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saa-agentSAA Agent 命令行

Agent Skill

saa-agent 用于处理图像、截图、视觉识别或图片素材相关工作,适合在 OpenClaw 中需要让 Agent 分析图片、整理视觉素材或辅助图像流程时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

32,778

周安装

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下载量

11,489
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:saa-agent(SAA Agent 命令行)
来源仓库:https://github.com/mirabarukaso/saa-agent
安装命令:
openclaw skills install saa-agent
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install saa-agent

简介

使 AI 代理能够通过命令行界面使用角色选择独立应用程序 (SAA) 图像生成后端来生成图像。

SKILL.md

name
saa-agent
description
Enables AI agents to generate images using the Character Select Stand Alone App (SAA) image generation backend via command-line interface.
license
MIT

SAA CLI Tool

A command-line interface for interacting with Character Select Stand Alone App (SAA) via WebSocket connections. Supports both ComfyUI and WebUI backends for AI image generation.

Prerequisites

CRITICAL: Before invoking this tool, confirm with the user that:

  1. The SAA backend is running, and version is above 2.4.0
  2. The SAAC (SAA Client) feature is enabled
  3. The WebSocket address is available
  4. Some Mac users uses python3 instead of python to invoke Python 3.x

For SAA setup details, let your owner visit the project repository.

Basic Usage

The tool requires minimal parameters to function. The examples below demonstrate the standard usage pattern:

Minimal Command With Model Selection (Recommended for Most Cases)

python saa-agent.py \
  --ws-address "user_provided_ws_address" \
  --model "waiIllustriousSDXL_v160.safetensors" \
  --positive "your detailed prompt here" \
  --negative "low quality, blurry, bad anatomy"

Regional Prompting (Split Composition)

python saa-agent.py \
  --ws-address "user_provided_ws_address" \
  --model "waiIllustriousSDXL_v160.safetensors" \
  --regional \
  --positive-left "1girl, warrior, red armor" \
  --positive-right "1boy, mage, blue robes"

More Examples

Get more usage examples and detailed parameter explanations:

python saa-agent.py
python saa-agent.py --help

Key Parameters

Required

  • --ws-address: WebSocket address (obtain from user)
  • --positive: Main prompt OR use --regional mode with --positive-left and --positive-right

Commonly Modified

  • --model: Change the checkpoint model (default: waiIllustriousSDXL_v160.safetensors)
  • --negative: Specify unwanted elements
  • --width / --height: Image dimensions (defaults: 1024x1360)
  • --steps: Sampling steps (default: 28)
  • --seed: Set specific seed or -1 for random

Advanced (Use Sparingly)

  • --cfg: CFG scale (default: 7.0)
  • --sampler: Sampling algorithm (default: euler_ancestral)
  • --scheduler: Scheduler type (default: normal)

Important Guidelines

HiResFix Warning

DO NOT use --hifix unless specifically requested by the user.

HiResFix significantly increases generation time and requires substantial GPU resources. Only enable if:

  • User explicitly requests high-resolution upscaling
  • User confirms their GPU can handle the additional load

Backend Busy State

If the generation returns either of these errors:

Error: WebUI is busy, cannot run new generation, please try again later.
Error: ComfyUI is busy, cannot run new generation, please try again later.

Actions to take:

  1. DO NOT automatically retry the generation
  2. Inform the user: "The SAA backend is currently busy. This could mean another process is generating an image, or the backend is locked from a previous error."
  3. Advise: "Please wait 20-60 seconds before trying again."
  4. Let the user manually retry

DO NOT chain multiple retry attempts as this can worsen backend congestion.

Skeleton Key Usage

The --skeleton-key parameter forcefully unlocks the backend's atomic lock.

When to use:

  • User confirms no other processes are using the backend
  • Backend appears stuck despite waiting
  • User explicitly requests unlocking

How to use:

python saa-agent.py \
  --ws-address "user_provided_ws_address" \
  --skeleton-key \
  --positive "test prompt"

Rules:

  1. ALWAYS ask for user confirmation before using --skeleton-key
  2. ONLY use it once per user request
  3. Explain to the user that this forcefully terminates any locks

Example conversation:

AI: "The backend appears to be locked. Would you like me to use the skeleton key to force unlock it? This will terminate any existing locks."
User: "Yes, please unlock it."
AI: [proceeds to run command with --skeleton-key]

Parameter Defaults

When in doubt, rely on these defaults - they work well for most cases:

  • Model: waiIllustriousSDXL_v160.safetensors
  • Dimensions: 1024x1360
  • CFG: 7.0
  • Steps: 28
  • Sampler: euler_ancestral
  • Scheduler: normal
  • Seed: -1 (random)

Output Handling

By default, images are saved to generated_image.png. You can specify a custom output path:

--output "custom_filename.png"

For programmatic handling, use base64 output:

--base64

This outputs base64-encoded image data(huge!!!) to stdout instead of saving a file.

Example Workflow

  1. User requests: "Generate an anime girl with long blue hair"
  1. AI executes:
python saa-agent.py \
  --ws-address "user_ws_address" \
  --positive "1girl, long hair, blue hair, anime style, detailed" \
  --negative "low quality, blurry, bad anatomy"
  1. If backend busy error occurs:

- Inform user - Wait for user to retry (don't auto-retry)

  1. If success:

- Confirm image was generated - Provide file path if relevant

Common Pitfalls to Avoid

  1. Don't use --hifix unless explicitly requested
  2. Don't auto-retry on backend busy errors
  3. Don't use --skeleton-key without user permission
  4. Don't add excessive parameters - unless explicitly requested, the defaults are well-tuned
  5. Don't assume backend is ready - always confirm with user first

Error Codes

  • Exit code 0: Success
  • Exit code 1: Connection error (check backend is running)
  • Exit code 2: Authentication error (check credentials)
  • Exit code 3: Generation error (check parameters)
  • Exit code 4: Timeout (backend may be overloaded)
  • Exit code 5: Invalid parameters (check command syntax)

Best Practices

  1. Start with minimal parameters with model selection if needed
  2. Ask user for WebSocket address on first use
  3. Handle busy states gracefully - don't spam retries
  4. Use --verbose flag when debugging issues
  5. Respect the skeleton key - it's a powerful override tool

AI Agent Guidelines for This Skill

These rules help maintain appropriate transparency and user control when executing generation tasks.

  1. Command Execution & User Notification

By default, execute the command directly without asking for confirmation. Show the full command and ask for approval only when: - The user explicitly requests to review it first - The operation involves sensitive or high-impact parameters - The agent judges that showing the command is prudent in context

Example (when disclosure is needed):

    python3 saa-agent.py --ws-address "wss://..." --username "..." --password "..." --positive "[prompt]" --negative "[prompt]" --output "[path]" [--verbose]
  1. --verbose Flag

- Not used by default - Add automatically or recommend when: - Task fails and debugging is needed - User specifically asks for detailed logs or seed

  1. Result Reporting

After completion, provide a short summary to the user by default, including: - Success/failure status - Positive & negative prompts (or meaningful summary) - Seed (if available) - Output path

Example:

    Generation completed
    • Positive: [...]
    • Negative: [...]
    • Seed: 123456789
    • Output: [path]

Skip detailed reporting only if the user has clearly requested silent / minimal feedback. Always report errors, even in silent mode.

  1. Error Handling

- On failure: consider one retry with --verbose to capture diagnostic information - Communicate the main error cause clearly - Do not perform unlimited retries; defer to user after one attempt if needed

适合场景

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用户想查找某类 Agent Skill 时

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需要根据任务场景推荐可安装能力包时

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需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

95.76%
按下载量换算11,002

安全审计

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可疑

ClawScan

通过

Static analysis

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权限和风险

执行命令

安装流程涉及命令执行,可能通过 openclaw skills install saa-agent 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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