{"id":"e7d32f76-85ed-40da-88a3-7e9a6eb9d3f5","entityType":"agent","slug":"clawhub-zack-dev-cm-imagegen","name":"Image Gen","canonicalUrl":"https://www.xpersona.co/agent/clawhub-zack-dev-cm-imagegen","canonicalPath":"/agent/clawhub-zack-dev-cm-imagegen","generatedAt":"2026-10-10T21:41:24.528Z","source":"CLAWHUB","claimStatus":"UNCLAIMED","verificationTier":"NONE","summary":{"evidence":{"source":"CLAWHUB","verified":false,"confidence":"medium","updatedAt":"2026-10-10T15:42:31.743Z","emptyReason":null},"description":"Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparen...","descriptionLabel":"Source description","evidenceSummary":"Capability contract not published. No trust telemetry is available yet. 1.4K downloads reported by the source. 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edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparen...\n\nTags: codex:1.0.1, image-generation:1.0.1, latest:1.0.1\n\nVersion history:\n\nv1.0.1 | 2026-06-10T13:13:42.579Z | user\n\nPublish instruction-only ClawHub bundle for cleaner security checks.\n\nv1.0.0 | 2026-06-10T12:58:04.146Z | user\n\nInitial public Codex image generation skill release.\n\nArchive index:\n\nArchive v1.0.1: 7 files, 19351 bytes\n\nFiles: agents/openai.yaml (193b), LICENSE.txt (10776b), references/prompting.md (9896b), references/sample-prompts.md (19239b), skill-card.md (2091b), SKILL.md (5886b), _meta.json (127b)\n\nFile v1.0.1:SKILL.md\n\n---\nname: \"imagegen\"\ndescription: \"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.\"\n---\n\n# Image Generation Skill\n\nUse this skill when the user needs a bitmap image rather than repo-native code,\nSVG, HTML, CSS, canvas, or an existing vector/icon system.\n\nThis public ClawHub release is instruction-only. It does not bundle executable\nAPI helpers, dependencies, or generated assets. In Codex sessions, use the\navailable built-in image generation or image editing tool. In other agent\nhosts, use the host's approved image-generation capability and keep the same\nprompting and verification standards.\n\n## When To Use\n\n- Generate a new raster image: product shot, hero image, concept art, cover,\n  sprite, texture, UI mockup, infographic, or educational visual.\n- Edit an existing image while preserving important invariants such as identity,\n  product shape, text, lighting direction, or composition.\n- Derive visual variants from supplied reference images.\n- Produce multiple related bitmap assets when each output has a distinct prompt\n  or role.\n\n## When Not To Use\n\n- The requested asset should be an SVG, icon font, HTML/CSS composition, canvas\n  graphic, or repo-native component.\n- The repo already has an editable vector/logo/icon system that should be\n  extended directly.\n- The user asks for deterministic code-native output rather than generated\n  imagery.\n\n## Workflow\n\n1. Decide intent: `generate` for a new image, `edit` for changing an existing\n   image while preserving parts of it.\n2. Decide whether the image is preview-only or project-bound.\n3. Label every input image by role: edit target, reference image, style source,\n   insert, or supporting context.\n4. Normalize the prompt into a compact production spec. Preserve user\n   constraints and avoid adding unrelated characters, brands, slogans, or story\n   elements.\n5. Use the host-provided image generation/editing tool. For distinct assets,\n   make separate tool calls or jobs rather than relying on variants of one\n   prompt.\n6. Inspect the output for subject accuracy, composition, text rendering,\n   style fit, prohibited content, and requested invariants.\n7. Iterate with one targeted change when needed.\n8. For project-bound assets, place the selected final artifact in the workspace\n   and update consuming references. Never leave a project-referenced final image\n   only in a host default output directory.\n9. Report final saved path(s), whether the output is preview-only or\n   project-bound, and the final prompt used.\n\n## Transparent Or Cutout Requests\n\nFor simple opaque subjects, request a flat removable chroma-key background and\nremove it locally with an approved project or host helper if available.\n\nPrompt the source image like this:\n\n```text\nCreate the requested subject on a perfectly flat solid #00ff00 chroma-key background for background removal.\nThe background must be one uniform color with no shadows, gradients, texture, reflections, floor plane, or lighting variation.\nKeep the subject fully separated from the background with crisp edges and generous padding.\nDo not use #00ff00 anywhere in the subject.\nNo cast shadow, no contact shadow, no reflection, no watermark, and no text unless explicitly requested.\n```\n\nUse a different key color when green appears in the subject. If the subject has\nhair, smoke, glass, translucent material, soft shadow, reflection, or colors\nthat conflict with practical key colors, explain that true native transparency\nor manual editing may be required before proceeding.\n\n## Prompt Schema\n\nUse only the lines that help the request:\n\n```text\nUse case: <taxonomy slug>\nAsset type: <where the image will be used>\nPrimary request: <user's main prompt>\nInput images: <Image 1: role; Image 2: role>\nScene/backdrop: <environment>\nSubject: <main subject>\nStyle/medium: <photo, illustration, 3D render, diagram, etc.>\nComposition/framing: <wide, close, top-down, centered, negative space>\nLighting/mood: <lighting and mood>\nColor palette: <palette notes>\nMaterials/textures: <surface details>\nText (verbatim): \"<exact text>\"\nConstraints: <must keep or must avoid>\nAvoid: <negative constraints>\n```\n\n## Use-Case Slugs\n\nGeneration:\n\n- `photorealistic-natural`\n- `product-mockup`\n- `ui-mockup`\n- `infographic-diagram`\n- `scientific-educational`\n- `ads-marketing`\n- `productivity-visual`\n- `logo-brand`\n- `illustration-story`\n- `stylized-concept`\n- `historical-scene`\n\nEditing:\n\n- `text-localization`\n- `identity-preserve`\n- `precise-object-edit`\n- `lighting-weather`\n- `background-extraction`\n- `style-transfer`\n- `compositing`\n- `sketch-to-render`\n\n## Prompting Rules\n\n- Start with the visual job and intended use.\n- Specify subject, scene, style, composition, lighting, and constraints.\n- Quote exact text and keep it short; generated text can still need correction.\n- For edits, repeat invariants plainly: what must change and what must stay\n  unchanged.\n- For reference images, state exactly how each reference should influence the\n  output.\n- Avoid fake UI evidence, fake policy proof, fake screenshots, endorsements, or\n  brand marks unless the user has rights and asks for them.\n- Prefer real product screenshots or rendered app state where users or reviewers\n  need factual evidence.\n\n## Reference Map\n\n- `references/prompting.md`: shared prompting principles.\n- `references/sample-prompts.md`: copy/paste prompt recipes by asset type.\n\nFile v1.0.1:_meta.json\n\n{\n  \"ownerId\": \"kn7dhjt1k1f111whp13fmrqwnh81tn1v\",\n  \"slug\": \"imagegen\",\n  \"version\": \"1.0.1\",\n  \"publishedAt\": 1781097222579\n}\n\nFile v1.0.1:references/prompting.md\n\n# Prompting best practices\n\nThese prompting principles are shared by both top-level modes of the skill:\n- built-in `image_gen` tool (default)\n- explicit `scripts/image_gen.py` CLI fallback\n\nThis 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.\n\n## Contents\n- [Structure](#structure)\n- [Specificity policy](#specificity-policy)\n- [Allowed and disallowed augmentation](#allowed-and-disallowed-augmentation)\n- [Composition and layout](#composition-and-layout)\n- [Constraints and invariants](#constraints-and-invariants)\n- [Text in images](#text-in-images)\n- [Input images and references](#input-images-and-references)\n- [Iterate deliberately](#iterate-deliberately)\n- [Transparent images](#transparent-images)\n- [Fallback-only execution controls](#fallback-only-execution-controls)\n- [Use-case tips](#use-case-tips)\n- [Where to find copy/paste recipes](#where-to-find-copypaste-recipes)\n\n## Structure\n- Use a consistent order: scene/backdrop -> subject -> key details -> constraints -> output intent.\n- Include intended use (ad, UI mock, infographic) to set the level of polish.\n- For complex requests, use short labeled lines instead of one long paragraph.\n\n## Specificity policy\n- If the user prompt is already specific and detailed, normalize it into a clean spec without adding creative requirements.\n- If the prompt is generic, you may add tasteful detail when it materially improves the output.\n- Treat examples in `sample-prompts.md` as fully-authored recipes, not as the default amount of augmentation to add to every request.\n- 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.\n\n## Allowed and disallowed augmentation\n\nAllowed augmentation for generic prompts:\n- composition and framing cues\n- intended-use or polish-level hints\n- practical layout guidance\n- reasonable scene concreteness that supports the request\n\nDo not add:\n- extra characters, props, or objects that are not implied\n- brand palettes, slogans, or story beats that are not implied\n- arbitrary side-specific placement unless the surrounding layout supports it\n\n## Composition and layout\n- Specify framing and viewpoint (close-up, wide, top-down) and placement only when it materially helps.\n- Call out negative space if the asset clearly needs room for UI or copy.\n- Avoid making left/right layout decisions unless the user or surrounding layout supports them.\n- 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`).\n\n## Constraints and invariants\n- State what must not change (`keep background unchanged`).\n- For edits, say `change only X; keep Y unchanged` and repeat invariants on every iteration to reduce drift.\n\n## Text in images\n- Put literal text in quotes or ALL CAPS and specify typography (font style, size, color, placement).\n- Spell uncommon words letter-by-letter if accuracy matters.\n- For in-image copy, require verbatim rendering and no extra characters.\n- In CLI fallback mode, use `medium` or `high` quality for small text, dense infographics, data-heavy slides, multi-font layouts, legends, axes, and footnotes.\n\n## Input images and references\n- Do not assume that every provided image is an edit target.\n- Label each image by index and role (`Image 1: edit target`, `Image 2: style reference`).\n- If the user provides images for style, composition, or mood guidance and does not ask to modify them, treat the request as generation with references.\n- If the user asks to preserve an existing image while changing specific parts, treat the request as an edit.\n- For compositing, describe how the images interact (`place the subject from Image 2 into Image 1`).\n\n## Iterate deliberately\n- Start with a clean base prompt, then make small single-change edits.\n- Re-specify critical constraints when you iterate.\n- Prefer one targeted follow-up at a time over rewriting the whole prompt.\n\n## Transparent images\n- Use built-in `image_gen` first for transparent-image requests. If the subject is clearly too complex for chroma-key removal, explain the fallback and ask before switching to CLI.\n- Prompt for a perfectly flat solid chroma-key background, usually `#00ff00`; use `#ff00ff` when the subject is green, and avoid key colors that appear in the subject.\n- Explicitly prohibit shadows, gradients, floor planes, reflections, texture, and lighting variation in the background.\n- Ask for crisp edges, generous padding, and no use of the key color inside the subject.\n- After generation, remove the background locally with `python \"${CODEX_HOME:-$HOME/.codex}/skills/.system/imagegen/scripts/remove_chroma_key.py\" --input <source> --out <final.png> --auto-key border --soft-matte --transparent-threshold 12 --opaque-threshold 220 --despill` and validate the alpha result before shipping it.\n- Use soft matte and despill for antialiased edges; hard tolerance-only removal is mainly for flat pixel-art or exact-color fixtures.\n- Use CLI `gpt-image-1.5 --background transparent --output-format png` only after the user explicitly confirms the fallback, or when the user already explicitly requested `gpt-image-1.5`, `scripts/image_gen.py`, or CLI fallback. Ask first for true/native transparency requests, failed chroma-key validation, or complex transparent subjects such as hair, fur, glass, smoke, liquids, translucent materials, reflective objects, or soft shadows.\n\n## Fallback-only execution controls\n- `quality`, `input_fidelity`, explicit masks, output format, and output paths are fallback-only execution controls.\n- Do not assume they are built-in `image_gen` tool arguments.\n- If the user explicitly chooses CLI fallback, see `references/cli.md` and `references/image-api.md` for those controls.\n- In CLI fallback mode, `gpt-image-2` is the default. It supports `quality=low|medium|high|auto`; use `low` for fast drafts and thumbnails, and move to `medium`, `high`, or `auto` for final assets.\n- `gpt-image-2` always uses high fidelity for image inputs, so do not set `input_fidelity` with that model.\n- If a transparent request needs true CLI transparency, ask before using `gpt-image-1.5` unless the user already explicitly chose it. Explain that built-in chroma-key removal is the default path, but `gpt-image-2` does not support `background=transparent`.\n- If the user asks for 4K-style output with `gpt-image-2`, use `3840x2160` for landscape or `2160x3840` for portrait.\n\n## Use-case tips\nGenerate:\n- photorealistic-natural: Prompt as if a real photo is captured in the moment; use photography language (lens, lighting, framing); call for real texture; avoid over-stylized polish unless requested.\n- product-mockup: Describe the product/packaging and materials; ensure clean silhouette and label clarity; if in-image text is needed, require verbatim rendering and specify typography.\n- ui-mockup: Describe the target fidelity first (shippable mockup or low-fi wireframe), then focus on layout, hierarchy, and practical UI elements; avoid concept-art language.\n- infographic-diagram: Define the audience and layout flow; label parts explicitly; require verbatim text; prefer higher quality in CLI mode for dense labels.\n- logo-brand: Keep it simple and scalable; ask for a strong silhouette and balanced negative space; avoid decorative flourishes unless requested.\n- ads-marketing: Write like a creative brief; include brand positioning, audience, desired vibe, scene, and exact tagline if text must appear.\n- productivity-visual: Name the exact artifact (slide, chart, workflow diagram), define the canvas and hierarchy, provide real labels/data, and ask for readable typography and polished spacing.\n- scientific-educational: Define audience, lesson objective, required labels, scientific constraints, arrows, and scan-friendly whitespace.\n- illustration-story: Define panels or scene beats; keep each action concrete.\n- stylized-concept: Specify style cues, material finish, and rendering approach (3D, painterly, clay) without inventing new story elements.\n- historical-scene: State the location/date and required period accuracy; constrain clothing, props, and environment to match the era.\n\nEdit:\n- text-localization: Change only the text; preserve layout, typography, spacing, and hierarchy; no extra words or reflow unless needed.\n- identity-preserve: Lock identity (face, body, pose, hair, expression); change only the specified elements; match lighting and shadows.\n- precise-object-edit: Specify exactly what to remove/replace; preserve surrounding texture and lighting; keep everything else unchanged.\n- lighting-weather: Change only environmental conditions (light, shadows, atmosphere, precipitation); keep geometry, framing, and subject identity.\n- background-extraction: For simple opaque subjects, request a clean cutout on a perfectly flat chroma-key background; crisp silhouette; generous padding; no shadows; no halos; preserve label text exactly; no restyling. Ask before using true CLI transparency for complex subjects.\n- style-transfer: Specify style cues to preserve (palette, texture, brushwork) and what must change; add `no extra elements` to prevent drift.\n- compositing: Reference inputs by index; specify what moves where; match lighting, perspective, and scale; keep the base framing unchanged.\n- sketch-to-render: Preserve layout, proportions, and perspective; choose materials and lighting that support the supplied sketch without adding new elements.\n\n## Where to find copy/paste recipes\nFor copy/paste prompt specs (examples only), see `references/sample-prompts.md`. This file focuses on principles, specificity, and iteration patterns.\n\nFile v1.0.1:references/sample-prompts.md\n\n# Sample prompts (copy/paste)\n\nThese prompt recipes are shared across both top-level modes of the skill:\n- built-in `image_gen` tool (default)\n- `scripts/image_gen.py` CLI fallback for explicit CLI/API/model requests or user-confirmed true-transparent-output fallback requests\n\nUse these as starting points. They are intentionally complete prompt recipes, not the default amount of augmentation to add to every user request.\n\nWhen adapting a user's prompt:\n- keep user-provided requirements\n- only add detail according to the specificity policy in `SKILL.md`\n- do not treat every example below as permission to invent extra story elements\n\nThe 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.\n\nExecution 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.\n\nCLI model notes:\n- `gpt-image-2` is the fallback CLI default for new workflows.\n- `gpt-image-2` supports `quality` values `low`, `medium`, `high`, and `auto`.\n- For 4K-style `gpt-image-2` output, use `3840x2160` or `2160x3840`.\n- 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`.\n- Do not set `input_fidelity` with `gpt-image-2`; image inputs already use high fidelity.\n\nFor prompting principles (structure, specificity, invariants, iteration), see `references/prompting.md`.\n\n## Generate\n\n### photorealistic-natural\n```\nUse case: photorealistic-natural\nPrimary request: candid photo of an elderly sailor on a small fishing boat adjusting a net\nScene/backdrop: coastal water with soft haze\nSubject: weathered skin with wrinkles and sun texture\nStyle/medium: photorealistic candid photo\nComposition/framing: medium close-up, eye-level\nLighting/mood: soft coastal daylight, shallow depth of field, subtle film grain\nMaterials/textures: real skin texture, worn fabric, salt-worn wood\nConstraints: natural color balance; no heavy retouching; no glamorization; no watermark\nAvoid: studio polish; staged look\n```\n\n### product-mockup\n```\nUse case: product-mockup\nPrimary request: premium product photo of a matte black shampoo bottle with a minimal label\nScene/backdrop: clean studio gradient from light gray to white\nSubject: single bottle centered with subtle reflection\nStyle/medium: premium product photography\nComposition/framing: centered, slight three-quarter angle, generous padding\nLighting/mood: softbox lighting, clean highlights, controlled shadows\nMaterials/textures: matte plastic, crisp label printing\nConstraints: no logos or trademarks; no watermark\n```\n\n### ui-mockup\n```\nUse case: ui-mockup\nPrimary request: mobile app home screen for a local farmers market with vendors and daily specials\nAsset type: mobile app screen\nStyle/medium: realistic product UI, not concept art\nComposition/framing: clean vertical mobile layout with clear hierarchy\nConstraints: practical layout, clear typography, no logos or trademarks, no watermark\n```\n\n### infographic-diagram\n```\nUse case: infographic-diagram\nPrimary request: detailed infographic of an automatic coffee machine flow\nScene/backdrop: clean, light neutral background\nSubject: bean hopper -> grinder -> brew group -> boiler -> water tank -> drip tray\nStyle/medium: clean vector-like infographic with clear callouts and arrows\nComposition/framing: vertical poster layout, top-to-bottom flow\nText (verbatim): \"Bean Hopper\", \"Grinder\", \"Brew Group\", \"Boiler\", \"Water Tank\", \"Drip Tray\"\nConstraints: clear labels, strong contrast, no logos or trademarks, no watermark\n```\n\n### scientific-educational\n```\nUse case: scientific-educational\nPrimary request: biology diagram titled \"Cellular Respiration at a Glance\" for high school students\nScene/backdrop: clean white classroom handout background\nSubject: glucose turns into energy inside a cell; include glycolysis, Krebs cycle, and electron transport chain\nStyle/medium: flat scientific diagram with consistent icons, arrows, and readable labels\nComposition/framing: landscape slide-style layout with clear hierarchy and generous whitespace\nText (verbatim): \"Cellular Respiration at a Glance\", \"Glucose\", \"Pyruvate\", \"ATP\", \"NADH\", \"FADH2\", \"CO2\", \"O2\", \"H2O\"\nConstraints: scientifically plausible; avoid tiny text; no extra decoration; no watermark\n```\n\n### logo-brand\n```\nUse case: logo-brand\nPrimary request: original logo for \"Field & Flour\", a local bakery\nStyle/medium: vector logo mark; flat colors; minimal\nComposition/framing: single centered logo on a plain background with generous padding\nConstraints: strong silhouette, balanced negative space; original design only; no gradients unless essential; no trademarks; no watermark\n```\n\n### illustration-story\n```\nUse case: illustration-story\nPrimary request: 4-panel comic about a pet left alone at home\nScene/backdrop: cozy living room across panels\nSubject: pet reacting to the owner leaving, then relaxing, then returning to a composed pose\nStyle/medium: comic illustration with clear panels\nComposition/framing: 4 equal-sized vertical panels, readable actions per panel\nConstraints: no text; no logos or trademarks; no watermark\n```\n\n### stylized-concept\n```\nUse case: stylized-concept\nPrimary request: cavernous hangar interior with tall support beams and drifting fog\nScene/backdrop: industrial hangar interior, deep scale, light haze\nSubject: compact shuttle parked near the center\nStyle/medium: cinematic concept art, industrial realism\nComposition/framing: wide-angle, low-angle\nLighting/mood: volumetric light rays cutting through fog\nConstraints: no logos or trademarks; no watermark\n```\n\n### ads-marketing\n```\nUse case: ads-marketing\nPrimary request: campaign image for a streetwear brand called Thread\nSubject: group of friends hanging out together in a stylish urban setting\nStyle/medium: polished youth streetwear campaign photography\nComposition/framing: vertical ad layout with natural poses and integrated headline space\nLighting/mood: contemporary, energetic, tasteful\nText (verbatim): \"Yours to Create.\"\nConstraints: render the tagline exactly once; clean legible typography; no extra text; no watermarks; no unrelated logos\n```\n\n### productivity-visual\n```\nUse case: productivity-visual\nPrimary request: one pitch-deck slide titled \"Market Opportunity\"\nAsset type: fundraising slide image\nStyle/medium: clean modern deck slide, white background, crisp sans-serif typography\nSubject: TAM/SAM/SOM concentric-circle diagram plus a small growth bar chart from 2021 to 2026\nComposition/framing: 16:9 landscape slide, clear data hierarchy, polished spacing\nText (verbatim): \"Market Opportunity\", \"TAM: $42B\", \"SAM: $8.7B\", \"SOM: $340M\", \"AGI Research, 2024\", \"Internal analysis\"\nConstraints: readable labels, no clip art, no stock photography, no decorative clutter, no watermark\n```\n\n### historical-scene\n```\nUse case: historical-scene\nPrimary request: outdoor crowd scene in Bethel, New York on August 16, 1969\nScene/backdrop: open field with period-appropriate staging\nSubject: crowd in period-accurate clothing, authentic environment\nStyle/medium: photorealistic photo\nComposition/framing: wide shot, eye-level\nConstraints: period-accurate details; no modern objects; no logos or trademarks; no watermark\n```\n\n## Asset type templates (taxonomy-aligned)\n\n### Website assets template\n```\nUse case: <photorealistic-natural|stylized-concept|product-mockup|infographic-diagram|ui-mockup>\nAsset type: <hero image / section illustration / blog header>\nPrimary request: <short description>\nScene/backdrop: <environment or abstract backdrop>\nSubject: <main subject>\nStyle/medium: <photo/illustration/3D>\nComposition/framing: <wide/centered; note usable negative space only if needed>\nLighting/mood: <soft/bright/neutral>\nColor palette: <brand colors or neutral>\nConstraints: <no text; no logos; no watermark; leave room for UI if needed>\n```\n\n### Website assets example: minimal hero background\n```\nUse case: stylized-concept\nAsset type: landing page hero background\nPrimary request: minimal abstract background with a soft gradient and subtle texture\nStyle/medium: matte illustration / soft-rendered abstract background\nComposition/framing: wide composition with usable negative space for page copy\nLighting/mood: gentle studio glow\nColor palette: restrained neutral palette\nConstraints: no text; no logos; no watermark\n```\n\n### Website assets example: feature section illustration\n```\nUse case: stylized-concept\nAsset type: feature section illustration\nPrimary request: simple abstract shapes suggesting connection and flow\nScene/backdrop: subtle light-gray backdrop with faint texture\nStyle/medium: flat illustration; soft shadows; restrained contrast\nComposition/framing: centered cluster; open margins for UI\nColor palette: muted neutral palette\nConstraints: no text; no logos; no watermark\n```\n\n### Website assets example: blog header image\n```\nUse case: photorealistic-natural\nAsset type: blog header image\nPrimary request: overhead desk scene with notebook, pen, and coffee cup\nScene/backdrop: warm wooden tabletop\nStyle/medium: photorealistic photo\nComposition/framing: wide crop with clean room for page copy\nLighting/mood: soft morning light\nConstraints: no text; no logos; no watermark\n```\n\n### Game assets template\n```\nUse case: stylized-concept\nAsset type: <game environment concept art / game character concept / game UI icon / tileable game texture>\nPrimary request: <biome/scene/character/icon/material>\nScene/backdrop: <location + set dressing> (if applicable)\nSubject: <main focal element(s)>\nStyle/medium: <realistic/stylized>; <concept art / character render / UI icon / texture>\nComposition/framing: <wide/establishing/top-down>; <camera angle>; <focal point placement>\nLighting/mood: <time of day>; <mood>; <volumetric/fog/etc>\nConstraints: no logos or trademarks; no watermark\n```\n\n### Game assets example: environment concept art\n```\nUse case: stylized-concept\nAsset type: game environment concept art\nPrimary request: cavernous hangar interior with tall support beams and drifting fog\nScene/backdrop: industrial hangar interior, deep scale, light haze\nSubject: compact shuttle parked near the center\nStyle/medium: cinematic concept art, industrial realism\nComposition/framing: wide-angle, low-angle\nLighting/mood: volumetric light rays cutting through fog\nConstraints: no logos or trademarks; no watermark\n```\n\n### Game assets example: character concept\n```\nUse case: stylized-concept\nAsset type: game character concept\nPrimary request: desert scout character with layered travel gear\nSubject: long coat, satchel, practical travel clothing\nStyle/medium: character render; stylized realism\nComposition/framing: neutral hero pose on a simple backdrop\nConstraints: no logos or trademarks; no watermark\n```\n\n### Game assets example: UI icon\n```\nUse case: stylized-concept\nAsset type: game UI icon\nPrimary request: round shield icon with a subtle rune pattern\nStyle/medium: painted game UI icon\nComposition/framing: centered icon; generous padding; clear silhouette\nConstraints: no text; no background scene elements; no logos or trademarks; no watermark\n```\n\n### Game assets example: tileable texture\n```\nUse case: stylized-concept\nAsset type: tileable game texture\nPrimary request: worn sandstone blocks\nStyle/medium: seamless tileable texture; PBR-ish look\nScene/backdrop: neutral lighting reference only\nConstraints: seamless edges; no obvious focal elements; no text; no logos or trademarks; no watermark\n```\n\n### Wireframe template\n```\nUse case: ui-mockup\nAsset type: website wireframe\nPrimary request: <page or flow to sketch>\nStyle/medium: low-fi grayscale wireframe\nComposition/framing: <landscape or portrait to match expected device>\nSubject: <sections in order; grid/columns; key labels>\nConstraints: no color; no logos; no real photos; no watermark\n```\n\n### Wireframe example: homepage (desktop)\n```\nUse case: ui-mockup\nAsset type: website wireframe\nPrimary request: SaaS homepage layout with clear hierarchy\nStyle/medium: low-fi grayscale wireframe\nSubject: top nav; hero with headline and CTA; three feature cards; testimonial strip; pricing preview; footer\nComposition/framing: landscape desktop layout\nConstraints: label major blocks; no color; no logos; no real photos; no watermark\n```\n\n### Wireframe example: pricing page\n```\nUse case: ui-mockup\nAsset type: website wireframe\nPrimary request: pricing page layout with comparison table\nStyle/medium: low-fi grayscale wireframe\nSubject: header; plan toggle; 3 pricing cards; comparison table; FAQ accordion; footer\nComposition/framing: desktop or tablet layout\nConstraints: label key areas; no color; no logos; no real photos; no watermark\n```\n\n### Wireframe example: mobile onboarding flow\n```\nUse case: ui-mockup\nAsset type: mobile onboarding wireframe\nPrimary request: three-screen mobile onboarding flow\nStyle/medium: low-fi grayscale wireframe\nSubject: screen 1 headline and CTA; screen 2 feature bullets; screen 3 form fields and CTA\nComposition/framing: portrait mobile layout\nConstraints: label screens and blocks; no color; no logos; no real photos; no watermark\n```\n\n### Logo template\n```\nUse case: logo-brand\nAsset type: logo concept\nPrimary request: <brand idea or symbol concept>\nStyle/medium: vector logo mark; flat colors; minimal\nComposition/framing: centered mark; clear silhouette; generous margin\nColor palette: <1-2 colors; high contrast>\nText (verbatim): \"<exact name>\" (only if needed)\nConstraints: no gradients; no mockups; no 3D; no watermark\n```\n\n### Logo example: abstract symbol mark\n```\nUse case: logo-brand\nAsset type: logo concept\nPrimary request: geometric leaf symbol suggesting sustainability and growth\nStyle/medium: vector logo mark; flat colors; minimal\nComposition/framing: centered mark; clear silhouette\nColor palette: deep green and off-white\nConstraints: no text unless requested; no gradients; no mockups; no 3D; no watermark\n```\n\n### Logo example: monogram mark\n```\nUse case: logo-brand\nAsset type: logo concept\nPrimary request: interlocking monogram of the letters \"AV\"\nStyle/medium: vector logo mark; flat colors; minimal\nComposition/framing: centered mark; balanced spacing\nColor palette: black on white\nConstraints: no gradients; no mockups; no 3D; no watermark\n```\n\n### Logo example: wordmark\n```\nUse case: logo-brand\nAsset type: logo concept\nPrimary request: clean wordmark for a modern studio\nStyle/medium: vector wordmark; flat colors; minimal\nText (verbatim): \"Studio North\"\nComposition/framing: centered text; even letter spacing\nConstraints: no gradients; no mockups; no 3D; no watermark\n```\n\n## Edit\n\n### text-localization\n```\nUse case: text-localization\nInput images: Image 1: original infographic\nPrimary request: replace \"Bean Hopper\", \"Grinder\", \"Brew Group\", \"Boiler\", \"Water Tank\", and \"Drip Tray\" with \"Tolva\", \"Molino\", \"Grupo de infusión\", \"Caldera\", \"Depósito de agua\", and \"Bandeja de goteo\"\nConstraints: change only the text; preserve layout, typography, spacing, and hierarchy; no extra words; do not alter logos or imagery\n```\n\n### identity-preserve\n```\nUse case: identity-preserve\nInput images: Image 1: person photo; Image 2..N: clothing references\nPrimary request: replace only the clothing with the provided garments\nConstraints: preserve face, body shape, pose, hair, expression, and identity; match lighting and shadows; keep the background unchanged; no accessories or text\n```\n\n### precise-object-edit\n```\nUse case: precise-object-edit\nInput images: Image 1: room photo\nPrimary request: replace only the white chairs with wooden chairs\nConstraints: preserve camera angle, room lighting, floor shadows, and surrounding objects; keep all other aspects unchanged\n```\n\n### lighting-weather\n```\nUse case: lighting-weather\nInput images: Image 1: original photo\nPrimary request: make it look like a winter evening with gentle snowfall\nConstraints: preserve subject identity, geometry, camera angle, and composition; change only lighting, atmosphere, and weather\n```\n\n### background-extraction\n```\nUse case: background-extraction\nInput images: Image 1: product photo\nPrimary request: isolate the product on a clean transparent background\nScene/backdrop: perfectly flat solid #00ff00 chroma-key background for local background removal\nConstraints: background must be one uniform color with no shadows, gradients, texture, reflections, floor plane, or lighting variation; crisp silhouette; generous padding; no halos or fringing; preserve label text exactly; no restyling; do not use #00ff00 anywhere in the subject\n```\n\nPost-process note: after built-in generation, run `python \"${CODEX_HOME:-$HOME/.codex}/skills/.system/imagegen/scripts/remove_chroma_key.py\" --input <source> --out <final.png> --auto-key border --soft-matte --transparent-threshold 12 --opaque-threshold 220 --despill`. Ask before using CLI `gpt-image-1.5 --background transparent --output-format png` for true/native transparency, failed chroma-key validation, or complex subjects such as hair, fur, glass, smoke, liquids, translucent materials, reflections, or soft shadows, unless the user already explicitly requested `gpt-image-1.5`, `scripts/image_gen.py`, or CLI fallback.\n\n### style-transfer\n```\nUse case: style-transfer\nInput images: Image 1: style reference\nPrimary request: apply Image 1's visual style to a man riding a motorcycle on a plain white backdrop\nConstraints: preserve palette, texture, and brushwork; no extra elements\n```\n\n### compositing\n```\nUse case: compositing\nInput images: Image 1: base scene; Image 2: subject to insert\nPrimary request: place the subject from Image 2 next to the person in Image 1\nConstraints: match lighting, perspective, and scale; keep the base framing unchanged; no extra elements\n```\n\n### character consistency workflow\n```\nUse case: identity-preserve\nInput images: Image 1: previous character anchor illustration\nPrimary request: continue the story with the same character in a new scene and action\nScene/backdrop: snowy forest after a winter storm\nSubject: same young forest hero gently helping a frightened squirrel out of a fallen tree\nStyle/medium: same children's book watercolor illustration style as Image 1\nConstraints: do not redesign the character; preserve facial features, proportions, outfit, color palette, and personality; no text; no watermark\n```\n\n### sketch-to-render\n```\nUse case: sketch-to-render\nInput images: Image 1: drawing\nPrimary request: turn the drawing into a photorealistic image\nConstraints: preserve layout, proportions, and perspective; choose realistic materials and lighting; do not add new elements or text\n```\n\nFile v1.0.1:skill-card.md\n\n## Description:\n\nGenerate or edit raster images for websites, games, UI mockups, product shots, textures, sprites, and other bitmap assets using a host-approved image generation capability.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[zack-dev-cm](https://clawhub.ai/user/zack-dev-cm)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers 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.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Generated images can contain inaccurate details, poor text rendering, or rights-sensitive content such as logos, endorsements, or identity-preserving edits.\n\nMitigation: Review generated images for accuracy, rights-sensitive content, requested invariants, and in-image text before using or publishing them.\n\nRisk: Project-bound image assets can be left outside the workspace or referenced before the selected final output is saved.\n\nMitigation: Save selected final assets into the workspace, update consuming references, and report the saved path and final prompt.\n\n## Reference(s):\n\n- [Prompting best practices](references/prompting.md)\n- [Sample prompts](references/sample-prompts.md)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, shell commands, guidance, files]\n\n**Output Format:** [Markdown guidance with prompt specifications, verification notes, saved-path reporting, and optional shell commands]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [May result in project-bound bitmap image files when the host-approved image generation or editing capability is used.]\n\n## Skill Version(s):\n\n1.0.1 (source: server release metadata)\n\n## Ethical Considerations:\n\nUsers 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.\n\nFile v1.0.1:agents/openai.yaml\n\ninterface:\n  display_name: \"Image Gen\"\n  short_description: \"Generate or edit images for websites, games, and more\"\n  default_prompt: \"Use $imagegen to make or edit an image for this project.\"\n\nFile v1.0.1:LICENSE.txt\n\nApache License\nVersion 2.0, January 2004\nhttp://www.apache.org/licenses/\n\nTERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n1. 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We also recommend that a\n   file or class name and description of purpose be included on the\n   same \"printed page\" as the copyright notice for easier\n   identification within third-party archives.\n\nCopyright [yyyy] [name of copyright owner]\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n    http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\nArchive v1.0.0: 13 files, 47560 bytes\n\nFiles: agents/openai.yaml (275b), assets/imagegen-small.svg (2889b), LICENSE.txt (10776b), references/cli.md (10188b), references/codex-network.md (1779b), references/image-api.md (6079b), references/prompting.md (9896b), references/sample-prompts.md (19239b), scripts/image_gen.py (34271b), scripts/remove_chroma_key.py (13836b), skill-card.md (2411b), SKILL.md (24003b), _meta.json (127b)\n\nFile v1.0.0:SKILL.md\n\n---\nname: \"imagegen\"\ndescription: \"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.\"\n---\n\n# Image Generation Skill\n\nGenerates or edits images for the current project (for example website assets, game assets, UI mockups, product mockups, wireframes, logo design, photorealistic images, or infographics).\n\n## Top-level modes and rules\n\nThis skill has exactly two top-level modes:\n\n- **Default built-in tool mode (preferred):** built-in `image_gen` tool for normal image generation, editing, and simple transparent-image requests. Does not require `OPENAI_API_KEY`.\n- **Fallback CLI mode:** `scripts/image_gen.py` CLI. Use when the user explicitly asks for the CLI/API/model path, or after the user explicitly confirms a true model-native transparency fallback with `gpt-image-1.5`. Requires `OPENAI_API_KEY`.\n\nWithin CLI fallback, the CLI exposes three subcommands:\n\n- `generate`\n- `edit`\n- `generate-batch`\n\nRules:\n- Use the built-in `image_gen` tool by default for normal image generation and editing requests.\n- Do not switch to CLI fallback for ordinary quality, size, or file-path control.\n- If the user explicitly asks for a transparent image/background, stay on built-in `image_gen` first: prompt for a flat removable chroma-key background, then remove it locally with the installed helper at `$CODEX_HOME/skills/.system/imagegen/scripts/remove_chroma_key.py`.\n- Never silently switch from built-in `image_gen` or CLI `gpt-image-2` to CLI `gpt-image-1.5`. Treat this as a model/path downgrade and ask the user before doing it, unless the user has already explicitly requested `gpt-image-1.5`, `scripts/image_gen.py`, or CLI fallback.\n- If a transparent request appears too complex for clean chroma-key removal, asks for true/native transparency, or local removal fails validation, explain that true transparency requires CLI `gpt-image-1.5 --background transparent --output-format png` because `gpt-image-2` does not support `background=transparent`, then ask whether to proceed. Run the CLI fallback only after the user confirms.\n- The word `batch` by itself does not mean CLI fallback. If the user asks for many assets or says to batch-generate assets without explicitly asking for CLI/API/model controls, stay on the built-in path and issue one built-in call per requested asset or variant.\n- If the built-in tool fails or is unavailable, tell the user the CLI fallback exists and that it requires `OPENAI_API_KEY`. Proceed only if the user explicitly asks for that fallback.\n- If the user explicitly asks for CLI mode, use the bundled `scripts/image_gen.py` workflow. Do not create one-off SDK runners.\n- Never modify `scripts/image_gen.py`. If something is missing, ask the user before doing anything else.\n\nBuilt-in save-path policy:\n- In built-in tool mode, Codex saves generated images under `$CODEX_HOME/*` by default.\n- Do not describe or rely on OS temp as the default built-in destination.\n- Do not describe or rely on a destination-path argument (if any) on the built-in `image_gen` tool. If a specific location is needed, generate first and then move or copy the selected output from `$CODEX_HOME/generated_images/...`.\n- Save-path precedence in built-in mode:\n  1. If the user names a destination, move or copy the selected output there.\n  2. If the image is meant for the current project, move or copy the final selected image into the workspace before finishing.\n  3. If the image is only for preview or brainstorming, render it inline; the underlying file can remain at the default `$CODEX_HOME/*` path.\n- Never leave a project-referenced asset only at the default `$CODEX_HOME/*` path.\n- Do not overwrite an existing asset unless the user explicitly asked for replacement; otherwise create a sibling versioned filename such as `hero-v2.png` or `item-icon-edited.png`.\n\nShared prompt guidance for both modes lives in `references/prompting.md` and `references/sample-prompts.md`.\n\nFallback-only docs/resources for CLI mode:\n- `references/cli.md`\n- `references/image-api.md`\n- `references/codex-network.md`\n- `scripts/image_gen.py`\n\nLocal post-processing helper:\n- `$CODEX_HOME/skills/.system/imagegen/scripts/remove_chroma_key.py`: removes a flat chroma-key background from a generated image and writes a PNG/WebP with alpha. Prefer auto-key sampling, soft matte, and despill for antialiased edges.\n\n## When to use\n- Generate a new image (concept art, product shot, cover, website hero)\n- Generate a new image using one or more reference images for style, composition, or mood\n- Edit an existing image (inpainting, lighting or weather transformations, background replacement, object removal, compositing, transparent background)\n- Produce many assets or variants for one task\n\n## When not to use\n- Extending or matching an existing SVG/vector icon set, logo system, or illustration library inside the repo\n- Creating simple shapes, diagrams, wireframes, or icons that are better produced directly in SVG, HTML/CSS, or canvas\n- Making a small project-local asset edit when the source file already exists in an editable native format\n- Any task where the user clearly wants deterministic code-native output instead of a generated bitmap\n\n## Decision tree\n\nThink about two separate questions:\n\n1. **Intent:** is this a new image or an edit of an existing image?\n2. **Execution strategy:** is this one asset or many assets/variants?\n\nIntent:\n- If the user wants to modify an existing image while preserving parts of it, treat the request as **edit**.\n- If the user provides images only as references for style, composition, mood, or subject guidance, treat the request as **generate**.\n- If the user provides no images, treat the request as **generate**.\n\nBuilt-in edit semantics:\n- Built-in edit mode is for images already visible in the conversation context, such as attached images or images generated earlier in the thread.\n- If the user wants to edit a local image file with the built-in tool, first load it with built-in `view_image` tool so the image is visible in the conversation context, then proceed with the built-in edit flow.\n- Do not promise arbitrary filesystem-path editing through the built-in tool.\n- If a local file still needs direct file-path control, masks, or other explicit CLI-only parameters, use the explicit CLI fallback only when the user asks for it.\n- For edits, preserve invariants aggressively and save non-destructively by default.\n\nExecution strategy:\n- In the built-in default path, produce many assets or variants by issuing one `image_gen` call per requested asset or variant.\n- In the CLI fallback path, use the CLI `generate-batch` subcommand only when the user explicitly chose CLI mode and needs many prompts/assets.\n- For many distinct assets, do not use `n` as a substitute for separate prompts. `n` is for variants of one prompt; distinct assets need distinct built-in calls or distinct CLI `generate-batch` jobs.\n\nAssume the user wants a new image unless they clearly ask to change an existing one.\n\n## Workflow\n1. Decide the top-level mode: built-in by default, including simple transparent-output requests; fallback CLI only if explicitly requested or after the user explicitly confirms a transparent-output fallback.\n2. Decide the intent: `generate` or `edit`.\n3. Decide whether the output is preview-only or meant to be consumed by the current project.\n4. Decide the execution strategy: single asset vs repeated built-in calls vs CLI `generate-batch`.\n5. Collect inputs up front: prompt(s), exact text (verbatim), constraints/avoid list, and any input images.\n6. For every input image, label its role explicitly:\n   - reference image\n   - edit target\n   - supporting insert/style/compositing input\n7. If the edit target is only on the local filesystem and you are staying on the built-in path, inspect it with `view_image` first so the image is available in conversation context.\n8. If the user asked for a photo, illustration, sprite, product image, banner, or other explicitly raster-style asset, use `image_gen` rather than substituting SVG/HTML/CSS placeholders. If the request is for an icon, logo, or UI graphic that should match existing repo-native SVG/vector/code assets, prefer editing those directly instead.\n9. Augment the prompt based on specificity:\n   - If the user's prompt is already specific and detailed, normalize it into a clear spec without adding creative requirements.\n   - If the user's prompt is generic, add tasteful augmentation only when it materially improves output quality.\n10. Use the built-in `image_gen` tool by default.\n11. For transparent-output requests, follow the transparent image guidance below: generate with built-in `image_gen` on a flat chroma-key background, copy the selected output into the workspace or `tmp/imagegen/`, run the installed `$CODEX_HOME/skills/.system/imagegen/scripts/remove_chroma_key.py` helper, and validate the alpha result before using it. If this path looks unsuitable or fails, ask before switching to CLI `gpt-image-1.5`.\n12. Inspect outputs and validate: subject, style, composition, text accuracy, and invariants/avoid items.\n13. Iterate with a single targeted change, then re-check.\n14. For preview-only work, render the image inline; the underlying file may remain at the default `$CODEX_HOME/generated_images/...` path.\n15. For project-bound work, move or copy the selected artifact into the workspace and update any consuming code or references. Never leave a project-referenced asset only at the default `$CODEX_HOME/generated_images/...` path.\n16. For batches or multi-asset requests, persist every requested deliverable final in the workspace unless the user explicitly asked to keep outputs preview-only. Discarded variants do not need to be kept unless requested.\n17. If the user explicitly chooses or confirms the CLI fallback, then use the fallback-only docs for model, quality, size, `input_fidelity`, masks, output format, output paths, and network setup.\n18. Always report the final saved path(s) for any workspace-bound asset(s), plus the final prompt or prompt set and whether the built-in tool or fallback CLI mode was used.\n\n## Transparent image requests\n\nTransparent-image requests still use built-in `image_gen` first. Because the built-in tool does not expose a true transparent-background control, create a removable chroma-key source image and then convert the key color to alpha locally.\n\nDefault sequence:\n1. Use built-in `image_gen` to generate the requested subject on a perfectly flat solid chroma-key background.\n2. Choose a key color that is unlikely to appear in the subject: default `#00ff00`, use `#ff00ff` for green subjects, and avoid `#0000ff` for blue subjects.\n3. After generation, move or copy the selected source image from `$CODEX_HOME/generated_images/...` into the workspace or `tmp/imagegen/`.\n4. Run the installed helper path, not a project-relative script path:\n   ```bash\n   python \"${CODEX_HOME:-$HOME/.codex}/skills/.system/imagegen/scripts/remove_chroma_key.py\" \\\n     --input <source> \\\n     --out <final.png> \\\n     --auto-key border \\\n     --soft-matte \\\n     --transparent-threshold 12 \\\n     --opaque-threshold 220 \\\n     --despill\n   ```\n5. Validate that the output has an alpha channel, transparent corners, plausible subject coverage, and no obvious key-color fringe. If a thin fringe remains, retry once with `--edge-contract 1`; use `--edge-feather 0.25` only when the edge is visibly stair-stepped and the subject is not shiny or reflective.\n6. Save the final alpha PNG/WebP in the project if the asset is project-bound. Never leave a project-referenced transparent asset only under `$CODEX_HOME/*`.\n\nPrompt transparent requests like this:\n\n```text\nCreate the requested subject on a perfectly flat solid #00ff00 chroma-key background for background removal.\nThe background must be one uniform color with no shadows, gradients, texture, reflections, floor plane, or lighting variation.\nKeep the subject fully separated from the background with crisp edges and generous padding.\nDo not use #00ff00 anywhere in the subject.\nNo cast shadow, no contact shadow, no reflection, no watermark, and no text unless explicitly requested.\n```\n\nDo not automatically use CLI `gpt-image-1.5 --background transparent --output-format png` instead of chroma keying. Ask the user first when the user asks for true/native transparency, when local removal fails validation, or when the requested image is complex: hair, fur, feathers, smoke, glass, liquids, translucent materials, reflective objects, soft shadows, realistic product grounding, or subject colors that conflict with all practical key colors.\n\nUse a concise confirmation like:\n\n```text\nThis likely needs true native transparency. The default built-in path uses a chroma-key background plus local removal, but true transparency requires the CLI fallback with gpt-image-1.5 because gpt-image-2 does not support background=transparent. It also requires OPENAI_API_KEY. Should I proceed with that CLI fallback?\n```\n\n## Prompt augmentation\n\nReformat user prompts into a structured, production-oriented spec. Make the user's goal clearer and more actionable, but do not blindly add detail.\n\nTreat this as prompt-shaping guidance, not a closed schema. Use only the lines that help, and add a short extra labeled line when it materially improves clarity.\n\n### Specificity policy\n\nUse the user's prompt specificity to decide how much augmentation is appropriate:\n\n- If the prompt is already specific and detailed, preserve that specificity and only normalize/structure it.\n- If the prompt is generic, you may add tasteful augmentation when it will materially improve the result.\n\nAllowed augmentations:\n- composition or framing hints\n- polish level or intended-use hints\n- practical layout guidance\n- reasonable scene concreteness that supports the stated request\n\nNot allowed augmentations:\n- extra characters or objects that are not implied by the request\n- brand names, slogans, palettes, or narrative beats that are not implied\n- arbitrary side-specific placement unless the surrounding layout supports it\n\n## Use-case taxonomy (exact slugs)\n\nClassify each request into one of these buckets and keep the slug consistent across prompts and references.\n\nGenerate:\n- photorealistic-natural — candid/editorial lifestyle scenes with real texture and natural lighting.\n- product-mockup — product/packaging shots, catalog imagery, merch concepts.\n- ui-mockup — app/web interface mockups and wireframes; specify the desired fidelity.\n- infographic-diagram — diagrams/infographics with structured layout and text.\n- scientific-educational — classroom explainers, scientific diagrams, and learning visuals with required labels and accuracy constraints.\n- ads-marketing — campaign concepts and ad creatives with audience, brand position, scene, and exact tagline/copy.\n- productivity-visual — slide, chart, workflow, and data-heavy business visuals.\n- logo-brand — logo/mark exploration, vector-friendly.\n- illustration-story — comics, children’s book art, narrative scenes.\n- stylized-concept — style-driven concept art, 3D/stylized renders.\n- historical-scene — period-accurate/world-knowledge scenes.\n\nEdit:\n- text-localization — translate/replace in-image text, preserve layout.\n- identity-preserve — try-on, person-in-scene; lock face/body/pose.\n- precise-object-edit — remove/replace a specific element (including interior swaps).\n- lighting-weather — time-of-day/season/atmosphere changes only.\n- background-extraction — transparent background / clean cutout. Use built-in `image_gen` with chroma-key removal first for simple opaque subjects; ask before using CLI true transparency for complex subjects.\n- style-transfer — apply reference style while changing subject/scene.\n- compositing — multi-image insert/merge with matched lighting/perspective.\n- sketch-to-render — drawing/line art to photoreal render.\n\n## Shared prompt schema\n\nUse the following labeled spec as shared prompt scaffolding for both top-level modes:\n\n```text\nUse case: <taxonomy slug>\nAsset type: <where the asset will be used>\nPrimary request: <user's main prompt>\nInput images: <Image 1: role; Image 2: role> (optional)\nScene/backdrop: <environment>\nSubject: <main subject>\nStyle/medium: <photo/illustration/3D/etc>\nComposition/framing: <wide/close/top-down; placement>\nLighting/mood: <lighting + mood>\nColor palette: <palette notes>\nMaterials/textures: <surface details>\nText (verbatim): \"<exact text>\"\nConstraints: <must keep/must avoid>\nAvoid: <negative constraints>\n```\n\nNotes:\n- `Asset type` and `Input images` are prompt scaffolding, not dedicated CLI flags.\n- `Scene/backdrop` refers to the visual setting. It is not the same as the fallback CLI `background` parameter, which controls output transparency behavior.\n- Fallback-only execution notes such as `Quality:`, `Input fidelity:`, masks, output format, and output paths belong in the CLI path only. Do not treat them as built-in `image_gen` tool arguments.\n\nAugmentation rules:\n- Keep it short.\n- Add only the details needed to improve the prompt materially.\n- For edits, explicitly list invariants (`change only X; keep Y unchanged`).\n- If any critical detail is missing and blocks success, ask a question; otherwise proceed.\n\n## Examples\n\n### Generation example (hero image)\n```text\nUse case: product-mockup\nAsset type: landing page hero\nPrimary request: a minimal hero image of a ceramic coffee mug\nStyle/medium: clean product photography\nComposition/framing: wide composition with usable negative space for page copy if needed\nLighting/mood: soft studio lighting\nConstraints: no logos, no text, no watermark\n```\n\n### Edit example (invariants)\n```text\nUse case: precise-object-edit\nAsset type: product photo background replacement\nPrimary request: replace only the background with a warm sunset gradient\nConstraints: change only the background; keep the product and its edges unchanged; no text; no watermark\n```\n\n## Prompting best practices\n- Structure prompt as scene/backdrop -> subject -> details -> constraints.\n- Include intended use (ad, UI mock, infographic) to set the mode and polish level.\n- Use camera/composition language for photorealism.\n- Only use SVG/vector stand-ins when the user explicitly asked for vector output or a non-image placeholder.\n- Quote exact text and specify typography + placement.\n- For tricky words, spell them letter-by-letter and require verbatim rendering.\n- For multi-image inputs, reference images by index and describe how they should be used.\n- For edits, repeat invariants every iteration to reduce drift.\n- Iterate with single-change follow-ups.\n- If the prompt is generic, add only the extra detail that will materially help.\n- If the prompt is already detailed, normalize it instead of expanding it.\n- For CLI fallback only, see `references/cli.md` and `references/image-api.md` for model, `quality`, `input_fidelity`, masks, output format, and output-path guidance.\n- For transparent images, use the built-in-first chroma-key workflow unless the request is complex enough to need true CLI transparency; ask before switching to CLI `gpt-image-1.5`.\n\nMore principles shared by both modes: `references/prompting.md`.\nCopy/paste specs shared by both modes: `references/sample-prompts.md`.\n\n## Guidance by asset type\nAsset-type templates (website assets, game assets, wireframes, logo) are consolidated in `references/sample-prompts.md`.\n\n## gpt-image-2 guidance for CLI fallback\n\nThe fallback CLI defaults to `gpt-image-2`.\n\n- Use `gpt-image-2` for new CLI/API workflows unless the request needs true model-native transparent output.\n- If a transparent request may need 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 the built-in chroma-key path is the default, but true transparency requires `gpt-image-1.5` because `gpt-image-2` does not support `background=transparent`.\n- `gpt-image-2` always uses high fidelity for image inputs; do not set `input_fidelity` with this model.\n- `gpt-image-2` supports `quality` values `low`, `medium`, `high`, and `auto`.\n- Use `quality low` for fast drafts, thumbnails, and quick iterations. Use `medium`, `high`, or `auto` for final assets, dense text, diagrams, identity-sensitive edits, or high-resolution outputs.\n- Square images are typically fastest to generate. Use `1024x1024` for fast square drafts.\n- If the user asks for 4K-style output, use `3840x2160` for landscape or `2160x3840` for portrait.\n- `gpt-image-2` size may be `auto` or `WIDTHxHEIGHT` if all constraints hold: max edge `<= 3840px`, both edges multiples of `16px`, long-to-short ratio `<= 3:1`, total pixels between `655,360` and `8,294,400`.\n\nPopular `gpt-image-2` sizes:\n- `1024x1024` square\n- `1536x1024` landscape\n- `1024x1536` portrait\n- `2048x2048` 2K square\n- `2048x1152` 2K landscape\n- `3840x2160` 4K landscape\n- `2160x3840` 4K portrait\n- `auto`\n\n## Fallback CLI mode only\n\n### Temp and output conventions\nThese conventions apply only to the CLI fallback. They do not describe built-in `image_gen` output behavior.\n- Use `tmp/imagegen/` for intermediate files (for example JSONL batches); delete them when done.\n- Write final artifacts under `output/imagegen/`.\n- Use `--out` or `--out-dir` to control output paths; keep filenames stable and descriptive.\n\n### Dependencies\nPrefer `uv` for dependency management in this repo.\n\nRequired Python package:\n```bash\nuv pip install openai\n```\n\nRequired for local chroma-key removal and optional downscaling:\n```bash\nuv pip install pillow\n```\n\nPortability note:\n- If you are using the installed skill outside this repo, install dependencies into that environment with its package manager.\n- In uv-managed environments, `uv pip install ...` remains the preferred path.\n\n### Environment\n- `OPENAI_API_KEY` must be set for live API calls.\n- Do not ask the user for `OPENAI_API_KEY` when using the built-in `image_gen` tool.\n- Never ask the user to paste the full key in chat. Ask them to set it locally and confirm when ready.\n\nIf the key is missing, give the user these steps:\n1. Create an API key in the OpenAI platform UI: https://platform.openai.com/api-keys\n2. Set `OPENAI_API_KEY` as an environment variable in their system.\n3. Offer to guide them through setting the environment variable for their OS/shell if needed.\n\nIf installation is not possible in this environment, tell the user which dependency is missing and how to install it into their active environment.\n\n### Script-mode notes\n- CLI commands + examples: `references/cli.md`\n- API parameter quick reference: `references/image-api.md`\n- Network approvals / sandbox settings for CLI mode: `references/codex-network.md`\n\n## Reference map\n- `references/prompting.md`: shared prompting principles for both modes.\n- `references/sample-prompts.md`: shared copy/paste prompt recipes for both modes.\n- `references/cli.md`: fallback-only CLI usage via `scripts/image_gen.py`.\n- `references/image-api.md`: fallback-only API/CLI parameter reference.\n- `references/codex-network.md`: fallback-only network/sandbox troubleshooting for CLI mode.\n- `scripts/image_gen.py`: fallback-only CLI implementation. Do not load or use it unless the user explicitly chooses CLI mode or explicitly confirms a transparent request's true CLI transparency fallback.\n- `$CODEX_HOME/skills/.system/imagegen/scripts/remove_chroma_key.py`: local post-processing helper for built-in transparent-image requests.\n\nFile v1.0.0:_meta.json\n\n{\n  \"ownerId\": \"kn7dhjt1k1f111whp13fmrqwnh81tn1v\",\n  \"slug\": \"imagegen\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1781096284146\n}\n\nFile v1.0.0:references/cli.md\n\n# CLI reference (`scripts/image_gen.py`)\n\nThis file is for the fallback CLI mode only. Read it when the user explicitly asks to use `scripts/image_gen.py` / CLI / API / model controls, or after the user explicitly confirms that a transparent-output request should use the `gpt-image-1.5` true-transparency fallback path.\n\n`generate-batch` is a CLI subcommand in this fallback path. It is not a top-level mode of the skill.\nThe word `batch` in a user request is not CLI opt-in by itself.\n\n## What this CLI does\n- `generate`: generate a new image from a prompt\n- `edit`: edit one or more existing images\n- `generate-batch`: run many generation jobs from a JSONL file after the user explicitly chooses CLI/API/model controls\n\nReal API calls require **network access** + `OPENAI_API_KEY`. `--dry-run` does not.\n\n## Quick start (works from any repo)\nSet a stable path to the skill CLI (default `CODEX_HOME` is `~/.codex`):\n\n```\nexport CODEX_HOME=\"${CODEX_HOME:-$HOME/.codex}\"\nexport IMAGE_GEN=\"$CODEX_HOME/skills/.system/imagegen/scripts/image_gen.py\"\n```\n\nInstall dependencies into that environment with its package manager. In uv-managed environments, `uv pip install ...` remains the preferred path.\n\n## Quick start\n\nDry-run (no API call; no network required; does not require the `openai` package):\n\n```bash\npython \"$IMAGE_GEN\" generate \\\n  --prompt \"Test\" \\\n  --out output/imagegen/test.png \\\n  --dry-run\n```\n\nNotes:\n- One-off dry-runs print the API payload and the computed output path(s).\n- Repo-local finals should live under `output/imagegen/`.\n\nGenerate (requires `OPENAI_API_KEY` + network):\n\n```bash\npython \"$IMAGE_GEN\" generate \\\n  --prompt \"A cozy alpine cabin at dawn\" \\\n  --size 1024x1024 \\\n  --out output/imagegen/alpine-cabin.png\n```\n\nEdit:\n\n```bash\npython \"$IMAGE_GEN\" edit \\\n  --image input.png \\\n  --prompt \"Replace only the background with a warm sunset\" \\\n  --out output/imagegen/sunset-edit.png\n```\n\n## Guardrails\n- Use the bundled CLI directly (`python \"$IMAGE_GEN\" ...`) after activating the correct environment.\n- Do **not** create one-off runners (for example `gen_images.py`) unless the user explicitly asks for a custom wrapper.\n- **Never modify** `scripts/image_gen.py`. If something is missing, ask the user before doing anything else.\n- Do not silently downgrade from CLI `gpt-image-2` or built-in `image_gen` to CLI `gpt-image-1.5`; ask first unless the user already explicitly requested `gpt-image-1.5`, `scripts/image_gen.py`, or CLI fallback.\n\n## Defaults\n- Model: `gpt-image-2`\n- Supported model family for this CLI: GPT Image models (`gpt-image-*`)\n- Size: `auto`\n- Quality: `medium`\n- Output format: `png`\n- Default one-off output path: `output/imagegen/output.png`\n- Background: unspecified unless `--background` is set\n\n## gpt-image-2 size and model guidance\n\n`gpt-image-2` is the default model for new CLI fallback work.\n\n- Use `--quality low` for fast drafts, thumbnails, and quick iterations.\n- Use `--quality medium`, `--quality high`, or `--quality auto` for final assets, dense text, diagrams, identity-sensitive edits, and high-resolution outputs.\n- Square images are typically fastest. Use `--size 1024x1024` for quick square drafts.\n- If the user asks for 4K-style output, use `--size 3840x2160` for landscape or `--size 2160x3840` for portrait.\n- Do not pass `--input-fidelity` with `gpt-image-2`; this model always uses high fidelity for image inputs.\n- Do not use `--background transparent` with `gpt-image-2`; the default transparent-image workflow uses built-in `image_gen` on a flat chroma-key background plus local removal. Use `gpt-image-1.5` only after the user explicitly confirms the true-transparent CLI fallback, unless they already requested `gpt-image-1.5`, `scripts/image_gen.py`, or CLI fallback.\n\nPopular `gpt-image-2` sizes:\n- `1024x1024`\n- `1536x1024`\n- `1024x1536`\n- `2048x2048`\n- `2048x1152`\n- `3840x2160`\n- `2160x3840`\n- `auto`\n\n`gpt-image-2` size constraints:\n- max edge `<= 3840px`\n- both edges multiples of `16px`\n- long edge to short edge ratio `<= 3:1`\n- total pixels between `655,360` and `8,294,400`\n- outputs above `2560x1440` total pixels are experimental\n\nFast draft:\n\n```bash\npython \"$IMAGE_GEN\" generate \\\n  --prompt \"A product thumbnail of a matte ceramic mug on a stone surface\" \\\n  --quality low \\\n  --size 1024x1024 \\\n  --out output/imagegen/mug-draft.png\n```\n\nFinal 2K landscape:\n\n```bash\npython \"$IMAGE_GEN\" generate \\\n  --prompt \"A polished landing-page hero image of a matte ceramic mug on a stone surface\" \\\n  --quality high \\\n  --size 2048x1152 \\\n  --out output/imagegen/mug-hero.png\n```\n\n4K landscape:\n\n```bash\npython \"$IMAGE_GEN\" generate \\\n  --prompt \"A detailed architectural visualization at golden hour\" \\\n  --size 3840x2160 \\\n  --quality high \\\n  --out output/imagegen/architecture-4k.png\n```\n\nTrue transparent fallback request:\n\nAsk for confirmation before using this command unless the user already explicitly requested `gpt-image-1.5`, `scripts/image_gen.py`, or CLI fallback.\n\n```bash\npython \"$IMAGE_GEN\" generate \\\n  --model gpt-image-1.5 \\\n  --prompt \"A clean product cutout on a transparent background\" \\\n  --background transparent \\\n  --output-format png \\\n  --out output/imagegen/product-cutout.png\n```\n\nWhen using this path, explain briefly that built-in `image_gen` plus chroma-key removal is the default transparent-image path, but this request needs true model-native transparency. `gpt-image-2` does not support `background=transparent`, so `gpt-image-1.5` is required for this confirmed fallback.\n\n## Quality, input fidelity, and masks (CLI fallback only)\nThese are explicit CLI controls. They are not built-in `image_gen` tool arguments.\n\n- `--quality` works for `generate`, `edit`, and `generate-batch`: `low|medium|high|auto`\n- `--input-fidelity` is **edit-only** and validated as `low|high`; it is not supported for `gpt-image-2`\n- `--mask` is **edit-only**\n\nExample:\n\n```bash\npython \"$IMAGE_GEN\" edit \\\n  --model gpt-image-1.5 \\\n  --image input.png \\\n  --prompt \"Change only the background\" \\\n  --quality high \\\n  --input-fidelity high \\\n  --out output/imagegen/background-edit.png\n```\n\nMask notes:\n- For multi-image edits, pass repeated `--image` flags. Their order is meaningful, so describe each image by index and role in the prompt.\n- The CLI accepts a single `--mask`.\n- Image and mask must be the same size and format and each under 50MB.\n- Masks must include an alpha channel.\n- If multiple input images are provided, the mask applies to the first image.\n- Masking is prompt-guided; do not promise exact pixel-perfect mask boundaries.\n- Use a PNG mask when possible; the script treats mask handling as best-effort and does not perform full preflight validation beyond file checks/warnings.\n- In the edit prompt, repeat invariants (`change only the background; keep the subject unchanged`) to reduce drift.\n\n## Output handling\n- Use `tmp/imagegen/` for temporary JSONL inputs or scratch files.\n- Use `output/imagegen/` for final outputs.\n- Reruns fail if a target file already exists unless you pass `--force`.\n- `--out-dir` changes one-off naming to `image_1.<ext>`, `image_2.<ext>`, and so on.\n- Downscaled copies use the default suffix `-web` unless you override it.\n\n## Common recipes\n\nGenerate with augmentation fields:\n\n```bash\npython \"$IMAGE_GEN\" generate \\\n  --prompt \"A minimal hero image of a ceramic coffee mug\" \\\n  --use-case \"product-mockup\" \\\n  --style \"clean product photography\" \\\n  --composition \"wide product shot with usable negative space for page copy\" \\\n  --constraints \"no logos, no text\" \\\n  --out output/imagegen/mug-hero.png\n```\n\nGenerate + also write a downscaled copy for fast web loading:\n\n```bash\npython \"$IMAGE_GEN\" generate \\\n  --prompt \"A cozy alpine cabin at dawn\" \\\n  --size 1024x1024 \\\n  --downscale-max-dim 1024 \\\n  --out output/imagegen/alpine-cabin.png\n```\n\nGenerate multiple prompts concurrently (async batch):\n\n```bash\nmkdir -p tmp/imagegen output/imagegen/batch\ncat > tmp/imagegen/prompts.jsonl << 'EOF'\n{\"prompt\":\"Cavernous hangar interior with a compact shuttle parked near the center\",\"use_case\":\"stylized-concept\",\"composition\":\"wide-angle, low-angle\",\"lighting\":\"volumetric light rays through drifting fog\",\"constraints\":\"no logos or trademarks; no watermark\",\"size\":\"1536x1024\"}\n{\"prompt\":\"Gray wolf in profile in a snowy forest\",\"use_case\":\"photorealistic-natural\",\"composition\":\"eye-level\",\"constraints\":\"no logos or trademarks; no watermark\",\"size\":\"1024x1024\"}\nEOF\n\npython \"$IMAGE_GEN\" generate-batch \\\n  --input tmp/imagegen/prompts.jsonl \\\n  --out-dir output/imagegen/batch \\\n  --concurrency 5\n\nrm -f tmp/imagegen/prompts.jsonl\n```\n\nNotes:\n- `generate-batch` requires `--out-dir`.\n- generate-batch requires --out-dir.\n- Use `--concurrency` to control parallelism (default `5`).\n- Per-job overrides are supported in JSONL (for example `size`, `quality`, `background`, `output_format`, `output_compression`, `moderation`, `n`, `model`, `out`, and prompt-augmentation fields).\n- `--n` generates multiple variants for a single prompt; `generate-batch` is for many different prompts.\n- In batch mode, per-job `out` is treated as a filename under `--out-dir`.\n- For many requested deliverable assets, provide one prompt/job per distinct asset and use semantic filenames when possible.\n\n## CLI notes\n- Supported sizes depend on the model. `gpt-image-2` supports flexible constrained sizes; older GPT Image models support `1024x1024`, `1536x1024`, `1024x1536`, or `auto`.\n- True transparent CLI outputs require `output_format` to be `png` or `webp` and are not supported by `gpt-image-2`.\n- `--prompt-file`, `--output-compression`, `--moderation`, `--max-attempts`, `--fail-fast`, `--force`, and `--no-augment` are supported.\n- This CLI is intended for GPT Image models. Do not assume older non-GPT image-model behavior applies here.\n\n## See also\n- API parameter quick reference for fallback CLI mode: `references/image-api.md`\n- Prompt examples shared across both top-level modes: `references/sample-prompts.md`\n- Network/sandbox notes for fallback CLI mode: `references/codex-network.md`\n- Built-in-first transparent image workflow: `SKILL.md` and `$CODEX_HOME/skills/.system/imagegen/scripts/remove_chroma_key.py`\n\nFile v1.0.0:references/codex-network.md\n\n# Codex network approvals / sandbox notes\n\nThis file is for the fallback CLI mode only. Read it when the user explicitly asks to use `scripts/image_gen.py` / CLI / API / model controls, or after the user explicitly confirms that a transparent-output request should use the `gpt-image-1.5` true-transparency fallback path.\n\nThis guidance is intentionally isolated from `SKILL.md` because it can vary by environment and may become stale. Prefer the defaults in your environment when in doubt.\n\n## Why am I asked to approve image generation calls?\nThe fallback CLI uses the OpenAI Image API, so it needs outbound network access. In many Codex setups, network access is disabled by default and/or the approval policy requires confirmation before networked commands run.\n\n## Important note about approvals vs network\n- `--ask-for-approval never` suppresses approval prompts.\n- It does **not** by itself enable network access.\n- In `workspace-write`, network access still depends on your Codex configuration (for example `[sandbox_workspace_write] network_access = true`).\n\n## How do I reduce repeated approval prompts?\nIf you trust the repo and want fewer prompts, use a configuration or profile that both:\n- enables network for the sandbox mode you plan to use\n- sets an approval policy that matches your risk tolerance\n\nExample `~/.codex/config.toml` pattern:\n\n```toml\napproval_policy = \"on-request\"\nsandbox_mode = \"workspace-write\"\n\n[sandbox_workspace_write]\nnetwork_access = true\n```\n\nIf you want quieter automation after network is enabled, you can choose a stricter approval policy, but do that intentionally and with care.\n\n## Safety note\nEnabling network and reducing approvals lowers friction, but increases risk if you run untrusted code or work in an untrusted repository.\n\nFile v1.0.0:references/image-api.md\n\n# Image API quick reference\n\nThis file is for the fallback CLI mode only. Use it when the user explicitly asks to use `scripts/image_gen.py` / CLI / API / model controls, or after the user explicitly confirms that a transparent-output request should use the `gpt-image-1.5` true-transparency fallback path.\n\nThese parameters describe the Image API and bundled CLI fallback surface. Do not assume they are normal arguments on the built-in `image_gen` tool.\n\n## Scope\n- This fallback CLI is intended for GPT Image models (`gpt-image-2`, `gpt-image-1.5`, `gpt-image-1`, and `gpt-image-1-mini`).\n- The built-in `image_gen` tool and the fallback CLI do not expose the same controls.\n\n## Model summary\n\n| Model | Quality | Input fidelity | Resolutions | Recommended use |\n| --- | --- | --- | --- | --- |\n| `gpt-image-2` | `low`, `medium`, `high`, `auto` | Always high fidelity for image inputs; do not set `input_fidelity` | `auto` or flexible sizes that satisfy the constraints below | Default for new CLI/API workflows: high-quality generation and editing, text-heavy images, photorealism, compositing, identity-sensitive edits, and workflows where fewer retries matter |\n| `gpt-image-1.5` | `low`, `medium`, `high`, `auto` | `low`, `high` | `1024x1024`, `1024x1536`, `1536x1024`, `auto` | True transparent-background fallback and backward-compatible workflows |\n| `gpt-image-1` | `low`, `medium`, `high`, `auto` | `low`, `high` | `1024x1024`, `1024x1536`, `1536x1024`, `auto` | Legacy compatibility |\n| `gpt-image-1-mini` | `low`, `medium`, `high`, `auto` | `low`, `high` | `1024x1024`, `1024x1536`, `1536x1024`, `auto` | Cost-sensitive draft batches and lower-stakes previews |\n\n## gpt-image-2 sizes\n\n`gpt-image-2` accepts `auto` or any `WIDTHxHEIGHT` size that satisfies all constraints:\n\n- Maximum edge length must be less than or equal to `3840px`.\n- Both edges must be multiples of `16px`.\n- Long edge to short edge ratio must not exceed `3:1`.\n- Total pixels must be at least `655,360` and no more than `8,294,400`.\n\nPopular sizes:\n\n| Label | Size | Notes |\n| --- | --- | --- |\n| Square | `1024x1024` | Typical fast default |\n| Landscape | `1536x1024` | Standard landscape |\n| Portrait | `1024x1536` | Standard portrait |\n| 2K square | `2048x2048` | Larger square output |\n| 2K landscape | `2048x1152` | Widescreen output |\n| 4K landscape | `3840x2160` | Widescreen 4K output |\n| 4K portrait | `2160x3840` | Vertical 4K output |\n| Auto | `auto` | Default size |\n\nSquare images are typically fastest to generate. For 4K-style output, use `3840x2160` or `2160x3840`.\n\n## Endpoints\n- Generate: `POST /v1/images/generations` (`client.images.generate(...)`)\n- Edit: `POST /v1/images/edits` (`client.images.edit(...)`)\n\n## Core parameters for GPT Image models\n- `prompt`: text prompt\n- `model`: image model\n- `n`: number of images (1-10)\n- `size`: `auto` by default for `gpt-image-2`; flexible `WIDTHxHEIGHT` sizes are allowed only for `gpt-image-2`; older GPT Image models use `1024x1024`, `1536x1024`, `1024x1536`, or `auto`\n- `quality`: `low`, `medium`, `high`, or `auto`\n- `background`: output transparency behavior (`transparent`, `opaque`, or `auto`) for generated output; this is not the same thing as the prompt's visual scene/backdrop\n- `output_format`: `png` (default), `jpeg`, `webp`\n- `output_compression`: 0-100 (jpeg/webp only)\n- `moderation`: `auto` (default) or `low`\n\n## Edit-specific parameters\n- `image`: one or more input images. For GPT Image models, you can provide up to 16 images.\n- `mask`: optional mask image\n- `input_fidelity`: `low` or `high` only for models that support it; do not set this for `gpt-image-2`\n\nModel-specific note for `input_fidelity`:\n- `gpt-image-2` always uses high fidelity for image inputs and does not support setting `input_fidelity`.\n- `gpt-image-1` and `gpt-image-1-mini` preserve all input images, but the first image gets richer textures and finer details.\n- `gpt-image-1.5` preserves the first 5 input images with higher fidelity.\n\n## Transparent backgrounds\n\n`gpt-image-2` does not currently support the Image API `background=transparent` parameter. The skill's default transparent-image path is built-in `image_gen` with a flat chroma-key background, followed by local alpha extraction with `python \"${CODEX_HOME:-$HOME/.codex}/skills/.system/imagegen/scripts/remove_chroma_key.py\"`.\n\nUse CLI `gpt-image-1.5` with `background=transparent` and a transparent-capable output format such as `png` or `webp` only after the user explicitly confirms that fallback, unless they already requested `gpt-image-1.5`, `scripts/image_gen.py`, or CLI fallback. If the user asks for true/native transparency, the subject is too complex for clean chroma-key removal, or local background removal fails validation, explain the tradeoff and ask before switching.\n\n## Output\n- `data[]` list with `b64_json` per image\n- The bundled `scripts/image_gen.py` CLI decodes `b64_json` and writes output files for you.\n\n## Limits and notes\n- Input images and masks must be under 50MB.\n- Use the edits endpoint when the user requests changes to an existing image.\n- Masking is prompt-guided; exact shapes are not guaranteed.\n- Large sizes and high quality increase latency and cost.\n- Use `quality=low` for fast drafts, thumbnails, and quick iterations. Use `medium` or `high` for final assets, dense text, diagrams, identity-sensitive edits, or high-resolution outputs.\n- High `input_fidelity` can materially increase input token usage on models that support it.\n- If a request fails because a specific option is unsupported by the selected GPT Image model, retry manually without that option only when the option is not required by the user. If true transparent CLI output is required, ask before switching to `gpt-image-1.5` instead of dropping `background=transparent`, unless the user already explicitly chose that fallback.\n\n## Important boundary\n- `quality`, `input_fidelity`, explicit masks, `background`, `output_format`, and related parameters are fallback-only execution controls.\n- Do not assume they are built-in `image_gen` tool arguments.\n\nFile v1.0.0:references/prompting.md\n\n# Prompting best practices\n\nThese prompting principles are shared by both top-level modes of the skill:\n- built-in `image_gen` tool (default)\n- explicit `scripts/image_gen.py` CLI fallback\n\nThis 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.\n\n## Contents\n- [Structure](#structure)\n- [Specificity policy](#specificity-policy)\n- [Allowed and disallowed augmentation](#allowed-and-disallowed-augmentation)\n- [Composition and layout](#composition-and-layout)\n- [Constraints and invariants](#constraints-and-invariants)\n- [Text in images](#text-in-images)\n- [Input images and references](#input-images-and-references)\n- [Iterate deliberately](#iterate-deliberately)\n- [Transparent images](#transparent-images)\n- [Fallback-only execution controls](#fallback-only-execution-controls)\n- [Use-case tips](#use-case-tips)\n- [Where to find copy/paste recipes](#where-to-find-copypaste-recipes)\n\n## Structure\n- Use a consistent order: scene/backdrop -> subject -> key details -> constraints -> output intent.\n- Include intended use (ad, UI mock, infographic) to set the level of polish.\n- For complex requests, use short labeled lines instead of one long paragraph.\n\n## Specificity policy\n- If the user prompt is already specific and detailed, normalize it into a clean spec without adding creative requirements.\n- If the prompt is generic, you may add tasteful detail when it materially improves the output.\n- Treat examples in `sample-prompts.md` as fully-authored recipes, not as the default amount of augmentation to add to every request.\n- 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.\n\n## Allowed and disallowed augmentation\n\nAllowed augmentation for generic prompts:\n- composition and framing cues\n- intended-use or polish-level hints\n- practical layout guidance\n- reasonable scene concreteness that supports the request\n\nDo not add:\n- extra characters, props, or objects that are not implied\n- brand palettes, slogans, or story beats that are not implied\n- arbitrary side-specific placement unless the surrounding layout supports it\n\n## Composition and layout\n- Specify framing and viewpoint (close-up, wide, top-down) and placement only when it materially helps.\n- Call out negative space if the asset clearly needs room for UI or copy.\n- Avoid making left/right layout decisions unless the user or surrounding layout supports them.\n- 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`).\n\n## Constraints and invariants\n- State what must not change (`keep background unchanged`).\n- For edits, say `change only X; keep Y unchanged` and repeat invariants on every iteration to reduce drift.\n\n## Text in images\n- Put literal text in quotes or ALL CAPS and specify typography (font style, size, color, placement).\n- Spell uncommon words letter-by-letter if accuracy matters.\n- For in-image copy, require verbatim rendering and no extra characters.\n- In CLI fallback mode, use `medium` or `high` quality for small text, dense infographics, data-heavy slides, multi-font layouts, legends, axes, and footnotes.\n\n## Input images and references\n- Do not assume that every provided image is an edit target.\n- Label each image by index and role (`Image 1: edit target`, `Image 2: style reference`).\n- If the user provides images for style, composition, or mood guidance and does not ask to modify them, treat the request as generation with references.\n- If the user asks to preserve an existing image while changing specific parts, treat the request as an edit.\n- For compositing, describe how the images interact (`place the subject from Image 2 into Image 1`).\n\n## Iterate deliberately\n- Start with a clean base prompt, then make small single-change edits.\n- Re-specify critical constraints when you iterate.\n- Prefer one targeted follow-up at a time over rewriting the whole prompt.\n\n## Transparent images\n- Use built-in `image_gen` first for transparent-image requests. If the subject is clearly too complex for chroma-key removal, explain the fallback and ask before switching to CLI.\n- Prompt for a perfectly flat solid chroma-key background, usually `#00ff00`; use `#ff00ff` when the subject is green, and avoid key colors that appear in the subject.\n- Explicitly prohibit shadows, gradients, floor planes, reflections, texture, and lighting variation in the background.\n- Ask for crisp edges, generous padding, and no use of the key color inside the subject.\n- After generation, remove the background locally with `python \"${CODEX_HOME:-$HOME/.codex}/skills/.system/imagegen/scripts/remove_chroma_key.py\" --input <source> --out <final.png> --auto-key border --soft-matte --transparent-threshold 12 --opaque-threshold 220 --despill` and validate the alpha result before shipping it.\n- Use soft matte and despill for antialiased edges; hard tolerance-only removal is mainly for flat pixel-art or exact-color fixtures.\n- Use CLI `gpt-image-1.5 --background transparent --output-format png` only after the user explicitly confirms the fallback, or when the user already explicitly requested `gpt-image-1.5`, `scripts/image_gen.py`, or CLI fallback. Ask first for true/native transparency requests, failed chroma-key validation, or complex transparent subjects such as hair, fur, glass, smoke, liquids, translucent materials, reflective objects, or soft shadows.\n\n## Fallback-only execution controls\n- `quality`, `input_fidelity`, explicit masks, output format, and output paths are fallback-only execution controls.\n- Do not assume they are built-in `image_gen` tool arguments.\n- If the user explicitly chooses CLI fallback, see `references/cli.md` and `references/image-api.md` for those controls.\n- In CLI fallback mode, `gpt-image-2` is the default. It supports `quality=low|medium|high|auto`; use `low` for fast drafts and thumbnails, and move to `medium`, `high`, or `auto` for final assets.\n- `gpt-image-2` always uses high fidelity for image inputs, so do not set `input_fidelity` with that model.\n- If a transparent request needs true CLI transparency, ask before using `gpt-image-1.5` unless the user already explicitly chose it. Explain that built-in chroma-key removal is the default path, but `gpt-image-2` does not support `background=transparent`.\n- If the user asks for 4K-style output with `gpt-image-2`, use `3840x2160` for landscape or `2160x3840` for portrait.\n\n## Use-case tips\nGenerate:\n- photorealistic-natural: Prompt as if a real photo is captured in the moment; use photography language (lens, lighting, framing); call for real texture; avoid over-stylized polish unless requested.\n- product-mockup: Describe the product/packaging and materials; ensure clean silhouette and label clarity; if in-image text is needed, require verbatim rendering and specify typography.\n- ui-mockup: Describe the target fidelity first (shippable mockup or low-fi wireframe), then focus on layout, hierarchy, and practical UI elements; avoid concept-art language.\n- infographic-diagram: Define the audience and layout flow; label parts explicitly; require verbatim text; prefer higher quality in CLI mode for dense labels.\n- logo-brand: Keep it simple and scalable; ask for a strong silhouette and balanced negative space; avoid decorative flourishes unless requested.\n- ads-marketing: Write like a creative brief; include brand positioning, audience, desired vibe, scene, and exact tagline if text must appear.\n- productivity-visual: Name the exact artifact (slide, chart, workflow diagram), define the canvas and hierarchy, provide real labels/data, and ask for readable typography and polished spacing.\n- scientific-educational: Define audience, lesson objective, required labels, scientific constraints, arrows, and scan-friendly whitespace.\n- illustration-story: Define panels or scene beats; keep each action concrete.\n- stylized-concept: Specify style cues, material finish, and rendering approach (3D, painterly, clay) without inventing new story elements.\n- historical-scene: State the location/date and required period accuracy; constrain clothing, props, and environment to match the era.\n\nEdit:\n- text-localization: Change only the text; preserve layout, typography, spacing, and hierarchy; no extra words or reflow unless needed.\n- identity-preserve: Lock identity (face, body, pose, hair, expression); change only the specified elements; match lighting and shadows.\n- precise-object-edit: Specify exactly what to remove/replace; preserve surrounding texture and lighting; keep everything else unchanged.\n- lighting-weather: Change only environmental conditions (light, shadows, atmosphere, precipitation); keep geometry, framing, and subject identity.\n- background-extraction: For simple opaque subjects, request a clean cutout on a perfectly flat chroma-key background; crisp silhouette; generous padding; no shadows; no halos; preserve label text exactly; no restyling. Ask before using true CLI transparency for complex subjects.\n- style-transfer: Specify style cues to preserve (palette, texture, brushwork) and what must change; add `no extra elements` to prevent drift.\n- compositing: Reference inputs by index; specify what moves where; match lighting, perspective, and scale; keep the base framing unchanged.\n- sketch-to-render: Preserve layout, proportions, and perspective; choose materials and lighting that support the supplied sketch without adding new elements.\n\n## Where to find copy/paste recipes\nFor copy/paste prompt specs (examples only), see `references/sample-prompts.md`. This file focuses on principles, specificity, and iteration patterns.\n\nFile v1.0.0:references/sample-prompts.md\n\n# Sample prompts (copy/paste)\n\nThese prompt recipes are shared across both top-level modes of the skill:\n- built-in `image_gen` tool (default)\n- `scripts/image_gen.py` CLI fallback for explicit CLI/API/model requests or user-confirmed true-transparent-output fallback requests\n\nUse these as starting points. They are intentionally complete prompt recipes, not the default amount of augmentation to add to every user request.\n\nWhen adapting a user's prompt:\n- keep user-provided requirements\n- only add detail according to the specificity policy in `SKILL.md`\n- do not treat every example below as permission to invent extra story elements\n\nThe 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.\n\nExecution 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.\n\nCLI model notes:\n- `gpt-image-2` is the fallback CLI default for new workflows.\n- `gpt-image-2` supports `quality` values `low`, `medium`, `high`, and `auto`.\n- For 4K-style `gpt-image-2` output, use `3840x2160` or `2160x3840`.\n- 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`.\n- Do not set `input_fidelity` with `gpt-image-2`; image inputs already use high fidelity.\n\nFor prompting principles (structure, specificity, invariants, iteration), see `references/prompting.md`.\n\n## Generate\n\n### photorealistic-natural\n```\nUse case: photorealistic-natural\nPrimary request: candid photo of an elderly sailor on a small fishing boat adjusting a net\nScene/backdrop: coastal water with soft haze\nSubject: weathered skin with wrinkles and sun texture\nStyle/medium: photorealistic candid photo\nComposition/framing: medium close-up, eye-level\nLighting/mood: soft coastal daylight, shallow depth of field, subtle film grain\nMaterials/textures: real skin texture, worn fabric, salt-worn wood\nConstraints: natural color balance; no heavy retouching; no glamorization; no watermark\nAvoid: studio polish; staged look\n```\n\n### product-mockup\n```\nUse case: product-mockup\nPrimary request: premium product photo of a matte black shampoo bottle with a minimal label\nScene/backdrop: clean studio gradient from light gray to white\nSubject: single bottle centered with subtle reflection\nStyle/medium: premium product photography\nComposition/framing: centered, slight three-quarter angle, generous padding\nLighting/mood: softbox lighting, clean highlights, controlled shadows\nMaterials/textures: matte plastic, crisp label printing\nConstraints: no logos or trademarks; no watermark\n```\n\n### ui-mockup\n```\nUse case: ui-mockup\nPrimary request: mobile app home screen for a local farmers market with vendors and daily specials\nAsset type: mobile app screen\nStyle/medium: realistic product UI, not concept art\nComposition/framing: clean vertical mobile layout with clear hierarchy\nConstraints: practical layout, clear typography, no logos or trademarks, no watermark\n```\n\n### infographic-diagram\n```\nUse case: infographic-diagram\nPrimary request: detailed infographic of an automatic coffee machine flow\nScene/backdrop: clean, light neutral background\nSubject: bean hopper -> grinder -> brew group -> boiler -> water tank -> drip tray\nStyle/medium: clean vector-like infographic with clear callouts and arrows\nComposition/framing: vertical poster layout, top-to-bottom flow\nText (verbatim): \"Bean Hopper\", \"Grinder\", \"Brew Group\", \"Boiler\", \"Water Tank\", \"Drip Tray\"\nConstraints: clear labels, strong contrast, no logos or trademarks, no watermark\n```\n\n### scientific-educational\n```\nUse case: scientific-educational\nPrimary request: biology diagram titled \"Cellular Respiration at a Glance\" for high school students\nScene/backdrop: clean white classroom handout background\nSubject: glucose turns into energy inside a cell; include glycolysis, Krebs cycle, and electron transport chain\nStyle/medium: flat scientific diagram with consistent icons, arrows, and readable labels\nComposition/framing: landscape slide-style layout with clear hierarchy and generous whitespace\nText (verbatim): \"Cellular Respiration at a Glance\", \"Glucose\", \"Pyruvate\", \"ATP\", \"NADH\", \"FADH2\", \"CO2\", \"O2\", \"H2O\"\nConstraints: scientifically plausible; avoid tiny text; no extra decoration; no watermark\n```\n\n### logo-brand\n```\nUse case: logo-brand\nPrimary request: original logo for \"Field & Flour\", a local bakery\nStyle/medium: vector logo mark; flat colors; minimal\nComposition/framing: single centered logo on a plain background with generous padding\nConstraints: strong silhouette, balanced negative space; original design only; no gradients unless essential; no trademarks; no watermark\n```\n\n### illustration-story\n```\nUse case: illustration-story\nPrimary request: 4-panel comic about a pet left alone at home\nScene/backdrop: cozy living room across panels\nSubject: pet reacting to the owner leaving, then relaxing, then returning to a composed pose\nStyle/medium: comic illustration with clear panels\nComposition/framing: 4 equal-sized vertical panels, readable actions per panel\nConstraints: no text; no logos or trademarks; no watermark\n```\n\n### stylized-concept\n```\nUse case: stylized-concept\nPrimary request: cavernous hangar interior with tall support beams and drifting fog\nScene/backdrop: industrial hangar interior, deep scale, light haze\nSubject: compact shuttle parked near the center\nStyle/medium: cinematic concept art, industrial realism\nComposition/framing: wide-angle, low-angle\nLighting/mood: volumetric light rays cutting through fog\nConstraints: no logos or trademarks; no watermark\n```\n\n### ads-marketing\n```\nUse case: ads-marketing\nPrimary request: campaign image for a streetwear brand called Thread\nSubject: group of friends hanging out together in a stylish urban setting\nStyle/medium: polished youth streetwear campaign photography\nComposition/framing: vertical ad layout with natural poses and integrated headline space\nLighting/mood: contemporary, energetic, tasteful\nText (verbatim): \"Yours to Create.\"\nConstraints: render the tagline exactly once; clean legible typography; no extra text; no watermarks; no unrelated logos\n```\n\n### productivity-visual\n```\nUse case: productivity-visual\nPrimary request: one pitch-deck slide titled \"Market Opportunity\"\nAsset type: fundraising slide image\nStyle/medium: clean modern deck slide, white background, crisp sans-serif typography\nSubject: TAM/SAM/SOM concentric-circle diagram plus a small growth bar chart from 2021 to 2026\nComposition/framing: 16:9 landscape slide, clear data hierarchy, polished spacing\nText (verbatim): \"Market Opportunity\", \"TAM: $42B\", \"SAM: $8.7B\", \"SOM: $340M\", \"AGI Research, 2024\", \"Internal analysis\"\nConstraints: readable labels, no clip art, no stock photography, no decorative clutter, no watermark\n```\n\n### historical-scene\n```\nUse case: historical-scene\nPrimary request: outdoor crowd scene in Bethel, New York on August 16, 1969\nScene/backdrop: open field with period-appropriate staging\nSubject: crowd in period-accurate clothing, authentic environment\nStyle/medium: photorealistic photo\nComposition/framing: wide shot, eye-level\nConstraints: period-accurate details; no modern objects; no logos or trademarks; no watermark\n```\n\n## Asset type templates (taxonomy-aligned)\n\n### Website assets template\n```\nUse case: <photorealistic-natural|stylized-concept|product-mockup|infographic-diagram|ui-mockup>\nAsset type: <hero image / section illustration / blog header>\nPrimary request: <short description>\nScene/backdrop: <environment or abstract backdrop>\nSubject: <main subject>\nStyle/medium: <photo/illustration/3D>\nComposition/framing: <wide/centered; note usable negative space only if needed>\nLighting/mood: <soft/bright/neutral>\nColor palette: <brand colors or neutral>\nConstraints: <no text; no logos; no watermark; leave room for UI if needed>\n```\n\n### Website assets example: minimal hero background\n```\nUse case: stylized-concept\nAsset type: landing page hero background\nPrimary request: minimal abstract background with a soft gradient and subtle texture\nStyle/medium: matte illustration / soft-rendered abstract background\nComposition/framing: wide composition with usable negative space for page copy\nLighting/mood: gentle studio glow\nColor palette: restrained neutral palette\nConstraints: no text; no logos; no watermark\n```\n\n### Website assets example: feature section illustration\n```\nUse case: stylized-concept\nAsset type: feature section illustration\nPrimary request: simple abstract shapes suggesting connection and flow\nScene/backdrop: subtle light-gray backdrop with faint texture\nStyle/medium: flat illustration; soft shadows; restrained contrast\nComposition/framing: centered cluster; open margins for UI\nColor palette: muted neutral palette\nConstraints: no text; no logos; no watermark\n```\n\n### Website assets example: blog header image\n```\nUse case: photorealistic-natural\nAsset type: blog header image\nPrimary request: overhead desk scene with notebook, pen, and coffee cup\nScene/backdrop: warm wooden tabletop\nStyle/medium: photorealistic photo\nComposition/framing: wide crop with clean room for page copy\nLighting/mood: soft morning light\nConstraints: no text; no logos; no watermark\n```\n\n### Game assets template\n```\nUse case: stylized-concept\nAsset type: <game environment concept art / game character concept / game UI icon / tileable game texture>\nPrimary request: <biome/scene/character/icon/material>\nScene/backdrop: <location + set dressing> (if applicable)\nSubject: <main focal element(s)>\nStyle/medium: <realistic/stylized>; <concept art / character render / UI icon / texture>\nComposition/framing: <wide/establishing/top-down>; <camera angle>; <focal point placement>\nLighting/mood: <time of day>; <mood>; <volumetric/fog/etc>\nConstraints: no logos or trademarks; no watermark\n```\n\n### Game assets example: environment concept art\n```\nUse case: stylized-concept\nAsset type: game environment concept art\nPrimary request: cavernous hangar interior with tall support beams and drifting fog\nScene/backdrop: industrial hangar interior, deep scale, light haze\nSubject: compact shuttle parked near the center\nStyle/medium: cinematic concept art, industrial realism\nComposition/framing: wide-angle, low-angle\nLighting/mood: volumetric light rays cutting through fog\nConstraints: no logos or trademarks; no watermark\n```\n\n### Game assets example: character concept\n```\nUse case: stylized-concept\nAsset type: game character concept\nPrimary request: desert scout character with layered travel gear\nSubject: long coat, satchel, practical travel clothing\nStyle/medium: character render; stylized realism\nComposition/framing: neutral hero pose on a simple backdrop\nConstraints: no logos or trademarks; no watermark\n```\n\n### Game assets example: UI icon\n```\nUse case: stylized-concept\nAsset type: game UI icon\nPrimary request: round shield icon with a subtle rune pattern\nStyle/medium: painted game UI icon\nComposition/framing: centered icon; generous padding; clear silhouette\nConstraints: no text; no background scene elements; no logos or trademarks; no watermark\n```\n\n### Game assets example: tileable texture\n```\nUse case: stylized-concept\nAsset type: tileable game texture\nPrimary request: worn sandstone blocks\nStyle/medium: seamless tileable texture; PBR-ish look\nScene/backdrop: neutral lighting reference only\nConstraints: seamless edges; no obvious focal elements; no text; no logos or trademarks; no watermark\n```\n\n### Wireframe template\n```\nUse case: ui-mockup\nAsset type: website wireframe\nPrimary request: <page or flow to sketch>\nStyle/medium: low-fi grayscale wireframe\nComposition/framing: <landscape or portrait to match expected device>\nSubject: <sections in order; grid/columns; key labels>\nConstraints: no color; no logos; no real photos; no watermark\n```\n\n### Wireframe example: homepage (desktop)\n```\nUse case: ui-mockup\nAsset type: website wireframe\nPrimary request: SaaS homepage layout with clear hierarchy\nStyle/medium: low-fi grayscale wireframe\nSubject: top nav; hero with headline and CTA; three feature cards; testimonial strip; pricing preview; footer\nComposition/framing: landscape desktop layout\nConstraints: label major blocks; no color; no logos; no real photos; no watermark\n```\n\n### Wireframe example: pricing page\n```\nUse case: ui-mockup\nAsset type: website wireframe\nPrimary request: pricing page layout with comparison table\nStyle/medium: low-fi grayscale wireframe\nSubject: header; plan toggle; 3 pricing cards; comparison table; FAQ accordion; footer\nComposition/framing: desktop or tablet layout\nConstraints: label key areas; no color; no logos; no real photos; no watermark\n```\n\n### Wireframe example: mobile onboarding flow\n```\nUse case: ui-mockup\nAsset type: mobile onboarding wireframe\nPrimary request: three-screen mobile onboarding flow\nStyle/medium: low-fi grayscale wireframe\nSubject: screen 1 headline and CTA; screen 2 feature bullets; screen 3 form fields and CTA\nComposition/framing: portrait mobile layout\nConstraints: label screens and blocks; no color; no logos; no real photos; no watermark\n```\n\n### Logo template\n```\nUse case: logo-brand\nAsset type: logo concept\nPrimary request: <brand idea or symbol concept>\nStyle/medium: vector logo mark; flat colors; minimal\nComposition/framing: centered mark; clear silhouette; generous margin\nColor palette: <1-2 colors; high contrast>\nText (verbatim): \"<exact name>\" (only if needed)\nConstraints: no gradients; no mockups; no 3D; no watermark\n```\n\n### Logo example: abstract symbol mark\n```\nUse case: logo-brand\nAsset type: logo concept\nPrimary request: geometric leaf symbol suggesting sustainability and growth\nStyle/medium: vector logo mark; flat colors; minimal\nComposition/framing: centered mark; clear silhouette\nColor palette: deep green and off-white\nConstraints: no text unless requested; no gradients; no mockups; no 3D; no watermark\n```\n\n### Logo example: monogram mark\n```\nUse case: logo-brand\nAsset type: logo concept\nPrimary request: interlocking monogram of the letters \"AV\"\nStyle/medium: vector logo mark; flat colors; minimal\nComposition/framing: centered mark; balanced spacing\nColor palette: black on white\nConstraints: no gradients; no mockups; no 3D; no watermark\n```\n\n### Logo example: wordmark\n```\nUse case: logo-brand\nAsset type: logo concept\nPrimary request: clean wordmark for a modern studio\nStyle/medium: vector wordmark; flat colors; minimal\nText (verbatim): \"Studio North\"\nComposition/framing: centered text; even letter spacing\nConstraints: no gradients; no mockups; no 3D; no watermark\n```\n\n## Edit\n\n### text-localization\n```\nUse case: text-localization\nInput images: Image 1: original infographic\nPrimary request: replace \"Bean Hopper\", \"Grinder\", \"Brew Group\", \"Boiler\", \"Water Tank\", and \"Drip Tray\" with \"Tolva\", \"Molino\", \"Grupo de infusión\", \"Caldera\", \"Depósito de agua\", and \"Bandeja de goteo\"\nConstraints: change only the text; preserve layout, typography, spacing, and hierarchy; no extra words; do not alter logos or imagery\n```\n\n### identity-preserve\n```\nUse case: identity-preserve\nInput images: Image 1: person photo; Image 2..N: clothing references\nPrimary request: replace only the clothing with the provided garments\nConstraints: preserve face, body shape, pose, hair, expression, and identity; match lighting and shadows; keep the background unchanged; no accessories or text\n```\n\n### precise-object-edit\n```\nUse case: precise-object-edit\nInput images: Image 1: room photo\nPrimary request: replace only the white chairs with wooden chairs\nConstraints: preserve camera angle, room lighting, floor shadows, and surrounding objects; keep all other aspects unchanged\n```\n\n### lighting-weather\n```\nUse case: lighting-weather\nInput images: Image 1: original photo\nPrimary request: make it look like a winter evening with gentle snowfall\nConstraints: preserve subject identity, geometry, camera angle, and composition; change only lighting, atmosphere, and weather\n```\n\n### background-extraction\n```\nUse case: background-extraction\nInput images: Image 1: product photo\nPrimary request: isolate the product on a clean transparent background\nScene/backdrop: perfectly flat solid #00ff00 chroma-key background for local background removal\nConstraints: background must be one uniform color with no shadows, gradients, texture, reflections, floor plane, or lighting variation; crisp silhouette; generous padding; no halos or fringing; preserve label text exactly; no restyling; do not use #00ff00 anywhere in the subject\n```\n\nPost-process note: after built-in generation, run `python \"${CODEX_HOME:-$HOME/.codex}/skills/.system/imagegen/scripts/remove_chroma_key.py\" --input <source> --out <final.png> --auto-key border --soft-matte --transparent-threshold 12 --opaque-threshold 220 --despill`. Ask before using CLI `gpt-image-1.5 --background transparent --output-format png` for true/native transparency, failed chroma-key validation, or complex subjects such as hair, fur, glass, smoke, liquids, translucent materials, reflections, or soft shadows, unless the user already explicitly requested `gpt-image-1.5`, `scripts/image_gen.py`, or CLI fallback.\n\n### style-transfer\n```\nUse case: style-transfer\nInput images: Image 1: style reference\nPrimary request: apply Image 1's visual style to a man riding a motorcycle on a plain white backdrop\nConstraints: preserve palette, texture, and brushwork; no extra elements\n```\n\n### compositing\n```\nUse case: compositing\nInput images: Image 1: base scene; Image 2: subject to insert\nPrimary request: place the subject from Image 2 next to the person in Image 1\nConstraints: match lighting, perspective, and scale; keep the base framing unchanged; no extra elements\n```\n\n### character consistency workflow\n```\nUse case: identity-preserve\nInput images: Image 1: previous character anchor illustration\nPrimary request: continue the story with the same character in a new scene and action\nScene/backdrop: snowy forest after a winter storm\nSubject: same young forest hero gently helping a frightened squirrel out of a fallen tree\nStyle/medium: same children's book watercolor illustration style as Image 1\nConstraints: do not redesign the character; preserve facial features, proportions, outfit, color palette, and personality; no text; no watermark\n```\n\n### sketch-to-render\n```\nUse case: sketch-to-render\nInput images: Image 1: drawing\nPrimary request: turn the drawing into a photorealistic image\nConstraints: preserve layout, proportions, and perspective; choose realistic materials and lighting; do not add new elements or text\n```\n\nFile v1.0.0:skill-card.md\n\n## Description: <br>\nGenerate or edit raster images such as photos, illustrations, textures, sprites, mockups, and transparent-background cutouts when a bitmap asset is appropriate. <br>\n\nThis skill is ready for commercial/non-commercial use. <br>\n\n## Publisher: <br>\n[zack-dev-cm](https://clawhub.ai/user/zack-dev-cm) <br>\n\n### License/Terms of Use: <br>\nApache 2.0 <br>\n\n\n## Use Case: <br>\nDevelopers and agents use this skill to create or edit bitmap assets for projects, including website images, game assets, UI or product mockups, and transparent cutouts. <br>\n\n### Deployment Geography for Use: <br>\nGlobal <br>\n\n## Known Risks and Mitigations: <br>\nRisk: The optional CLI fallback can send prompts or input images through the OpenAI API. <br>\nMitigation: Use the built-in image tool for normal tasks; enable CLI fallback only intentionally, with OPENAI_API_KEY set locally, and avoid sensitive prompts or images unless API use is acceptable for the project. <br>\nRisk: Generated or edited images may be unsuitable for the intended project without review. <br>\nMitigation: Inspect outputs for subject accuracy, text accuracy, prompt adherence, and required transparency before using them in a project. <br>\n\n\n## Reference(s): <br>\n- [Image Gen on ClawHub](https://clawhub.ai/zack-dev-cm/imagegen) <br>\n- [CLI reference](references/cli.md) <br>\n- [Codex network approvals and sandbox notes](references/codex-network.md) <br>\n- [Image API reference](references/image-api.md) <br>\n- [Prompting guidance](references/prompting.md) <br>\n- [Sample prompts](references/sample-prompts.md) <br>\n- [OpenAI API keys](https://platform.openai.com/api-keys) <br>\n\n\n## Skill Output: <br>\n**Output Type(s):** [Guidance, Shell commands, Configuration, Files] <br>\n**Output Format:** [Markdown guidance with optional shell commands and generated raster image files] <br>\n**Output Parameters:** [1D] <br>\n**Other Properties Related to Output:** [May use local post-processing for transparent PNG or WebP outputs; CLI fallback requires OPENAI_API_KEY and network access.] <br>\n\n## Skill Version(s): <br>\n1.0.0 (source: server release metadata) <br>\n\n## Ethical Considerations: <br>\nUsers 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. <br>\n\nFile v1.0.0:agents/openai.yaml\n\ninterface:\n  display_name: \"Image Gen\"\n  short_description: \"Generate or edit images for websites, games, and more\"\n  icon_small: \"./assets/imagegen-small.svg\"\n  icon_large: \"./assets/imagegen.png\"\n  default_prompt: \"Use $imagegen to make or edit an image for this project.\"\n\nFile v1.0.0:LICENSE.txt\n\nApache License\nVersion 2.0, January 2004\nhttp://www.apache.org/licenses/\n\nTERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\n\n1. 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We also recommend that a\n   file or class name and description of purpose be included on the\n   same \"printed page\" as the copyright notice for easier\n   identification within third-party archives.\n\nCopyright [yyyy] [name of copyright owner]\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n    http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.","readmeExcerpt":"Skill: Image Gen Owner: zack-dev-cm Summary: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparen... Tags: codex:1.0.1, image-generation:1.0.1, latest:1.0.1 Version history: v1.0.1 | 2026-06-10T13:13:42.579Z | user Publish instruction-only ClawHub bundle for cleaner security checks. v1.0.0 | 2026-06-10T12:58:04.14","codeSnippets":[],"executableExamples":[{"language":"text","snippet":"Create the requested subject on a perfectly flat solid #00ff00 chroma-key background for background removal.\nThe background must be one uniform color with no shadows, gradients, texture, reflections, floor plane, or lighting variation.\nKeep the subject fully separated from the background with crisp edges and generous padding.\nDo not use #00ff00 anywhere in the subject.\nNo cast shadow, no contact shadow, no reflection, no watermark, and no text unless explicitly requested."},{"language":"text","snippet":"Use case: <taxonomy slug>\nAsset type: <where the image will be used>\nPrimary request: <user's main prompt>\nInput images: <Image 1: role; Image 2: role>\nScene/backdrop: <environment>\nSubject: <main subject>\nStyle/medium: <photo, illustration, 3D render, diagram, etc.>\nComposition/framing: <wide, close, top-down, centered, negative space>\nLighting/mood: <lighting and mood>\nColor palette: <palette notes>\nMaterials/textures: <surface details>\nText (verbatim): \"<exact text>\"\nConstraints: <must keep or must avoid>\nAvoid: <negative constraints>"},{"language":"text","snippet":"Use case: photorealistic-natural\nPrimary request: candid photo of an elderly sailor on a small fishing boat adjusting a net\nScene/backdrop: coastal water with soft haze\nSubject: weathered skin with wrinkles and sun texture\nStyle/medium: photorealistic candid photo\nComposition/framing: medium close-up, eye-level\nLighting/mood: soft coastal daylight, shallow depth of field, subtle film grain\nMaterials/textures: real skin texture, worn fabric, salt-worn wood\nConstraints: natural color balance; no heavy retouching; no glamorization; no watermark\nAvoid: studio polish; staged look"},{"language":"text","snippet":"Use case: product-mockup\nPrimary request: premium product photo of a matte black shampoo bottle with a minimal label\nScene/backdrop: clean studio gradient from light gray to white\nSubject: single bottle centered with subtle reflection\nStyle/medium: premium product photography\nComposition/framing: centered, slight three-quarter angle, generous padding\nLighting/mood: softbox lighting, clean highlights, controlled shadows\nMaterials/textures: matte plastic, crisp label printing\nConstraints: no logos or trademarks; no watermark"},{"language":"text","snippet":"Use case: ui-mockup\nPrimary request: mobile app home screen for a local farmers market with vendors and daily specials\nAsset type: mobile app screen\nStyle/medium: realistic product UI, not concept art\nComposition/framing: clean vertical mobile layout with clear hierarchy\nConstraints: practical layout, clear typography, no logos or trademarks, no watermark"},{"language":"text","snippet":"Use case: infographic-diagram\nPrimary request: detailed infographic of an automatic coffee machine flow\nScene/backdrop: clean, light neutral background\nSubject: bean hopper -> grinder -> brew group -> boiler -> water tank -> drip tray\nStyle/medium: clean vector-like infographic with clear callouts and arrows\nComposition/framing: vertical poster layout, top-to-bottom flow\nText (verbatim): \"Bean Hopper\", \"Grinder\", \"Brew Group\", \"Boiler\", \"Water Tank\", \"Drip Tray\"\nConstraints: clear labels, strong contrast, no logos or trademarks, no watermark"}],"parameters":null,"dependencies":[],"permissions":[],"extractedFiles":[{"path":"SKILL.md","content":"---\nname: \"imagegen\"\ndescription: \"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.\"\n---\n\n# Image Generation Skill\n\nUse this skill when the user needs a bitmap image rather than repo-native code,\nSVG, HTML, CSS, canvas, or an existing vector/icon system.\n\nThis public ClawHub release is instruction-only. It does not bundle executable\nAPI helpers, dependencies, or generated assets. In Codex sessions, use the\navailable built-in image generation or image editing tool. In other agent\nhosts, use the host's approved image-generation capability and keep the same\nprompting and verification standards.\n\n## When To Use\n\n- Generate a new raster image: product shot, hero image, concept art, cover,\n  sprite, texture, UI mockup, infographic, or educational visual.\n- Edit an existing image while preserving important invariants such as identity,\n  product shape, text, lighting direction, or composition.\n- Derive visual variants from supplied reference images.\n- Produce multiple related bitmap assets when each output has a distinct prompt\n  or role.\n\n## When Not To Use\n\n- The requested asset should be an SVG, icon font, HTML/CSS composition, canvas\n  graphic, or repo-native component.\n- The repo already has an editable vector/logo/icon system that should be\n  extended directly.\n- The user asks for deterministic code-native output rather than generated\n  imagery.\n\n## Workflow\n\n1. Decide intent: `generate` for a new image, `edit` for changing an existing\n   image while preserving parts of it.\n2. Decide whether the image is preview-only or project-bound.\n3. Label every input image by role: edit target, reference image, style source,\n   insert, or supporting context.\n4. Normalize the prompt into a compact production spec. Preserve user\n   constraints and avoid adding unrelated characters, brands, slogans, or story\n   elements.\n5. Use the host-provided image generation/editing tool. For distinct assets,\n   make separate tool calls or jobs rather than relying on variants of one\n   prompt.\n6. Inspect the output for subject accuracy, composition, text rendering,\n   style fit, prohibited content, and requested invariants.\n7. Iterate with one targeted change when needed.\n8. For project-bound assets, place the selected final artifact in the workspace\n   and update consuming references. Never leave a project-referenced final image\n   only in a host default output directory.\n9. Report final saved path(s), whether the output is preview-only or\n   project-bound, and t"},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn7dhjt1k1f111whp13fmrqwnh81tn1v\",\n  \"slug\": \"imagegen\",\n  \"version\": \"1.0.1\",\n  \"publishedAt\": 1781097222579\n}"},{"path":"references/prompting.md","content":"# Prompting best practices\n\nThese prompting principles are shared by both top-level modes of the skill:\n- built-in `image_gen` tool (default)\n- explicit `scripts/image_gen.py` CLI fallback\n\nThis 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.\n\n## Contents\n- [Structure](#structure)\n- [Specificity policy](#specificity-policy)\n- [Allowed and disallowed augmentation](#allowed-and-disallowed-augmentation)\n- [Composition and layout](#composition-and-layout)\n- [Constraints and invariants](#constraints-and-invariants)\n- [Text in images](#text-in-images)\n- [Input images and references](#input-images-and-references)\n- [Iterate deliberately](#iterate-deliberately)\n- [Transparent images](#transparent-images)\n- [Fallback-only execution controls](#fallback-only-execution-controls)\n- [Use-case tips](#use-case-tips)\n- [Where to find copy/paste recipes](#where-to-find-copypaste-recipes)\n\n## Structure\n- Use a consistent order: scene/backdrop -> subject -> key details -> constraints -> output intent.\n- Include intended use (ad, UI mock, infographic) to set the level of polish.\n- For complex requests, use short labeled lines instead of one long paragraph.\n\n## Specificity policy\n- If the user prompt is already specific and detailed, normalize it into a clean spec without adding creative requirements.\n- If the prompt is generic, you may add tasteful detail when it materially improves the output.\n- Treat examples in `sample-prompts.md` as fully-authored recipes, not as the default amount of augmentation to add to every request.\n- 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.\n\n## Allowed and disallowed augmentation\n\nAllowed augmentation for generic prompts:\n- composition and framing cues\n- intended-use or polish-level hints\n- practical layout guidance\n- reasonable scene concreteness that supports the request\n\nDo not add:\n- extra characters, props, or objects that are not implied\n- brand palettes, slogans, or story beats that are not implied\n- arbitrary side-specific placement unless the surrounding layout supports it\n\n## Composition and layout\n- Specify framing and viewpoint (close-up, wide, top-down) and placement only when it materially helps.\n- Call out negative space if the asset clearly needs room for UI or copy.\n- Avoid making left/right layout decisions unless the user or surrounding layout supports them.\n- 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`).\n\n## Constraints and invariants\n- State what must not change (`keep background unchanged`).\n- For edits, say `change only X; keep Y unchanged` and repeat invariants on every iteration to reduc"},{"path":"references/sample-prompts.md","content":"# Sample prompts (copy/paste)\n\nThese prompt recipes are shared across both top-level modes of the skill:\n- built-in `image_gen` tool (default)\n- `scripts/image_gen.py` CLI fallback for explicit CLI/API/model requests or user-confirmed true-transparent-output fallback requests\n\nUse these as starting points. They are intentionally complete prompt recipes, not the default amount of augmentation to add to every user request.\n\nWhen adapting a user's prompt:\n- keep user-provided requirements\n- only add detail according to the specificity policy in `SKILL.md`\n- do not treat every example below as permission to invent extra story elements\n\nThe 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.\n\nExecution 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.\n\nCLI model notes:\n- `gpt-image-2` is the fallback CLI default for new workflows.\n- `gpt-image-2` supports `quality` values `low`, `medium`, `high`, and `auto`.\n- For 4K-style `gpt-image-2` output, use `3840x2160` or `2160x3840`.\n- 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`.\n- Do not set `input_fidelity` with `gpt-image-2`; image inputs already use high fidelity.\n\nFor prompting principles (structure, specificity, invariants, iteration), see `references/prompting.md`.\n\n## Generate\n\n### photorealistic-natural\n```\nUse case: photorealistic-natural\nPrimary request: candid photo of an elderly sailor on a small fishing boat adjusting a net\nScene/backdrop: coastal water with soft haze\nSubject: weathered skin with wrinkles and sun texture\nStyle/medium: photorealistic candid photo\nComposition/framing: medium close-up, eye-level\nLighting/mood: soft coastal daylight, shallow depth of field, subtle film grain\nMaterials/textures: real skin texture, worn fabric, salt-worn wood\nConstraints: natural color balance; no heavy retouching; no glamorization; no watermark\nAvoid: studio polish; staged look\n```\n\n### product-mockup\n```\nUse case: product-mockup\nPrimary request: premium product photo of a matte black shampoo bottle with a minimal label\nScene/backdrop: clean studio gradient from light gray to white\nSubject: single bottle centered with subtle reflec"},{"path":"skill-card.md","content":"## Description:\n\nGenerate or edit raster images for websites, games, UI mockups, product shots, textures, sprites, and other bitmap assets using a host-approved image generation capability.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[zack-dev-cm](https://clawhub.ai/user/zack-dev-cm)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers 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.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: Generated images can contain inaccurate details, poor text rendering, or rights-sensitive content such as logos, endorsements, or identity-preserving edits.\n\nMitigation: Review generated images for accuracy, rights-sensitive content, requested invariants, and in-image text before using or publishing them.\n\nRisk: Project-bound image assets can be left outside the workspace or referenced before the selected final output is saved.\n\nMitigation: Save selected final assets into the workspace, update consuming references, and report the saved path and final prompt.\n\n## Reference(s):\n\n- [Prompting best practices](references/prompting.md)\n- [Sample prompts](references/sample-prompts.md)\n\n## Skill Output:\n\n**Output Type(s):** [text, markdown, shell commands, guidance, files]\n\n**Output Format:** [Markdown guidance with prompt specifications, verification notes, saved-path reporting, and optional shell commands]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [May result in project-bound bitmap image files when the host-approved image generation or editing capability is used.]\n\n## Skill Version(s):\n\n1.0.1 (source: server release metadata)\n\n## Ethical Considerations:\n\nUsers 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."}],"languages":[],"docsSourceLabel":"CLAWHUB","editorialOverview":null,"editorialQuality":{"score":100,"threshold":65,"status":"thin","wordCount":1841,"uniquenessScore":44,"reasons":["uniqueness-below-45"]}},"media":{"evidence":{"source":"no-media","verified":false,"confidence":"low","updatedAt":"2026-10-10T15:42:31.743Z","emptyReason":"No screenshots, media assets, or demo links are 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