Agent skill

Multi Agent Image

by kangarooking in kangarooking/kangarooking-skills

Standalone multi-agent image generation skill for Hermes. An agent skill from kangarooking/kangarooking-skills.

Install Multi Agent Image

skills CLI

$ npx skills add kangarooking/kangarooking-skills --skill multi-agent-image -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install kangarooking/kangarooking-skills multi-agent-image --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/kangarooking/kangarooking-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/multi-agent-image .claude/skills/multi-agent-image && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "multi-agent-image" agent skill from https://github.com/kangarooking/kangarooking-skills/tree/main/multi-agent-image into .claude/skills/multi-agent-image/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "multi-agent-image", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/kangarooking/kangarooking-skills/tree/main/multi-agent-image

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add kangarooking/kangarooking-skills --skill multi-agent-image -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install kangarooking/kangarooking-skills multi-agent-image --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/kangarooking/kangarooking-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/multi-agent-image .agents/skills/multi-agent-image && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "multi-agent-image" agent skill from https://github.com/kangarooking/kangarooking-skills/tree/main/multi-agent-image into .agents/skills/multi-agent-image/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "multi-agent-image", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add kangarooking/kangarooking-skills --skill multi-agent-image -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install kangarooking/kangarooking-skills multi-agent-image --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/kangarooking/kangarooking-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/multi-agent-image .cursor/skills/multi-agent-image && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "multi-agent-image" agent skill from https://github.com/kangarooking/kangarooking-skills/tree/main/multi-agent-image into .cursor/skills/multi-agent-image/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "multi-agent-image", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/kangarooking/kangarooking-skills.git --path multi-agent-image

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add kangarooking/kangarooking-skills --skill multi-agent-image -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install kangarooking/kangarooking-skills multi-agent-image --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/kangarooking/kangarooking-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/multi-agent-image .gemini/skills/multi-agent-image && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "multi-agent-image" agent skill from https://github.com/kangarooking/kangarooking-skills/tree/main/multi-agent-image into .gemini/skills/multi-agent-image/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "multi-agent-image", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install kangarooking/kangarooking-skills multi-agent-image

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add kangarooking/kangarooking-skills --skill multi-agent-image -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/kangarooking/kangarooking-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/multi-agent-image .github/skills/multi-agent-image && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "multi-agent-image" agent skill from https://github.com/kangarooking/kangarooking-skills/tree/main/multi-agent-image into .github/skills/multi-agent-image/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "multi-agent-image", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add kangarooking/kangarooking-skills --skill multi-agent-image -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install kangarooking/kangarooking-skills multi-agent-image --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/kangarooking/kangarooking-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/multi-agent-image .opencode/skills/multi-agent-image && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "multi-agent-image" agent skill from https://github.com/kangarooking/kangarooking-skills/tree/main/multi-agent-image into .opencode/skills/multi-agent-image/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "multi-agent-image", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: multi-agent-image
GitHub stars: 656
Token cost: ~2.6k tokens
SKILL.md length: 895 words
Files: 20 (incl. scripts, references)
Skills in repo: 19
Repo updated: Sep 7, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Standalone multi-agent image generation skill for Hermes. An agent skill from kangarooking/kangarooking-skills.

Works in 8 steps: Deploy the skill → Install Python dependencies → Set API key → …
Tasks that involve Image generation
SKILL.md covers When to Use, Architecture, Setup and Core Components, plus 4 more sections
Runs Python scripts from its folder; calls python3 and pip; needs OPENAI_API_KEY

What it does

Multi Agent Image is an agent skill from kangarooking/kangarooking-skills. Standalone multi-agent image generation skill for Hermes. Includes an internal design compiler, GPT-Image-2 generation via apimart.ai, case library reuse, interactive reference selection, batch workflows, and style-consistent series generation.

Its SKILL.md is about 2.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 22 other files, including scripts and reference files (for example `README.md`, `references/agents/image_generator.md` and `references/agents/metadata_manager.md`).

It sits in Media & Creative, covering Image generation and Multi-agent orchestration. The repository describes itself as: My custom AI Agent skills. The licence is MIT.

When your agent uses it

Tasks that involve Image generation
Tasks that involve Multi-agent orchestration

Example prompts

“/multi-agent-image”

Requirements

Python 3
A credential in OPENAI_API_KEY

Workflow steps

8 steps, taken from the step headings in SKILL.md.

Deploy the skill
Install Python dependencies
Set API key
design_reasoning
compiled_brief
prompt
prepare text for the user
execute after the user chooses

What it can do on your machine

Read from SKILL.md and the folder at commit 08bbee0. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 11 files in scripts/ (Python, from the files we listed), which the agent can run.
Shell commands in SKILL.md call:
- python3
- pip
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
- OPENAI_API_KEY
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Multi Agent Image loads about 2.6k tokens when it runs, and up to ~5.3k if it reads all its reference files. Until then it costs about 66 tokens; SKILL.md has 895 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~66
When it runs · the whole SKILL.md, loaded when a task matches: ~2.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~5.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from kangarooking/kangarooking-skills at commit 08bbee0, republished under its MIT licence (© kangarooking). 895 words, ~2,551 tokens.

Download SKILL.mdSave it as .claude/skills/multi-agent-image/SKILL.md (or your agent's skills folder). This skill also uses 19 other files; get the full folder from GitHub.

name: multi-agent-image
description: Standalone multi-agent image generation skill for Hermes. Includes an internal design compiler, GPT-Image-2 generation via apimart.ai, case library reuse, interactive reference selection, batch workflows, and style-consistent series generation.
version: 3.0.0
author: Hermes Agent
license: MIT

Multi-Agent Image

multi-agent-image is a standalone Hermes skill for image generation workflows.

It is designed for cases where a simple one-line prompt is not enough. Instead of sending raw user input directly to an image model, this skill:

analyzes the request,
compiles it into a design-aware prompt,
generates through gpt-image-2,
archives the result,
and optionally reuses successful outputs as future style references.

This skill is independent at runtime. The design compiler is built into this repository and does not require an external skill.

When to Use

Use this skill when the user wants one or more of the following:

Design-oriented poster generation
Product images or ad visuals
PPT cover visuals or chapter art
Infographic-like or teaching/demo visuals
Style reference reuse from prior generations
Interactive “show examples first, then generate” flow
Batch generation for multiple directions or aspect ratios
Series generation where multiple images should share one visual language

Do not use this skill for:

pixel-accurate UI recreation
editable charts
exact typography output inside the image
tasks that require vector, HTML, or PPT-native assets rather than raster images

Architecture

text

User Request
    ↓
[Prompt Engineer]
    ↓
[Style Scout]
    ↓
[Internal Design Compiler]
    ↓
[GPT-Image-2 Generation]
    ↓
[QA + Archive]
    ↓
[Case Library]

Optional layers on top of the main path:

Interactive reference selection
Batch generation
Series generation

Setup

1. Deploy the skill

The skill source lives in:

bash

~/.hermes/skills/multi-agent-image/

Install runtime files into the working directory:

bash

python3 ~/.hermes/skills/multi-agent-image/scripts/install.py

This prepares:

~/.hermes/agents/multi-agent-image/output/
~/.hermes/agents/multi-agent-image/case_library/
agent role folders and memory files
local runtime scripts copied from the skill

2. Install Python dependencies

bash

pip install openai requests

3. Set API key

bash

export OPENAI_API_KEY="sk-..."

This key is used with the apimart-compatible GPT-Image-2 endpoints in this skill.

Core Components

`scripts/design_compiler.py`

Internal prompt compiler.

Responsibilities:

detect task type
choose defaults for aspect and quality
build design_reasoning
compress it into compiled_brief
produce the final generation prompt

This is the core logic that makes the skill independent.

`scripts/design_image.py`

CLI entrypoint for the internal compiler.

Use it when you want:

prompt-only output
a local design compilation test
direct generation without the full multi-agent workflow

Example:

bash

cd ~/.hermes/agents/multi-agent-image
python3 design_image.py \
  --task poster \
  --brief "AI训练营招生海报，强调速度、增长、实战" \
  --direction balanced \
  --aspect 3:4 \
  --prompt-only

It prints:

design_reasoning
compiled_brief
prompt
settings

`scripts/orchestrator_v2.py`

Main workflow entrypoint.

Responsibilities:

run prompt analysis
choose task and generation parameters
optionally select a reference from the case library
call the internal compiler
call GPT-Image-2
archive outputs
auto-save successful results into the case library

`scripts/gpt_image2_generator.py`

Low-level GPT-Image-2 client.

Responsibilities:

submit async generation tasks
poll task status
download image results

Use this when you want direct API access without the full workflow.

`scripts/case_library.py`

Persistent library of past generations.

Responsibilities:

save outputs by task type
store metadata and rating
search by brief, prompt, or tags
return image paths for reuse as references

`scripts/case_selector.py`

Interactive helper for Hermes dialogue flows.

Responsibilities:

render user-facing selection text
parse replies like 1, n, case_001, or 搜索蓝色

`scripts/interactive_run.py`

Two-phase dialogue wrapper.

Use it when the workflow needs to ask the user before generating.

`scripts/batch_generator_v2.py`

Batch generation entrypoint.

Supports:

same brief, multiple directions
same brief, multiple aspect ratios
multiple briefs in one run

`scripts/series_generator.py`

Style-consistent series generator.

Workflow:

generate a master image
extract style signals from its compiled brief
generate child images that follow the same visual system

`templates/linear_batch.py`

Editable template for resumable sequential runs.

Useful when you want:

explicit scene lists
filesystem-based progress monitoring
style propagation from the first generated image

Internal Design Compiler

The internal compiler produces three layers:

Show full SKILL.md (365 more words)Show less

1. `design_reasoning`

This captures design intent before generation.

Typical fields:

task
communication_goal
audience
channel
visual_system
hierarchy_strategy
safe_zone_strategy
lighting_strategy
palette_strategy
anti_filler_rules
anti_slop_rules

2. `compiled_brief`

This is a compressed design brief for generation.

It includes:

what the image is for
what should dominate visually
what space should remain available
what to avoid

3. `prompt`

Final model-facing prompt used for GPT-Image-2.

The prompt is generated from design logic, not just from a list of style keywords.

Supported Tasks

The built-in compiler understands these task classes:

poster
product
ppt
infographic
teaching
auto

Default aspect assumptions:

poster → 3:4
product → 1:1
ppt → 16:9
infographic → 4:3
teaching → 16:9

Direction modes:

conservative
balanced
bold

Quality modes:

draft
final
premium

Current generation channel:

gpt-image-2

Usage

Quick start

bash

cd ~/.hermes/agents/multi-agent-image
python3 quick_start.py "AI训练营招生海报，强调速度、增长、实战"

Prompt-only compilation

bash

cd ~/.hermes/agents/multi-agent-image
python3 design_image.py \
  --task product \
  --brief "高端陶瓷咖啡杯电商首图，温暖晨光，突出釉面质感" \
  --prompt-only

Full orchestrated generation

python

from orchestrator_v2 import run

run("AI训练营招生海报，强调速度增长实战")

Force task and visual settings

python

from orchestrator_v2 import run

run(
    "高端咖啡杯商品图",
    task="product",
    direction="balanced",
    aspect="1:1",
    quality="final",
    use_reference=False,
)

Interactive Workflow

Use the two-phase pattern when Hermes should ask before generating.

Phase 1: prepare text for the user

python

from interactive_run import prepare

text = prepare("帮我做张 AI 训练营海报", task="poster")
print(text)

Phase 2: execute after the user chooses

python

from interactive_run import execute

result = execute("帮我做张 AI 训练营海报", user_choice="1", task="poster")

Supported reply patterns:

1, 2, 3
n
y
case_001
搜索蓝色

Batch Generation

Same brief, multiple directions

python

from batch_generator_v2 import batch_styles

batch_styles("AI训练营海报", task="poster")

Same brief, multiple aspect ratios

python

from batch_generator_v2 import batch_aspects

batch_aspects("AI训练营海报", task="poster", aspects=["1:1", "16:9", "9:16"])

Multiple briefs

python

from batch_generator_v2 import batch_briefs

batch_briefs(["海报A", "海报B", "海报C"], task="poster")

Series Generation

Use this when several outputs should feel like the same campaign or product family.

python

from series_generator import SeriesGenerator

sg = SeriesGenerator()
sg.create_series(
    master_brief="AI训练营系列视觉，科技蓝，专业商务感",
    items=[
        {"name": "主海报", "brief": "AI训练营招生主海报", "aspect": "3:4"},
        {"name": "Banner", "brief": "官网 Banner", "aspect": "16:9"},
        {"name": "朋友圈", "brief": "朋友圈推广方形图", "aspect": "1:1"},
    ],
    task="poster",
    direction="balanced",
)

Case Library

Case library directory:

text

~/.hermes/agents/multi-agent-image/case_library/

Output directory:

text

~/.hermes/agents/multi-agent-image/output/

Typical case structure:

text

case_library/
├── poster/
│   └── case_001_example/
│       ├── image.png
│       └── metadata.json

Typical metadata fields:

case_id
task
brief
prompt
params
tags
rating

Validation Guidance

Before generating at scale, test prompt quality first:

bash

python3 design_image.py \
  --task poster \
  --brief "AI训练营招生海报，强调速度、增长、实战" \
  --direction balanced \
  --aspect 3:4 \
  --prompt-only

What to check:

Does design_reasoning state a clear communication goal?
Is there an explicit safe zone?
Is hierarchy obvious?
Do anti_slop_rules remove HUD overlays, fog, and generic clutter?
Does the prompt describe a single strong visual idea rather than a pile of elements?

Current Limits

Current image provider is centered on gpt-image-2
QA scoring is intentionally lightweight
Series generation is heavier than one-off generation
The skill is optimized for raster outputs, not editable assets
Some reference documents remain longer than necessary, but the main runtime path is consistent

Version History

v1.0.0 Initial multi-agent workflow for GPT-Image-2 generation
v2.0.0 Added case library, interactive reference selection, and image-to-image style reuse
v2.1.0 Added stronger download retry logic, batch workflows, and series generation
v2.2.0 Packaged as a reusable Hermes skill with install script and runtime layout
v3.0.0 Internalized the design compiler and removed external runtime dependency

Files

SKILL.md and 19 other files (scripts, references) in multi-agent-image of kangarooking/kangarooking-skills.

SKILL.md
README.md
references/agents/image_generator.md
references/agents/metadata_manager.md
references/agents/prompt_engineer.md
references/agents/qa_bot.md
references/agents/style_scout.md
scripts/batch_generator_v2.py
scripts/case_library.py
scripts/case_selector.py
scripts/design_compiler.py
scripts/design_image.py
scripts/gpt_image2_generator.py
scripts/install.py
scripts/interactive_run.py
scripts/orchestrator_v2.py
scripts/quick_start.py
scripts/series_generator.py
… and 2 more

Open the folder on GitHubat commit 08bbee0

Compare with similar skills

Multi Agent Image next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Multi Agent Image compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Multi Agent Image this skillkangarooking/kangarooking-skills	656	—	~2.6k	Automated safety check: Pass	MIT	1 mo ago
Gauntlet Loopduolahypercho/gauntlet-loop	163	—	~689	Automated safety check: Pass	MIT	2 mo ago
Agent Fleetyan-labs/yan-skills	208	—	~2.3k	Automated safety check: Pass	MIT	3 days ago
Design With Imagesdzhng/skills	1k	—	~1.1k	Automated safety check: Pass	MIT	yesterday
Design ReviewPrismer-AI/PrismerCloud	1.6k	—	~1.3k	Automated safety check: Notes	MIT	7 days ago
Cullglebis/claude-skills	388	—	~1.9k	Automated safety check: Pass	MIT	11 days ago

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kangarooking/kangarooking-skills
Generate and download images through APIMart's asynchronous GPT-Image-2 API.
656 GitHub stars~745 tokensUpdated 1 mo ago
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Bilibili Viral Topic
kangarooking/kangarooking-skills
Find low-follower viral Bilibili videos for account-growth topic selection.
656 GitHub stars~756 tokensUpdated 1 mo ago
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Viral Title
kangarooking/kangarooking-skills
Generate high-potential viral title candidates for content across WeChat public account articles, X/Twitter posts, YouTube videos, Bilibili videos, and similar content platforms.
656 GitHub stars~1.7k tokensUpdated 1 mo ago
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Wechat Viral Topic
kangarooking/kangarooking-skills
Find WeChat public-account articles that break out above the account's average reads for account-growth topic selection.
656 GitHub stars~914 tokensUpdated 1 mo ago
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Questions about Multi Agent Image

What does Multi Agent Image do?

Standalone multi-agent image generation skill for Hermes. An agent skill from kangarooking/kangarooking-skills. Multi Agent Image is an agent skill from kangarooking/kangarooking-skills. Standalone multi-agent image generation skill for Hermes.

When should I use Multi Agent Image?

Multi Agent Image fits situations like: tasks that involve Image generation; tasks that involve Multi-agent orchestration.

How do I install Multi Agent Image in Claude Code?

Run `npx skills add kangarooking/kangarooking-skills --skill multi-agent-image -a claude-code`. Or copy the skill folder (multi-agent-image in kangarooking/kangarooking-skills) into .claude/skills/multi-agent-image in your project. Claude Code loads it when a task matches its description.

How do I install Multi Agent Image in Codex?

Run `npx skills add kangarooking/kangarooking-skills --skill multi-agent-image -a codex`. Or copy the skill folder (multi-agent-image in kangarooking/kangarooking-skills) into .agents/skills/multi-agent-image in your project. Codex loads it when a task matches its description.

Can I use Multi Agent Image in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add kangarooking/kangarooking-skills --skill multi-agent-image -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/multi-agent-image, .gemini/skills/multi-agent-image, .github/skills/multi-agent-image and .opencode/skills/multi-agent-image in your project.

What does Multi Agent Image need to run?

Going by SKILL.md and its folder, Multi Agent Image needs Python for the scripts in its folder, the command-line tools its instructions call (python3 and pip) and credentials named OPENAI_API_KEY. Our summary lists: Python 3; A credential in OPENAI_API_KEY.

Does Multi Agent Image access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Multi Agent Image safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Multi Agent Image use?

Multi Agent Image is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Multi Agent Image use?

About 2.6k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.7k tokens, read only when the agent opens those files.

What are the alternatives to Multi Agent Image?

Skills that share tags, products or a category with Multi Agent Image: Gauntlet Loop (duolahypercho/gauntlet-loop, 163 stars), Agent Fleet (yan-labs/yan-skills, 208 stars), Design With Images (dzhng/skills, 1k stars) and Design Review (Prismer-AI/PrismerCloud, 1.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Multi Agent Image?

kangarooking (a GitHub user) maintains it in kangarooking/kangarooking-skills, which has 656 GitHub stars. The repository holds 19 skills in this directory. The repository was last updated on September 7, 2026.

Source: kangarooking/kangarooking-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Multi Agent Image

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Multi-Agent Image

When to Use

Architecture

Setup

1. Deploy the skill

2. Install Python dependencies

3. Set API key

Core Components

scripts/design_compiler.py

scripts/design_image.py

scripts/orchestrator_v2.py

scripts/gpt_image2_generator.py

scripts/case_library.py

scripts/case_selector.py

scripts/interactive_run.py

scripts/batch_generator_v2.py

scripts/series_generator.py

templates/linear_batch.py

Internal Design Compiler

1. design_reasoning

2. compiled_brief

3. prompt

Supported Tasks

Usage

Quick start

Prompt-only compilation

Full orchestrated generation

Force task and visual settings

Interactive Workflow

Phase 1: prepare text for the user

Phase 2: execute after the user chooses

Batch Generation

Same brief, multiple directions

Same brief, multiple aspect ratios

Multiple briefs

Series Generation

Case Library

Validation Guidance

Current Limits

Version History

Files

Compare with similar skills

Similar skills

More from kangarooking/kangarooking-skills

Related

Questions about Multi Agent Image

`scripts/design_compiler.py`

`scripts/design_image.py`

`scripts/orchestrator_v2.py`

`scripts/gpt_image2_generator.py`

`scripts/case_library.py`

`scripts/case_selector.py`

`scripts/interactive_run.py`

`scripts/batch_generator_v2.py`

`scripts/series_generator.py`

`templates/linear_batch.py`

1. `design_reasoning`

2. `compiled_brief`

3. `prompt`