Sn Image Base

opensensenova/sn-image-base

| Base-layer skill for the SenseNova-Skills project, providing low-level APIs for image generation, recognition (VLM), and text optimization (LLM). This skill does not preprocess inputs; it only calls backend services and returns results. This skill is not user-facing and is intended for upper-layer skills only.

43k tokens

context cost

the whole folder, loaded on every use

files

ships runnable scripts

copies elsewhere

how many repositories repackaged it

4851

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/OpenSenseNova/SenseNova-Skills --skill sn-image-base

The instruction itself

14 sections, as written by the author

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 three low-level tools:

sn-image-generate: image generation (calls text-to-image-no-enhance API)
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:

|------|------|--------|------|

| --image-size | string | 2k | Image size preset (case-insensitive). Recommended: 2k. 4k optional, needs model support (sensenova rejects it → status=failed). Other values → status=failed. |

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):

|------|------|-----------|---------|------|

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):

|------|------|-----------|---------|------|

Available values for --llm-type:

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

VLM vs LLM

|------|----------|-----------------|-------------|

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:

|------|--------|----------|------|

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.

|------|-----------|-------------|-------------|

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:

|------|------------------------------|---------------|--------------|---------------|

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-6.7-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.

How to use it

Copy the folder

Take opensensenova/sn-image-base from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.

Install what it needs

The instructions reference pip. Without those the skill loads but fails at the first command.