video`
Two images as first+last frame	`first_last_frame_to_video`

2) User phrasing →

model_id

Normalize case-insensitively and ignore spaces:

User says	model_id
`ima-pro` , `pro` , `专业版` , `高质量`	`ima-pro`
`ima-pro-fast` , `fast` , `极速` , `快速`	`ima-pro-fast`
`Ima Sevio 1.0`	`ima-pro`
`Ima Sevio 1.0-Fast`	`ima-pro-fast`
"默认" / "推荐" / "自动"	`ima-pro`

If user explicitly asks "faster", prefer

ima-pro-fast

. If user explicitly asks "best quality", prefer

ima-pro

3) User phrasing → duration / resolution / aspect_ratio

User says	Parameter	Normalized value
5秒 / 5s	duration	5
10秒 / 10s	duration	10
15秒 / 15s	duration	15
横屏 / 16:9	aspect_ratio	16:9
竖屏 / 9:16	aspect_ratio	9:16
方形 / 1:1	aspect_ratio	1:1
720P / 720p	resolution	720P
1080P / 1080p	resolution	1080P
4K / 4k	resolution	4K (only if model/rule supports)

If unspecified, use product

form_config

defaults.

⚙️ How This Skill Works

This skill uses bundled script

scripts/ima_video_create.py

and keeps original API workflow:

product list query
parameter resolution
create task
poll task detail
return video URL

🌐 Network Endpoints Used

Domain	Purpose	What's Sent
`api.imastudio.com`	task create + status polling	prompt, model params, task IDs, API key
`imapi.liveme.com`	image upload (when image input exists)	image bytes, API key

Privacy notes:

API key is sent to both domains for auth.
```
--user-id
```
is local-only and not sent to IMA servers.
Local files: preferences and logs in
```
~/.openclaw
```
.

Agent Execution (Internal)

# Text to video
python3 {baseDir}/scripts/ima_video_create.py \
  --api-key $IMA_API_KEY \
  --task-type text_to_video \
  --model-id ima-pro \
  --prompt "a puppy runs across a sunny meadow, cinematic" \
  --user-id {user_id} \
  --output-json

# Image to video
python3 {baseDir}/scripts/ima_video_create.py \
  --api-key $IMA_API_KEY \
  --task-type image_to_video \
  --model-id ima-pro-fast \
  --prompt "camera slowly zooms in" \
  --input-images https://example.com/photo.jpg \
  --user-id {user_id} \
  --output-json

# First-last frame to video
python3 {baseDir}/scripts/ima_video_create.py \
  --api-key $IMA_API_KEY \
  --task-type first_last_frame_to_video \
  --model-id ima-pro \
  --prompt "smooth transition" \
  --input-images https://example.com/first.jpg https://example.com/last.jpg \
  --user-id {user_id} \
  --output-json

--input-images

accepts remote HTTP(S) links and local file paths. Local image files are uploaded to OSS first; non-local HTTP(S) links are assigned directly. CLI form is space-separated arguments; equivalent JSON form is:

["https://example.com/ref1.jpg","https://example.com/ref2.jpg"]

🚨 CRITICAL: How to send video to user

Always send remote URL directly:

video_url = json_output["url"]
message(action="send", media=video_url, caption="✅ 视频生成成功")

Do NOT download to local file before sending.

🧠 User Preference Memory

Storage:

~/.openclaw/memory/ima_prefs.json

{
  "user_{user_id}": {
    "text_to_video": {"model_id": "ima-pro", "model_name": "Ima Sevio 1.0", "credit": 0, "last_used": "..."},
    "image_to_video": {"model_id": "ima-pro-fast", "model_name": "Ima Sevio 1.0-Fast", "credit": 0, "last_used": "..."},
    "first_last_frame_to_video": {"model_id": "ima-pro", "model_name": "Ima Sevio 1.0", "credit": 0, "last_used": "..."},
    "reference_image_to_video": {"model_id": "ima-pro", "model_name": "Ima Sevio 1.0", "credit": 0, "last_used": "..."}
  }
}

Model selection priority:

user preference
knowledge-ai recommendation
fallback default (
```
ima-pro
```
)

Defaults

Task	Default	Alt (fast)
text_to_video	`ima-pro`	`ima-pro-fast`
image_to_video	`ima-pro`	`ima-pro-fast`
first_last_frame_to_video	`ima-pro`	`ima-pro-fast`
reference_image_to_video	`ima-pro`	`ima-pro-fast`

💬 User Experience Protocol (IM / Feishu / Discord)

Estimated Generation Time

Model	Estimated Time	Poll Every	Send Progress Every
ima-pro	120~300s	8s	45s
ima-pro-fast	60~120s	8s	30s

Polling timeout upper bound: 40 minutes (

2400s

Use:

Step 1: pre-generation notice (model/time/credits)
Step 2: progress updates
Step 3: success push (video first, then shareable link)
Step 4: failure message with actionable retry options

Progress formula:

P = min(95, floor(elapsed_seconds / estimated_max_seconds * 100))

Step 4 — Failure Notification

Translate technical errors to user language. For 401/4008 include links:

API key: https://www.imaclaw.ai/imaclaw/apikey
credits: https://www.imaclaw.ai/imaclaw/subscription

Enhanced Error Handling (Reflection)

The script keeps the same reflection mechanism (up to 3 retries):

```
500
```
→ parameter degradation
```
6009
```
→ auto-complete missing params from matched rules
```
6010
```
→ reselect matching credit rule
timeout → actionable guidance

Fallback suggestion table

Failed model	First alt	Second alt
`ima-pro`	`ima-pro-fast`	`ima-pro` (retry with downgraded params)
`ima-pro-fast`	`ima-pro`	`ima-pro-fast` (retry with defaults)
unknown	`ima-pro`	`ima-pro-fast`

Supported Models

Only two models are exposed by this skill:

```
ima-pro
```
```
ima-pro-fast
```

Supported categories:

```
text_to_video
```
```
image_to_video
```
```
first_last_frame_to_video
```
```
reference_image_to_video
```

Attribute rules, points, and exact parameter combinations must be queried at runtime from product list.

Environment

Base URL:

https://api.imastudio.com

Required headers:

Authorization: Bearer ima_your_api_key_here

```
x-app-source: ima_skills
```
```
x_app_language: en
```
(or
```
zh
```
)

⚠️ MANDATORY: Always Query Product List First

You MUST call

/open/v1/product/list

before creating tasks.

attribute_id

and

credit

must match current rule set.

Common failures if skipped:

invalid product attribute
insufficient points
```
6006
```
,
```
6010
```

Core Flow

1) GET /open/v1/product/list
2) (if image input) upload image(s) -> HTTPS CDN URL(s)
3) POST /open/v1/tasks/create
4) POST /open/v1/tasks/detail (poll every 8s)

Image Upload

For image tasks, source images must resolve to public HTTPS URLs. Bundled script supports local file path and uploads automatically.

API 1: Product List

GET /open/v1/product/list?app=ima&platform=web&category=<task_type>

Use type=3 leaf nodes to read:

```
model_id
```
```
id
```
(
```
model_version
```
)
```
credit_rules[]
```
```
form_config[]
```

API 2: Create Task

POST /open/v1/tasks/create

text_to_video (example)

{
  "task_type": "text_to_video",
  "enable_multi_model": false,
  "src_img_url": [],
  "parameters": [
    {
      "attribute_id": 1234,
      "model_id": "ima-pro",
      "model_name": "Ima Sevio 1.0",
      "model_version": "ima-pro",
      "app": "ima",
      "platform": "web",
      "category": "text_to_video",
      "credit": 25,
      "parameters": {
        "prompt": "a puppy dancing happily",
        "duration": 5,
        "resolution": "1080P",
        "aspect_ratio": "16:9",
        "n": 1,
        "input_images": [],
        "cast": {"points": 25, "attribute_id": 1234}
      }
    }
  ]
}

For image tasks, keep top-level

src_img_url

and nested

input_images

consistent.

API 3: Task Detail

POST /open/v1/tasks/detail

with

{ "task_id": "..." }

Status interpretation:

```
resource_status
```
:
```
0/null
```
processing,
```
1
```
ready,
```
2
```
failed,
```
3
```
deleted
Stop only when all medias have
```
resource_status == 1
```
and none failed

Common Mistakes

Polling too fast (use 8s)
Missing required nested fields (
```
prompt
```
,
```
cast
```
,
```
n
```
)
Credit/attribute mismatch (
```
6006
```
/
```
6010
```
)
Inconsistent
```
src_img_url
```
and
```
input_images
```
Wrong mode choice (
```
image_to_video
```
vs
```
reference_image_to_video
```
)

Python Example

import time
import requests

BASE_URL = "https://api.imastudio.com"
API_KEY = "ima_your_key_here"
HEADERS = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json",
    "x-app-source": "ima_skills",
    "x_app_language": "en",
}

ALLOWED = {"ima-pro", "ima-pro-fast"}


def get_products(category: str) -> list:
    r = requests.get(
        f"{BASE_URL}/open/v1/product/list",
        headers=HEADERS,
        params={"app": "ima", "platform": "web", "category": category},
    )
    r.raise_for_status()
    nodes = r.json().get("data", [])
    leaves = []

    def walk(items):
        for n in items:
            if n.get("type") == "3" and n.get("model_id") in ALLOWED:
                leaves.append(n)
            walk(n.get("children") or [])

    walk(nodes)
    return leaves


def create_video_task(task_type: str, prompt: str, product: dict, src_img_url=None, **extra) -> str:
    src_img_url = src_img_url or []
    rule = product["credit_rules"][0]
    defaults = {f["field"]: f["value"] for f in product.get("form_config", []) if f.get("value") is not None}

    params = {
        "prompt": prompt,
        "n": 1,
        "input_images": src_img_url,
        "cast": {"points": rule["points"], "attribute_id": rule["attribute_id"]},
        **defaults,
    }
    params.update(extra)

    payload = {
        "task_type": task_type,
        "enable_multi_model": False,
        "src_img_url": src_img_url,
        "parameters": [{
            "attribute_id": rule["attribute_id"],
            "model_id": product["model_id"],
            "model_name": product["name"],
            "model_version": product["id"],
            "app": "ima",
            "platform": "web",
            "category": task_type,
            "credit": rule["points"],
            "parameters": params,
        }],
    }

    r = requests.post(f"{BASE_URL}/open/v1/tasks/create", headers=HEADERS, json=payload)
    r.raise_for_status()
    return r.json()["data"]["id"]


def poll(task_id: str, interval: int = 8, timeout: int = 600) -> dict:
    deadline = time.time() + timeout
    while time.time() < deadline:
        r = requests.post(f"{BASE_URL}/open/v1/tasks/detail", headers=HEADERS, json={"task_id": task_id})
        r.raise_for_status()
        task = r.json().get("data", {})
        medias = task.get("medias", [])
        if medias:
            rs = lambda m: m.get("resource_status") if m.get("resource_status") is not None else 0
            if any(rs(m) in (2, 3) or (m.get("status") == "failed") for m in medias):
                raise RuntimeError(f"Task failed: {task_id}")
            if all(rs(m) == 1 for m in medias):
                return task
        time.sleep(interval)
    raise TimeoutError(f"Task timed out: {task_id}")