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Agent Skill · 运维

auto-midjourney(汽车中途)

auto-midjourney 用于辅助安全审计、权限检查和凭据风险排查,适合在 OpenClaw 中需要复核安全边界、认证流程或敏感配置时使用。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。

auto-midjourney 是运维类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描有提醒项,按安装量在运维类的 954 个 Skill 里排第 629。

总安装 4,229周安装 178GitHub Stars 公开资料未说明下载 1,481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
0.1.2

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:auto-midjourney(汽车中途)
来源仓库:https://github.com/standed/auto-midjourney
安装命令:
openclaw skills install auto-midjourney
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 auto-midjourney

这个 Skill 是做什么的

运维类 Skill 处理部署、监控、日志和云资源,多数需要较高的执行权限。

auto-midjourney 用于辅助安全审计、权限检查和凭据风险排查,适合在 OpenClaw 中复核敏感配置时使用。支持 Midjourney Alpha 图像生成,适用于创意内容生产。

通过 clawhub 安装,命令为 openclaw skills install auto-midjourney,需结合来源仓库进一步确认具体用法。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操作。当前功能描述基于原始 README,实际能力以官方文档为准。

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 auto-midjourney;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

    它需要接触敏感数据,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

auto-midjourney 怎么安装?

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

auto-midjourney 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。

auto-midjourney 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

auto-midjourney 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。

auto-midjourney 用的人多吗?

公开数据是累计安装 4,229 次,近一周安装 178 次,下载 1,481 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。

auto-midjourney 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

  • 需要接触敏感数据,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。
  • 需要处理运维类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

运维类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
docker-essentials

Docker 基础 · OpenClaw

1,005,15130接触敏感数据MIT-0通过
playwright-scraper-skill

Playwright scraper 技能 · OpenClaw

923,55754接触敏感数据MIT-0有提醒
moltguard

moltguard 搜索 · OpenClaw

825,096110接触敏感数据MIT-0通过
finance-news

财经新闻 · OpenClaw

275,51210需要联网MIT-0有提醒
security-monitor

安全监视器 · OpenClaw

224,1695需要联网MIT-0有提醒
auto-midjourney

汽车中途 · 本页

4,229—接触敏感数据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
auto-midjourney
description
Automate Midjourney Alpha web image generation from Claude using the authenticated https://alpha.midjourney.com session. Use this skill whenever the user wants to create Midjourney images, submit MJ prompts, default to Midjourney v8, optimize Midjourney prompt syntax, or poll/download results from the Midjourney web app instead of Discord. Also use it when the user shares Alpha web request samples, cookies, channel IDs, or asks to reverse-engineer the Midjourney website workflow.
compatibility

Auto Midjourney

Use the user's own Midjourney Alpha web session to submit imagine jobs and optionally poll for results.

This skill is intended for conservative, user-triggered assistance rather than unattended bulk automation.

What this skill does

  • Submits prompts to https://alpha.midjourney.com/api/submit-jobs
  • Defaults to Midjourney v8 for new prompts unless the user explicitly requests another version
  • Keeps credentials in environment variables instead of hardcoding them into the skill
  • Supports a one-command flow through scripts/run_imagine.py
  • Can read https://alpha.midjourney.com/api/user-mutable-state to inspect current web settings
  • Can infer user_id and singleplayer_<midjourney_id> from the authenticated cookie
  • Applies local conservative throttling to reduce accidental request bursts
  • Includes scripts/mj_doctor.py for setup validation
  • Includes an experimental recent-jobs reader
  • Supports browser transport backed by Chrome DevTools Protocol, with Playwright-over-CDP preferred when installed
  • Supports optional polling once a job-status endpoint has been confirmed
  • Includes prompt-craft guidance and reusable scenario presets for better prompt writing
  • Includes a structured prompt builder and opt-in quality profiles
  • Includes dedicated guidance for character sheets, split-view turnarounds, and reusable design assets

Current scope

This version focuses on:

  1. imagine
  2. Midjourney v8 as the default version
  3. Alpha web flow, not Discord bot flow
  4. Safe local configuration via .env
  5. Easier operation through inferred IDs, presets, and doctor checks

Implemented:

  • imagine submit through the Alpha web flow
  • browser-backed verification using low-impact CDP network watching plus page asset fallback
  • local download of the 4 returned image assets
  • optional browser-side conversion from webp to png during download
  • sequential batch generation

Not implemented yet:

  • Upscale / variation / reroll button actions
  • Image upload / reference-image workflow
  • Automatic result download from a confirmed final image endpoint

Those should be added only after capturing stable request samples from the browser.

Safety posture

Do not optimize this skill for bypassing restrictions, hiding automation, rotating accounts, or mass unattended generation.

Use these guardrails instead:

  • trigger requests manually
  • keep request frequency low
  • leave local throttling enabled
  • validate config with mj_doctor.py
  • prefer one human action to one live submit

The goal is risk reduction through conservative usage, not evasion.

Trigger rules

Use this skill proactively when the user asks to:

  • “用 Midjourney / MJ 出图”
  • “用 Midjourney v8 生成”
  • “帮我提交 imagine”
  • “优化 Midjourney prompt”
  • “做角色设定稿 / 四视图 / 角色资产图”
  • “抓 Midjourney Alpha 网站请求”
  • “轮询 Midjourney job 状态”
  • “把网页版 Midjourney 做成自动化能力”

Required configuration

Read these values from .env or shell environment:

VariableRequiredPurpose
MJ_COOKIEYesFull authenticated Cookie header copied from browser
MJ_CHANNEL_IDYesAlpha web singleplayer channel ID
MJ_STATUS_URL_TEMPLATENoJob status endpoint template containing {job_id}
MJ_USER_STATE_PATHNoDefaults to /api/user-mutable-state
MJ_RECENT_JOBS_URLNoExperimental recent-jobs endpoint
MJ_MODENofast by default
MJ_PRIVATENotrue by default
MJ_MIN_SUBMIT_INTERVAL_SECONDSNoLocal minimum spacing between submits. Default is 3 seconds
MJ_MAX_SUBMITS_PER_HOURNoLocal hourly cap. Set 0 to disable, which is now the default
MJ_MAX_SUBMITS_PER_DAYNoLocal daily cap. Set 0 to disable, which is now the default
MJ_USER_IDNoUsually inferred from the auth cookie
MJ_METRICS_TOKENNoOptional token observed on telemetry requests
MJ_BROWSER_BACKENDNoauto by default. Set playwright or cdp to force a backend

Never write real cookies or tokens into SKILL.md, reference files, git-tracked scripts, or user-facing summaries.

System requirements

For platform and device requirements, read system-requirements.md.

Workflow

Scenario 0: Check config first

Run:

python3 scripts/mj_doctor.py --fetch-user-state --transport browser

This shows:

  • whether the cookie exists
  • inferred midjourney_id
  • inferred channel_id
  • current server-side speed and visibility
  • current local safe-limit settings

Scenario 1: Submit one prompt

Run:

python3 scripts/run_imagine.py "1 girl --ar 16:9" --transport browser

Default behavior:

  • Appends --v 8 if the prompt does not already specify a version
  • Appends --raw by default unless the user disables it
  • Uses MJ_MODE and MJ_PRIVATE from the environment
  • Can sync server-side defaults before submitting
  • Records the submit locally and enforces conservative pacing
  • Prints structured JSON with the request payload, submission response, and extracted job_id

For simplest live use:

python3 scripts/run_imagine.py "cinematic portrait of a fox astronaut" --transport browser --sync-user-state --wait-page-assets --download --convert-to png

When --wait-page-assets is enabled, the browser transport now prefers watching Midjourney's existing in-page network traffic for the submitted job_id. It falls back to page asset probing only if the low-impact watcher does not yield 4 images.

Scenario 2: Submit and wait

If a working status endpoint has been captured and stored in MJ_STATUS_URL_TEMPLATE, run:

python3 scripts/run_imagine.py "cinematic portrait of a fox astronaut --ar 16:9" --wait

Scenario 3: Low-risk debugging

Use dry-run first when changing payload structure:

python3 scripts/run_imagine.py "robot barista in tokyo alley" --dry-run

This validates prompt normalization and payload generation without sending a live request.

Scenario 3b: Use a preset

Run:

python3 scripts/run_imagine.py "silver perfume bottle on black glass" --preset product --sync-user-state

Preset definitions live in config/presets.example.json.

When the user wants better prompt wording, templates, or parameter tradeoffs, read prompt-craft.md.

Scenario 3c: Build a prompt from a template

Run:

python3 scripts/mj_prompt_helper.py --template product --subject "premium silver perfume bottle" --camera "front three-quarter angle" --surface "black glass surface" --lighting "controlled softbox rim light" --background "dark charcoal background" --mood "minimal luxury beauty campaign" --preset product_clean_square --quality-profile final_v8_q4 --json

This produces a V8-friendly prompt string and a ready-to-run run_imagine.py command.

Scenario 4: Read current web settings

Run:

python3 scripts/get_user_state.py --transport browser

This reads the same user-mutable-state endpoint the web app uses and returns values such as:

  • settings.speed
  • settings.visibility
  • abilities
  • saved macros

Command reference

Submit only

python3 scripts/submit_job.py "minimalist glass monolith --ar 16:9 --v 8"

Poll one job

python3 scripts/poll_job.py "<job_id>"

Inspect current server-side settings

python3 scripts/get_user_state.py --transport browser

Validate config and inferred identity

python3 scripts/mj_doctor.py --fetch-user-state --transport browser

Experimental recent jobs lookup

python3 scripts/list_recent_jobs.py --amount 10

Submit, verify, and download locally

python3 scripts/run_imagine.py "fashion editorial, silver fabric, studio light --ar 3:4" --transport browser --sync-user-state --wait-page-assets --download --convert-to png

Batch generate and store PNGs

python3 scripts/batch_generate.py config/prompts.example.txt --transport browser --sync-user-state --convert-to png

Use conservative fixed spacing and batch cooldowns when needed:

python3 scripts/batch_generate.py config/prompts.example.txt --transport browser --sync-user-state --convert-to png --batch-size 5 --submit-interval-seconds 120 --batch-cooldown-seconds 600

Apply an opt-in quality profile to the whole batch:

python3 scripts/batch_generate.py config/prompts.example.txt --transport browser --sync-user-state --quality-profile final_v8_q4 --convert-to png

Convert existing WEBP downloads to PNG

python3 scripts/convert_downloads.py outputs

Or write converted files into a separate directory:

python3 scripts/convert_downloads.py outputs --output-dir outputs/png-converted

If you want to rebuild PNGs from a saved result manifest instead of local files:

python3 scripts/convert_downloads.py outputs/live-test/recent-job.json --output-dir outputs/png-rebuilt

This waits for the submitted job_id to appear in the browser page resource list, verifies that 4 Midjourney CDN image URLs exist, and downloads the returned image files into outputs/<job_id>/.

Batch generate sequentially

Create a text file with one prompt per line, then run:

python3 scripts/batch_generate.py prompts.txt --transport browser --sync-user-state --download-dir outputs/batch

This submits prompts one by one, waits for each job_id to produce page resource URLs, and downloads the returned image files before moving to the next prompt.

Full flow

python3 scripts/run_imagine.py "fashion editorial, silver fabric, studio light --ar 3:4" --sync-user-state --wait-recent-jobs --download

Prompt defaults

When the user does not specify MJ flags:

  • Default to --v 8
  • Default to --raw
  • Preserve any explicit aspect ratio or style flags already present
  • Do not auto-add --hd
  • Treat --q 4 as an opt-in final-pass override rather than a default for the current V8-focused flow

Do not silently override explicit user flags.

Output format

When you use this skill, report back in this structure:

Prompt: <final prompt sent>
Job ID: <job_id or "not returned">
Mode: <fast/relax/etc>
Visibility: <private/public>
Status: <submitted / polled / failed / dry-run>
Notes: <missing status endpoint, saved JSON path, or next step>

Simpler usage model

For day-to-day use, prefer this sequence:

  1. python3 scripts/mj_doctor.py --fetch-user-state --transport browser
  2. python3 scripts/run_imagine.py "<prompt>" --transport browser --sync-user-state
  3. python3 scripts/run_imagine.py "<prompt>" --transport browser --sync-user-state --wait-page-assets --download

This reduces manual mistakes and keeps you in a low-frequency workflow. On this machine, browser transport is the preferred live path because raw HTTP requests are blocked by Cloudflare.

Success criteria for a usable generation

Treat a generation as verified only when all of these are true:

  1. submit response contains a job_id
  2. page resource entries contain 4 CDN image URLs for the same job_id
  3. the image files are downloaded successfully to local disk

Normal Midjourney imagine behavior is a 4-image grid. Depending on the endpoint response, you may get one grid image file rather than four separately cropped files. This skill currently verifies and downloads the returned image assets as provided by Midjourney.

Extension path

After the user captures more browser requests, extend in this order:

  1. Confirm job-status endpoint and final-image fields
  2. Add result downloader
  3. Add button-action support for upscale / variation
  4. Add reference image upload flow
  5. Add prompt-template helpers for common MJ styles

Confirmed non-status endpoints

  • POST https://proxima.midjourney.com/ is currently treated as telemetry ingestion
  • POST /api/v1/traces is currently treated as tracing/observability data
  • GET /api/user-mutable-state is useful for reading current speed and visibility defaults

Do not mistake telemetry endpoints for job-status APIs.

GitHub-informed but experimental path

The skill includes an experimental recent-jobs reader based on public GitHub reverse-engineering notes. Treat it as best-effort support rather than a stable API contract.

平台分布

OpenClaw

85.72%
按下载量换算1,270

安全审计

VirusTotal

可疑

ClawScan

可疑

Static analysis

可疑

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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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