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:

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
```

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

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

multi-agent-image

NPX Install

Tags

SKILL.md Content

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

`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`