What makes Image Gen notable?

1.4K downloads. Trust evidence available

How should Image Gen be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

Image Gen

Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparen...

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.4k

Updated

Oct 10, 2026

Version

1.0.1

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.4K downloads reported by the source. Last updated 10/10/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 10, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 10, 2026
Adoption signal: 1.4K downloadsadoption · observed Oct 10, 2026
Latest release: 1.0.1release · observed Jun 10, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s170hyv1nagjajq3y5c6kpzx3s84jp29:imagegen

Install using `clawhub skill install s170hyv1nagjajq3y5c6kpzx3s84jp29:imagegen` in an isolated environment before connecting it to live workloads.
No published capability contract is available yet, so validate auth and request/response behavior manually.
Review the upstream CLAWHUB listing at https://clawhub.ai/zack-dev-cm/imagegen before using production credentials.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-zack-dev-cm-imagegen/snapshot"

Documentation

CLAWHUB

134,646 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: "imagegen"
description: "Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas."
---

# Image Generation Skill

Use this skill when the user needs a bitmap image rather than repo-native code,
SVG, HTML, CSS, canvas, or an existing vector/icon system.

This public ClawHub release is instruction-only. It does not bundle executable
API helpers, dependencies, or generated assets. In Codex sessions, use the
available built-in image generation or image editing tool. In other agent
hosts, use the host's approved image-generation capability and keep the same
prompting and verification standards.

## When To Use

- Generate a new raster image: product shot, hero image, concept art, cover,
sprite, texture, UI mockup, infographic, or educational visual.
- Edit an existing image while preserving important invariants such as identity,
product shape, text, lighting direction, or composition.
- Derive visual variants from supplied reference images.
- Produce multiple related bitmap assets when each output has a distinct prompt
or role.

## When Not To Use

- The requested asset should be an SVG, icon font, HTML/CSS composition, canvas
graphic, or repo-native component.
- The repo already has an editable vector/logo/icon system that should be
extended directly.
- The user asks for deterministic code-native output rather than generated
imagery.

## Workflow

1. Decide intent: `generate` for a new image, `edit` for changing an existing
image while preserving parts of it.
2. Decide whether the image is preview-only or project-bound.
3. Label every input image by role: edit target, reference image, style source,
insert, or supporting context.
4. Normalize the prompt into a compact production spec. Preserve user
constraints and avoid adding unrelated characters, brands, slogans, or story
elements.
5. Use the host-provided image generation/editing tool. For distinct assets,
make separate tool calls or jobs rather than relying on variants of one
prompt.
6. Inspect the output for subject accuracy, composition, text rendering,
style fit, prohibited content, and requested invariants.
7. Iterate with one targeted change when needed.
8. For project-bound assets, place the selected final artifact in the workspace
and update consuming references. Never leave a project-referenced final image
only in a host default output directory.
9. Report final saved path(s), whether the output is preview-only or
project-bound, and t

_meta.json

{
  "ownerId": "kn7dhjt1k1f111whp13fmrqwnh81tn1v",
  "slug": "imagegen",
  "version": "1.0.1",
  "publishedAt": 1781097222579
}

references/prompting.md

# Prompting best practices

These prompting principles are shared by both top-level modes of the skill:
- built-in `image_gen` tool (default)
- explicit `scripts/image_gen.py` CLI fallback

This file is about prompt structure, specificity, and iteration. Fallback-only execution controls such as `quality`, `input_fidelity`, masks, output format, and output paths live in the fallback docs.

## Contents
- [Structure](#structure)
- [Specificity policy](#specificity-policy)
- [Allowed and disallowed augmentation](#allowed-and-disallowed-augmentation)
- [Composition and layout](#composition-and-layout)
- [Constraints and invariants](#constraints-and-invariants)
- [Text in images](#text-in-images)
- [Input images and references](#input-images-and-references)
- [Iterate deliberately](#iterate-deliberately)
- [Transparent images](#transparent-images)
- [Fallback-only execution controls](#fallback-only-execution-controls)
- [Use-case tips](#use-case-tips)
- [Where to find copy/paste recipes](#where-to-find-copypaste-recipes)

## Structure
- Use a consistent order: scene/backdrop -> subject -> key details -> constraints -> output intent.
- Include intended use (ad, UI mock, infographic) to set the level of polish.
- For complex requests, use short labeled lines instead of one long paragraph.

## Specificity policy
- If the user prompt is already specific and detailed, normalize it into a clean spec without adding creative requirements.
- If the prompt is generic, you may add tasteful detail when it materially improves the output.
- Treat examples in `sample-prompts.md` as fully-authored recipes, not as the default amount of augmentation to add to every request.
- For photorealism, include `photorealistic` directly when that is the goal, plus concrete real-world texture such as pores, wrinkles, fabric wear, material grain, or imperfect everyday detail.

## Allowed and disallowed augmentation

Allowed augmentation for generic prompts:
- composition and framing cues
- intended-use or polish-level hints
- practical layout guidance
- reasonable scene concreteness that supports the request

Do not add:
- extra characters, props, or objects that are not implied
- brand palettes, slogans, or story beats that are not implied
- arbitrary side-specific placement unless the surrounding layout supports it

## Composition and layout
- Specify framing and viewpoint (close-up, wide, top-down) and placement only when it materially helps.
- Call out negative space if the asset clearly needs room for UI or copy.
- Avoid making left/right layout decisions unless the user or surrounding layout supports them.
- For people, describe body framing, scale, gaze, and object interactions when they matter (`full body visible`, `looking down at the book`, `hands naturally gripping the handlebars`).

## Constraints and invariants
- State what must not change (`keep background unchanged`).
- For edits, say `change only X; keep Y unchanged` and repeat invariants on every iteration to reduc

references/sample-prompts.md

# Sample prompts (copy/paste)

These prompt recipes are shared across both top-level modes of the skill:
- built-in `image_gen` tool (default)
- `scripts/image_gen.py` CLI fallback for explicit CLI/API/model requests or user-confirmed true-transparent-output fallback requests

Use these as starting points. They are intentionally complete prompt recipes, not the default amount of augmentation to add to every user request.

When adapting a user's prompt:
- keep user-provided requirements
- only add detail according to the specificity policy in `SKILL.md`
- do not treat every example below as permission to invent extra story elements

The labeled lines are prompt scaffolding, not a closed schema. `Asset type` and `Input images` are prompt-only scaffolding; the CLI does not expose them as dedicated flags.

Execution details such as explicit CLI flags, `quality`, `input_fidelity`, masks, output formats, and local output paths depend on mode. Use the built-in tool by default, including simple transparent-image requests. For transparent images, prompt for a flat chroma-key background and remove it locally with `python "${CODEX_HOME:-$HOME/.codex}/skills/.system/imagegen/scripts/remove_chroma_key.py"`; only apply CLI-specific controls when the user explicitly opts into fallback mode or explicitly confirms that the transparent request should use true CLI transparency.

CLI model notes:
- `gpt-image-2` is the fallback CLI default for new workflows.
- `gpt-image-2` supports `quality` values `low`, `medium`, `high`, and `auto`.
- For 4K-style `gpt-image-2` output, use `3840x2160` or `2160x3840`.
- If transparent output needs true CLI fallback, ask before using `gpt-image-1.5` unless the user already explicitly requested `gpt-image-1.5`, `scripts/image_gen.py`, or CLI fallback. Explain that built-in chroma-key removal is the default path, but `gpt-image-2` does not support `background=transparent`.
- Do not set `input_fidelity` with `gpt-image-2`; image inputs already use high fidelity.

For prompting principles (structure, specificity, invariants, iteration), see `references/prompting.md`.

## Generate

### photorealistic-natural
```
Use case: photorealistic-natural
Primary request: candid photo of an elderly sailor on a small fishing boat adjusting a net
Scene/backdrop: coastal water with soft haze
Subject: weathered skin with wrinkles and sun texture
Style/medium: photorealistic candid photo
Composition/framing: medium close-up, eye-level
Lighting/mood: soft coastal daylight, shallow depth of field, subtle film grain
Materials/textures: real skin texture, worn fabric, salt-worn wood
Constraints: natural color balance; no heavy retouching; no glamorization; no watermark
Avoid: studio polish; staged look
```

### product-mockup
```
Use case: product-mockup
Primary request: premium product photo of a matte black shampoo bottle with a minimal label
Scene/backdrop: clean studio gradient from light gray to white
Subject: single bottle centered with subtle reflec

skill-card.md

## Description:

Generate or edit raster images for websites, games, UI mockups, product shots, textures, sprites, and other bitmap assets using a host-approved image generation capability.

This skill is ready for commercial/non-commercial use.

## Publisher:

[zack-dev-cm](https://clawhub.ai/user/zack-dev-cm)

### License/Terms of Use:

MIT-0

## Use Case:

Developers and agents use this skill to turn image requests into compact production prompts, invoke host-approved image generation or editing tools, verify outputs, and save selected bitmap assets into projects.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Generated images can contain inaccurate details, poor text rendering, or rights-sensitive content such as logos, endorsements, or identity-preserving edits.

Mitigation: Review generated images for accuracy, rights-sensitive content, requested invariants, and in-image text before using or publishing them.

Risk: Project-bound image assets can be left outside the workspace or referenced before the selected final output is saved.

Mitigation: Save selected final assets into the workspace, update consuming references, and report the saved path and final prompt.

## Reference(s):

- [Prompting best practices](references/prompting.md)
- [Sample prompts](references/sample-prompts.md)

## Skill Output:

**Output Type(s):** [text, markdown, shell commands, guidance, files]

**Output Format:** [Markdown guidance with prompt specifications, verification notes, saved-path reporting, and optional shell commands]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [May result in project-bound bitmap image files when the host-approved image generation or editing capability is used.]

## Skill Version(s):

1.0.1 (source: server release metadata)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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