Codex Image Generation
Generate, edit, and prepare practical raster image assets directly within a project workflow.
The documentation separates built-in and CLI modes, requires confirmation for the transparency downgrade, avoids overwriting files, handles API keys cautiously, and discusses network-approval risk. It does not fully disclose image/prompt data flows, retention or privacy behavior, built-in-tool external effects, or a general rollback mechanism, so 7 points are deducted.
The script and docs provide parameter validation, missing-dependency guidance, output-collision protection, batch limits, and some transient-error retries; the main path is plausible from static inspection. However, there is no committed test suite or execution evidence, and built-in-tool failure coverage and recovery remain limited, so the score is capped conservatively.
Use cases, generate/edit distinctions, non-fit cases, trigger rules, and output handling are clearly described. Chinese-language support is not documented, and the CLI depends on OpenAI network services without evidence of mainland-China reachability or a practical alternative, so 5 points are deducted.
The package has strong information architecture, progressive disclosure, dependency notes, parameters, examples, limitations, and cross-references. It lacks an intrinsic version, changelog, explicit maintenance owner, and update path; license and governance are mainly supplied as external metadata, so 4 points are deducted.
The skill covers generation, editing, batching, transparency workflows, and project-local artifact handling, with a clear core benefit. Static files do not verify output quality, text accuracy, or iteration outcomes, so limited manual review remains necessary and 1 point is deducted.
The SKILL.md, reference documents, and CLI source provide auditable rules, parameters, and diagnostics, making some claims source-checkable. There is no committed test suite, CI coverage, or third-party execution record, so the static evidence supports only a low score.
- Image generation depends on an external model service; handling, retention, and privacy boundaries for prompts and input images are not explicitly documented.
- CLI mode requires OPENAI_API_KEY, Python dependencies, and network access; mainland-China users may face service reachability or network-approval issues.
- No tests or CI evidence cover the key paths, so output quality, text accuracy, transparency edges, and edit invariants require manual verification.
What does this skill do, and when should you use it?
This skill is designed for Codex image generation and editing, covering photos, illustrations, textures, sprites, mockups, and transparent-background assets. Its preferred path uses the built-in image_gen tool and does not require OPENAI_API_KEY; the CLI fallback requires an API key and additional dependencies. It supports new images, reference-guided generation, edits to visible images, and repeated asset variants. It is not intended for deterministic SVG, vector, HTML/CSS, canvas, or other code-native graphics work.
Generates raster images from prompts; edits images visible in the conversation; inspects a local edit target with view_image before built-in editing; handles simple transparency by generating a flat chroma-key background and running remove_chroma_key.py to produce PNG/WebP output with alpha; runs scripts/image_gen.py with generate, edit, or generate-batch in explicit CLI fallback mode; validates outputs and iterates with one targeted change at a time.
- A web or product team needs a landing-page hero, product shot, or marketing image.
- A game developer needs concept art, sprites, or other bitmap assets.
- A designer wants style or composition variants based on reference images.
- A user needs background replacement, object removal, or lighting and weather changes while preserving the rest of an image.
- A team needs UI mockups, wireframes, infographics, or scientific-educational visuals.
What are this skill's strengths and limitations?
- Covers generation, editing, reference-based variants, batches, and simple transparent-background processing.
- The preferred built-in mode does not require an API key.
- Provides structured prompting, use-case taxonomy, edit invariants, and output-validation guidance.
- Clearly separates raster generation from SVG, HTML/CSS, canvas, and other code-native workflows.
- It contains Codex-specific tools, paths, and environment conventions, so other clients require adaptation.
- CLI fallback requires OPENAI_API_KEY, Python, the openai package, and Pillow.
- Complex transparent subjects such as hair, smoke, glass, liquids, or reflective materials require confirmation before using the true-transparency fallback.
- The source does not document a standalone skill installer, test coverage, or cross-platform validation.
How do you install this skill?
The repository bundles this skill with 19 others at codex-rs/skills/src/assets/samples/imagegen/SKILL.md. The README documents Codex CLI installation but does not document a standalone installer for this skill. Following the repository instructions, install Codex CLI with curl -fsSL https://chatgpt.com/codex/install.sh | sh on macOS/Linux, or powershell -ExecutionPolicy ByPass -c "irm https://chatgpt.com/codex/install.ps1 | iex" on Windows. npm install -g @openai/codex and brew install --cask codex are also documented options.
How do you use this skill?
In Codex, provide a concrete image request such as: “Generate a minimal ceramic coffee mug product image for a website hero, using clean product photography, with usable negative space for copy and no logo, text, or watermark.” Normal generation and editing use the built-in image_gen path. Choose scripts/image_gen.py only when explicitly requesting CLI/API/model control; the CLI fallback requires OPENAI_API_KEY.