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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.
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.
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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:
gpt-image-2,This skill is independent at runtime. The design compiler is built into this repository and does not require an external skill.
Use this skill when the user wants one or more of the following:
Do not use this skill for:
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:
The skill source lives in:
~/.hermes/skills/multi-agent-image/
Install runtime files into the working directory:
python3 ~/.hermes/skills/multi-agent-image/scripts/install.py
This prepares:
~/.hermes/agents/multi-agent-image/output/~/.hermes/agents/multi-agent-image/case_library/pip install openai requests
export OPENAI_API_KEY="sk-..."
This key is used with the apimart-compatible GPT-Image-2 endpoints in this skill.
scripts/design_compiler.pyInternal prompt compiler.
Responsibilities:
design_reasoningcompiled_briefThis is the core logic that makes the skill independent.
scripts/design_image.pyCLI entrypoint for the internal compiler.
Use it when you want:
Example:
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_reasoningcompiled_briefpromptsettingsscripts/orchestrator_v2.pyMain workflow entrypoint.
Responsibilities:
scripts/gpt_image2_generator.pyLow-level GPT-Image-2 client.
Responsibilities:
Use this when you want direct API access without the full workflow.
scripts/case_library.pyPersistent library of past generations.
Responsibilities:
scripts/case_selector.pyInteractive helper for Hermes dialogue flows.
Responsibilities:
1, n, case_001, or 搜索蓝色scripts/interactive_run.pyTwo-phase dialogue wrapper.
Use it when the workflow needs to ask the user before generating.
scripts/batch_generator_v2.pyBatch generation entrypoint.
Supports:
scripts/series_generator.pyStyle-consistent series generator.
Workflow:
templates/linear_batch.pyEditable template for resumable sequential runs.
Useful when you want:
The internal compiler produces three layers:
design_reasoningThis captures design intent before generation.
Typical fields:
taskcommunication_goalaudiencechannelvisual_systemhierarchy_strategysafe_zone_strategylighting_strategypalette_strategyanti_filler_rulesanti_slop_rulescompiled_briefThis is a compressed design brief for generation.
It includes:
promptFinal model-facing prompt used for GPT-Image-2.
The prompt is generated from design logic, not just from a list of style keywords.
The built-in compiler understands these task classes:
posterproductpptinfographicteachingautoDefault aspect assumptions:
poster → 3:4product → 1:1ppt → 16:9infographic → 4:3teaching → 16:9Direction modes:
conservativebalancedboldQuality modes:
draftfinalpremiumCurrent generation channel:
gpt-image-2cd ~/.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"
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
from orchestrator_v2 import run
run("AI训练营招生海报，强调速度增长实战")
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,
)
Use the two-phase pattern when Hermes should ask before generating.
from interactive_run import prepare
text = prepare("帮我做张 AI 训练营海报", task="poster")
print(text)
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, 3nycase_001搜索蓝色from batch_generator_v2 import batch_styles
batch_styles("AI训练营海报", task="poster")
from batch_generator_v2 import batch_aspects
batch_aspects("AI训练营海报", task="poster", aspects=["1:1", "16:9", "9:16"])
from batch_generator_v2 import batch_briefs
batch_briefs(["海报A", "海报B", "海报C"], task="poster")
Use this when several outputs should feel like the same campaign or product family.
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 directory:
~/.hermes/agents/multi-agent-image/case_library/
Output directory:
~/.hermes/agents/multi-agent-image/output/
Typical case structure:
case_library/
├── poster/
│ └── case_001_example/
│ ├── image.png
│ └── metadata.json
Typical metadata fields:
case_idtaskbriefpromptparamstagsratingBefore generating at scale, test prompt quality first:
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:
design_reasoning state a clear communication goal?anti_slop_rules remove HUD overlays, fog, and generic clutter?gpt-image-2v1.0.0 Initial multi-agent workflow for GPT-Image-2 generationv2.0.0 Added case library, interactive reference selection, and image-to-image style reusev2.1.0 Added stronger download retry logic, batch workflows, and series generationv2.2.0 Packaged as a reusable Hermes skill with install script and runtime layoutv3.0.0 Internalized the design compiler and removed external runtime dependencyname: 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
metadata:
hermes:
tags: [image-generation, multi-agent, gpt-image-2, apimart, design-compiler, case-library, batch, series]
related_skills: [stable-diffusion]---
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
metadata:
hermes:
tags: [image-generation, multi-agent, gpt-image-2, apimart, design-compiler, case-library, batch, series]
related_skills: [stable-diffusion]
---
# 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:
1. analyzes the request,
2. compiles it into a design-aware prompt,
3. generates through `gpt-image-2`,
4. archives the result,
5. 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:
1. generate a master image
2. extract style signals from its compiled brief
3. 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:
### 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
Skill source recorded
Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.
Review before install: Avoid automatic install
License: MIT
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
Version reported in registry metadata; check source releases before relying on it.
Quality
74/100
Strong
Trust
57/100
Do not auto-install
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
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"label": "No agent outcome data yet"
},
"auto_install": {
"allowed": false,
"sandbox_required": true,
"reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
},
"best_for": [
"design-creative",
"agent-skill"
],
"known_risks": [
"The skill sends OPENAI_API_KEY to apimart.ai, a third-party endpoint; this should be clearly documented and users should use a dedicated/restricted key.",
"Quality score needs review",
"Permission surface needs review: secrets or environment access, shell or command execution",
"Dependency/runtime risk: command execution surface, credential or environment access",
"Permission surface: secrets or environment access, shell or command execution"
]
},
"agent_proven": {
"version": "agent-proven-v1",
"score": 0,
"tier": "unproven",
"label": "Needs first agent run",
"summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
"metrics": {
"totalOutcomes": 0,
"successfulOutcomes": 0,
"failedOutcomes": 0,
"installAttempts": 0,
"installSuccessRate": null,
"successRate": null,
"recentSuccessRate": null,
"recentFailureRate": null,
"riskBlocked": 0,
"setupRequired": 0,
"notRelevant": 0,
"avgOutputQuality": null,
"avgTimeToUsefulMs": null,
"productionOutcomes": 0,
"humanReviewRequired": 0,
"uniqueAgents": 0,
"lastOutcomeAt": null
},
"signals": [],
"penalties": [
"No real agent outcome evidence yet"
]
},
"audit": {
"score": 76,
"risk_level": "needs_review",
"risk_label": "Needs review",
"warnings": [
"Dependency or permission surface needs review",
"Permission surface may require sandboxing",
"The skill sends OPENAI_API_KEY to apimart.ai, a third-party endpoint; this should be clearly documented and users should use a dedicated/restricted key.",
"Several reference agent files (prompt_engineer.md, qa_bot.md) still describe Stable Diffusion-era parameters such as negative prompt, cfg_scale, and sampler, which are not aligned with the current GPT-Image-2 pipeline.",
"SKILL.md does not document error handling, retry/timeout behavior, or failure modes for the external generation API.",
"Quality score needs review",
"Permission surface needs review: secrets or environment access, shell or command execution",
"Dependency/runtime risk: command execution surface, credential or environment access"
]
},
"safety_gate": {
"tier": "blocked",
"label": "Blocked for auto-install",
"auto_install_policy": "block",
"auto_install_allowed": false,
"human_review_required": true,
"blocked": true,
"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
},
"quality": {
"score": 74,
"label": "Strong"
},
"supply": {
"track": "Design and creative production",
"scenario": "Design and creative",
"maintenance": "17d since push",
"risk": "Needs review"
},
"alternative_skills": [
{
"slug": "emilkowalski-apple-design",
"name": "Apple Design",
"url": "https://www.openagentskill.com/skills/emilkowalski-apple-design",
"stars": 34452,
"install_command": "npx skills@latest add emilkowalski/skills",
"trust_score": 94,
"audit_score": 96
}
],
"do_not_use_when": [
"teams that need a vendor-supported SLA",
"production agents without a repository review",
"The skill sends OPENAI_API_KEY to apimart.ai, a third-party endpoint; this should be clearly documented and users should use a dedicated/restricted key.",
"No OpenAgentSkill engagement data yet",
"High-risk permission hints: Shell or command execution, Secrets or environment access",
"Dependency or permission surface needs review",
"Permission surface may require sandboxing",
"Several reference agent files (prompt_engineer.md, qa_bot.md) still describe Stable Diffusion-era parameters such as negative prompt, cfg_scale, and sampler, which are not aligned with the current GPT-Image-2 pipeline."
],
"agent_contract": {
"task_input": "Use multi-agent-image in an agent workflow",
"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
"install_policy": "block",
"minimum_review_before_use": [
"Trust: 65/100 Manual review",
"Audit: 76/100 Needs review",
"Safety: 36/100 Avoid automatic install",
"Review repository, license, install command, and permission surface before production use."
],
"expected_agent_output": {
"selected_skill": "kangarooking-multi-agent-image (multi-agent-image)",
"install_command": "npx skills add kangarooking/kangarooking-skills --skill multi-agent-image",
"risk_summary": "Needs review; Blocked for auto-install; Review before production",
"verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
}
},
"outcome_feedback": {
"endpoint": "https://www.openagentskill.com/api/agent/outcome",
"method": "POST",
"requires_resolve_event_id": true,
"event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
"expected_outcomes": [
"success",
"failed",
"not_relevant",
"blocked_by_risk",
"setup_required"
],
"payload_template": {
"event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
"skill_slug": "kangarooking-multi-agent-image",
"task": "Use multi-agent-image in an agent workflow",
"agent": "codex",
"outcome": "success",
"install_used": true,
"risk_blocked": false,
"setup_required": false,
"task_success": true,
"output_quality": 4,
"error_type": null,
"human_review_required": false,
"workspace": "sandbox",
"time_to_useful_ms": 120000,
"notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
}
},
"endpoints": {
"web": "https://www.openagentskill.com/skills/kangarooking-multi-agent-image",
"api": "https://www.openagentskill.com/api/agent/skills/kangarooking-multi-agent-image",
"audit": "https://www.openagentskill.com/skills/kangarooking-multi-agent-image/audit",
"eval": "https://www.openagentskill.com/api/agent/evals?slug=kangarooking-multi-agent-image&task=Use%20multi-agent-image%20in%20an%20agent%20workflow&max_risk=medium",
"resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20multi-agent-image%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20multi-agent-image%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/kangarooking-multi-agent-image/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/kangarooking-multi-agent-image"
}
}Listing source
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Audit
76/100
Needs review
Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.