sn-image-base

sn-image-base

Dependency Installation

pip install -r requirements.txt

Overview

sn-image-base is the base-layer skill (tier 0) of the SenseNova-Skills project and provides four low-level tools:

• sn-image-generate: image generation (calls text-to-image-no-enhance API)
• sn-image-edit: image editing with SenseNova U1.5 Lite (calls /images/edits)
• sn-image-recognize: image recognition (uses VLM to analyze image content)
• sn-text-optimize: text optimization (uses LLM to process text)

This skill does not perform any input preprocessing and only calls backend services to return results.

Tools List

sn-image-generate

Image generation tool that calls the text-to-image-no-enhance API.

--prompt is required; all other parameters are optional:

Parameter	Type	Default	Description
--prompt	string	Required	Prompt text for image generation
--negative-prompt	string	""	Negative prompt
--image-size	string	2k	Image size preset (case-insensitive). Recommended: 2k. 4k is supported by sensenova-u1.5-lite; other SenseNova image models may reject it. Other values → status=failed.
--aspect-ratio	string	16:9	Aspect ratio, e.g. 1:1, 16:9, 9:16
--seed	int	None	Random seed for reproducible generation
--unet-name	string	None	Specify a UNet model name
--api-key	string	SN_IMAGE_GEN_API_KEY -> SN_API_KEY	API key (CLI argument has priority; MissingApiKeyError is raised when all are empty)
--base-url	string	SN_IMAGE_GEN_BASE_URL -> SN_BASE_URL	API base URL (CLI argument has priority)
--poll-interval	float	5.0	Polling interval (seconds)
--timeout	float	300.0	Timeout (seconds)
--insecure	flag	False	Disable TLS verification
--save-path	Path	Auto-generated	Save path

SenseNova image requests explicitly send watermark=false by default. Both sensenova-u1-fast and sensenova-u1.5-lite are supported; U1.5 Lite additionally supports native 4K output. This no-watermark feature is currently in free public beta and may become paid.

sn-image-edit

Edits one or more reference images with SenseNova U1.5 Lite through the /images/edits endpoint. Local paths are converted to Data URLs; HTTP(S) URLs and Data URLs are passed through.

python scripts/sn_agent_runner.py sn-image-edit \
    --prompt "Change the background to a snowy mountain" \
    --images source.png reference.png \
    --save-path edited.png

The edit request uses the official defaults n=1, size=auto, watermark=false, prompt_extend=true, and response_format=url.

sn-image-recognize

Image recognition tool that uses VLM (Vision Language Model) to analyze image content. Supports multiple image inputs.

--images and --user-prompt (or --user-prompt-path) are required. All other parameters use three-level defaults (CLI > env var > built-in default):

Parameter	Type	Built-in Default	Env Var	Description
--api-key	string	No hardcoded default	SN_VISION_API_KEY -> SN_CHAT_API_KEY -> SN_API_KEY	Chat runtime API key; raises MissingApiKeyError when all are unset
--base-url	string	SN_CHAT_BASE_URL default	SN_VISION_BASE_URL -> SN_CHAT_BASE_URL -> SN_BASE_URL	Vision provider base URL; falls back to shared chat/global provider
--model	string	sensenova-6.8-flash-lite	SN_VISION_MODEL -> SN_CHAT_MODEL	Vision-capable model name
--vlm-type	string	openai-completions	SN_VISION_TYPE -> SN_CHAT_TYPE	Chat protocol type override
--user-prompt-path	string	None	-	Local file path, mutually exclusive with --user-prompt
--system-prompt-path	string	None	-	Local file path, mutually exclusive with --system-prompt

Available values for --vlm-type:

• openai-completions: OpenAI-compatible /v1/chat/completions interface
• anthropic-messages: Anthropic Messages /v1/messages interface

sn-text-optimize

Text optimization tool that uses LLM (Language Model) to optimize text content. Does not accept image inputs.

--user-prompt (or --user-prompt-path) is required. All other parameters use three-level defaults (CLI > env var > built-in default):

Parameter	Type	Built-in Default	Env Var	Description
--api-key	string	No hardcoded default	SN_TEXT_API_KEY -> SN_CHAT_API_KEY -> SN_API_KEY	Chat runtime API key; raises MissingApiKeyError when all are unset
--base-url	string	SN_CHAT_BASE_URL default	SN_TEXT_BASE_URL -> SN_CHAT_BASE_URL -> SN_BASE_URL	Text provider base URL; falls back to shared chat/global provider
--model	string	sensenova-6.8-flash-lite	SN_TEXT_MODEL -> SN_CHAT_MODEL	Text model name
--llm-type	string	openai-completions	SN_TEXT_TYPE -> SN_CHAT_TYPE	Chat protocol type override
--user-prompt-path	string	None	-	Local file path, mutually exclusive with --user-prompt
--system-prompt-path	string	None	-	Local file path, mutually exclusive with --system-prompt

Available values for --llm-type:

• openai-completions: OpenAI-compatible /v1/chat/completions interface
• anthropic-messages: Anthropic Messages /v1/messages interface

VLM vs LLM

Tool	Model Type	Image Input	Interface Type Parameter
sn-image-recognize	VLM (Vision Language Model)	Yes, supports multiple images	--vlm-type
sn-text-optimize	LLM (Language Model)	No, text only	--llm-type

Usage

All tools are called through the unified sn_agent_runner.py entrypoint:

# Image generation (only prompt required; api-key/base-url have defaults)
python scripts/sn_agent_runner.py sn-image-generate \
    --prompt "..."

# Image generation (override base-url)
python scripts/sn_agent_runner.py sn-image-generate \
    --prompt "..." \
    --base-url "https://custom-endpoint.com/v1"

# Image generation (explicitly override api-key)
python scripts/sn_agent_runner.py sn-image-generate \
    --prompt "..." \
    --api-key "sk-xxx"

# Image recognition (VLM) - minimal call (uses built-in Sensenova defaults)
python scripts/sn_agent_runner.py sn-image-recognize \
    --user-prompt "Describe the image" \
    --images "path/to/image.png"

# Image recognition (VLM) - override to Anthropic Claude API compatible (messages interface)
python scripts/sn_agent_runner.py sn-image-recognize \
    --user-prompt "Describe the image" \
    --images "path/to/image.png" \
    --api-key "sk-ant-xxx" \
    --base-url "https://api.anthropic.com" \
    --model "claude-sonnet-4-6" \
    --vlm-type "anthropic-messages"

# Text optimization (LLM) - minimal call (uses built-in Sensenova defaults)
python scripts/sn_agent_runner.py sn-text-optimize \
    --user-prompt "Optimize the text: ..."

# Text optimization (LLM) - override to Anthropic Claude API compatible (messages interface)
python scripts/sn_agent_runner.py sn-text-optimize \
    --user-prompt "Optimize the text: ..." \
    --api-key "sk-ant-xxx" \
    --base-url "https://api.anthropic.com" \
    --model "claude-sonnet-4-6" \
    --llm-type "anthropic-messages"

Default Parameter Behavior

Authentication parameters for sn-image-generate have the following default behavior:

Parameter	Default	Override	Description
--base-url	SN_IMAGE_GEN_BASE_URL -> SN_BASE_URL	--base-url "..."	CLI argument has priority
--api-key	SN_IMAGE_GEN_API_KEY -> SN_API_KEY	--api-key "..."	CLI argument has priority; throws MissingApiKeyError if all values are empty

sn-image-recognize and sn-text-optimize use priority: CLI argument > command-specific env var > shared SN_CHAT_* env var > global SN_* env var > built-in default.

Parameter	Built-in Default	Vision Env Var	Text Env Var
--api-key	None (must be provided)	SN_VISION_API_KEY -> SN_CHAT_API_KEY -> SN_API_KEY	SN_TEXT_API_KEY -> SN_CHAT_API_KEY -> SN_API_KEY
--base-url	https://token.sensenova.cn/v1	SN_VISION_BASE_URL -> SN_CHAT_BASE_URL -> SN_BASE_URL	SN_TEXT_BASE_URL -> SN_CHAT_BASE_URL -> SN_BASE_URL
--model	sensenova-6.8-flash-lite	SN_VISION_MODEL -> SN_CHAT_MODEL	SN_TEXT_MODEL -> SN_CHAT_MODEL
--vlm-type / --llm-type	openai-completions	SN_VISION_TYPE -> SN_CHAT_TYPE	SN_TEXT_TYPE -> SN_CHAT_TYPE

api_key resolution order (high to low): CLI --api-key > command-specific key (SN_VISION_API_KEY/SN_TEXT_API_KEY) > SN_CHAT_API_KEY > SN_API_KEY. If all are unset, MissingApiKeyError is raised.

Only --api-key must be provided via CLI or environment; base URL, model, and interface type have shared chat defaults.

Agent Configuration Integration

The agent can automatically read parameters from openclaw.json without manual input:

CLI Parameter	openclaw.json Field	Example
--base-url	providers.<name>.baseUrl	https://api.anthropic.com
--llm-type	providers.<name>.api	anthropic-messages / openai-completions
--vlm-type	providers.<name>.api	anthropic-messages / openai-completions
--model	providers.<name>.models[].id	claude-sonnet-4-6
--api-key	providers.<name>.apiKey or env var	sk-cp-...

Note: --llm-type and --vlm-type share the same providers.<name>.api field and are used by LLM and VLM tools respectively.

Mapping between provider.api and interface type:

api Value	Corresponding --llm-type / --vlm-type	Endpoint Path
anthropic-messages	anthropic-messages	/v1/messages
openai-completions	openai-completions	/v1/chat/completions
openai-responses	(future extension)	/responses

Mapping Between base-url and Interface Type

Different API types have different requirements for base-url format:

Type	--llm-type / --vlm-type	Recommended base-url	Code Appended Path	Final URL Example
LLM	openai-completions	https://token.sensenova.cn/v1	/chat/completions	https://token.sensenova.cn/v1/chat/completions
LLM	anthropic-messages	https://api.anthropic.com/v1	/messages	https://api.anthropic.com/v1/messages
VLM	openai-completions	https://token.sensenova.cn/v1	/chat/completions	https://token.sensenova.cn/v1/chat/completions
VLM	anthropic-messages	https://api.anthropic.com/v1	/messages	https://api.anthropic.com/v1/messages

Note:

• Recommended chat base URLs include the provider API version path, for example /v1.
• For compatibility, if the configured chat base URL has no path, the runner appends /v1/chat/completions or /v1/messages.
• If the configured chat base URL already has a path such as /v1, the runner appends only /chat/completions or /messages.
• Some providers use versioned paths other than /v1, such as Gemini's /v1beta/openai.

Output Format

All tools support two output formats:

• --output-format text (default): outputs plain text result
• --output-format json: outputs JSON, including status and elapsed_seconds (runtime in seconds, rounded to 2 decimals)

JSON output for sn-image-recognize and sn-text-optimize also includes model, base_url, and interface_type to verify the effective runtime configuration:

{
  "status": "ok",
  "result": "...",
  "model": "sensenova-6.8-flash-lite",
  "base_url": "https://token.sensenova.cn/v1",
  "interface_type": "openai-completions",
  "elapsed_seconds": 1.23
}

On failure:

{
  "status": "failed",
  "error": "error message",
  "elapsed_seconds": 0.05
}

Input/Output Specification

See references/api_spec.md for details.