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Agent Skill · 效率

saa-agent(SAA Agent 命令行)

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。

saa-agent 是效率类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为执行命令;许可证为 MIT-0,第三方扫描有提醒项,按安装量在效率类的 21,003 个 Skill 里排第 1,876。

总安装 32,778周安装 1,408GitHub Stars 公开资料未说明下载 11,489核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.1

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

这个 Skill 是做什么的

效率类 Skill 把重复的整理、汇总、转换工作交给助手完成,减少手工复制粘贴。

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。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install saa-agent;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

saa-agent 怎么安装?

在终端执行 openclaw skills install saa-agent。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

saa-agent 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

saa-agent 需要什么权限?

权限级别是执行命令。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。

saa-agent 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

saa-agent 用的人多吗?

公开数据是累计安装 32,778 次,近一周安装 1,408 次,下载 11,489 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

saa-agent 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理效率类任务、又不想每次都手写提示词的人。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

效率类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
github

GitHub 代码协作 · OpenClaw

7,632,918545需要联网MIT-0通过
nano-pdf

Nano PDF 文档 · OpenClaw

4,249,476231需要联网MIT-0通过
auto-updater

自动更新程序 · OpenClaw

2,964,063386操作浏览器MIT-0通过
video-frames

视频帧 · OpenClaw

2,621,304112需要联网MIT-0通过
gemini

Gemini 助手 · OpenClaw

2,547,39648需要联网MIT-0有提醒
saa-agent

SAA Agent 命令行 · 本页

32,778—执行命令MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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

平台分布

OpenClaw

95.76%
按下载量换算11,002

安全审计

VirusTotal

可疑

ClawScan

通过

Static analysis

未展示

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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