imagegen
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.
git clone --depth 1 https://github.com/deonmenezes/mantishack /tmp/imagegen && cp -r /tmp/imagegen/codex-rs/skills/src/assets/samples/imagegen ~/.claude/skills/imagegenSKILL.md
# Image Generation Skill Generates or edits images for the current project (for example website assets, game assets, UI mockups, product mockups, wireframes, logo design, photorealistic images, or infographics). ## Top-level modes and rules This skill has exactly two top-level modes: - **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`. - **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`. Within CLI fallback, the CLI exposes three subcommands: - `generate` - `edit` - `generate-batch` Rules: - Use the built-in `image_gen` tool by default for normal image generation and editing requests. - Do not switch to CLI fallback for ordinary quality, size, or file-path control. - 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 `$CODEX_HOME/skills/.system/imagegen/scripts/remove_chroma_key.py`. - Never silently switch from built-in `image_gen` or CLI `gpt-image-2` to CLI `gpt-image-1.5`. Treat this as a model/path downgrade and ask the user before doing it, unless the user has already explicitly requested `gpt-image-1.5`, `scripts/image_gen.py`, or CLI fallback. - If a transparent request appears too complex for clean chroma-key removal, asks for true/native transparency, or local removal fails validation, explain that true transparency requires CLI `gpt-image-1.5 --background transparent --output-format png` because `gpt-image-2` does not support `background=transparent`, then ask whether to proceed. Run the CLI fallback only after the user confirms. - The word `batch` by itself does not mean CLI fallback. If the user asks for many assets or says to batch-generate assets without explicitly asking for CLI/API/model controls, stay on the built-in path and issue one built-in call per requested asset or variant. - If the built-in tool fails or is unavailable, tell the user the CLI fallback exists and that it requires `OPENAI_API_KEY`. Proceed only if the user explicitly asks for that fallback. - If the user explicitly asks for CLI mode, use the bundled `scripts/image_gen.py` workflow. Do not create one-off SDK runners. - Never modify `scripts/image_gen.py`. If something is missing, ask the user before doing anything else. Built-in save-path policy: - In built-in tool mode, Codex saves generated images under `$CODEX_HOME/*` by default. - Do not describe or rely on OS temp as the default built-in destination. - 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 `$CODEX_HOME/generated_images/...`. - Save-path precedence in built-in mode: 1. If the user names a destination, move or copy the selected output there. 2. If the image is meant for the current project, move or copy the final selected image into the workspace before finishing. 3. If the image is only for preview or brainstorming, render it inline; the underlying file can remain at the default `$CODEX_HOME/*` path. - Never leave a project-referenced asset only at the default `$CODEX_HOME/*` path. - Do not overwrite an existing asset unless the user explicitly asked for replacement; otherwise create a sibling versioned filename such as `hero-v2.png` or `item-icon-edited.png`. Shared prompt guidance for both modes lives in `references/prompting.md` and `references/sample-prompts.md`. Fallback-only docs/resources for CLI mode: - `references/cli.md` - `references/image-api.md` - `references/codex-network.md` - `scripts/image_gen.py` Local post-processing helper: - `$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. ## When to use - Generate a new image (concept art, product shot, cover, website hero) - Generate a new image using one or more reference images for style, composition, or mood - Edit an existing image (inpainting, lighting or weather transformations, background replacement, object removal, compositing, transparent background) - Produce many assets or variants for one task ## When not to use - Extending or matching an existing SVG/vector icon set, logo system, or illustration library inside the repo - Creating simple shapes, diagrams, wireframes, or icons that are better produced directly in SVG, HTML/CSS, or canvas - Making a small project-local asset edit when the source file already exists in an editable native format - Any task where the user clearly wants deterministic code-native output instead of a generated bitmap ## Decision tree Think about two separate questions: 1. **Intent:** is this a new image or an edit of an existing image? 2. **Execution strategy:** is this one asset or many assets/variants? Intent: - If the user wants to modify an existing image while preserving parts of it, treat the request as **edit**. - If the user provides images only as references for style, composition, mood, or subject guidance, treat the request as **generate**. - If the user provides no images, treat the request as **generate**. Built-in edit semantics: - Built-in edit mode is for images already visible in the conversation context, such as attached images or images generated earlier in the thread. - 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. - Do not promise arbitrary file
Use this agent when the target is a LIVE REST or GraphQL API you are authorized to test and the question is "can I tamper request bodies, headers, ids, and tokens to read or act on data that isn't mine?" — active, request-driven abuse of the API contract, not static code review. It drives REAL HTTP at the endpoints: BOLA/IDOR object-id enumeration (increment/swap/UUID-shuffle the id and diff the access decision), broken function-level authz (replay an admin verb/path with a low-priv token), mass-assignment (inject role/is_admin/is_verified/owner_id into the JSON body), excessive-data-exposure (the response over-returns fields the UI never shows), GraphQL introspection + alias/batch amplification + nested-query DoS, content-type and HTTP-verb tampering (POST→PUT/PATCH/DELETE, application/json→text/plain→x-www-form-urlencoded), JWT/session/token swap across two users, and rate-limit / idempotency-key bypass. It proves every finding with a behavioral oracle — a status/length/timing/field-set diff between the authorized baseline and the tampered request — never a guess. Prefer this agent over a code reader when you hold a base URL or a schema and want to mutate live traffic methodically.\n\n<example>\nContext: The user has a running API with numeric resource ids and two test accounts.\nuser: "Here's our staging API at https://api.staging.acme.test and tokens for user A and user B — can user A read user B's orders?"\nassistant: "That's textbook BOLA: same endpoint, swap the object id (or the bearer token) and diff the access decision. I'll use the Task tool to launch the api-abuse-fuzzer agent to enumerate /orders/{id} with A's token against B's ids and prove the cross-tenant read with a status + ownership-field oracle."\n<agent_launch>\nDelegating to api-abuse-fuzzer: a live authorized API + two tokens + object-id enumeration is its core BOLA/IDOR mission.\n</agent_launch>\n</example>\n\n<example>\nContext: The user exposes a GraphQL endpoint and isn't sure introspection or query batching is locked down.\nuser: "Our /graphql is behind auth but I want to know if a low-priv user can pull admin fields, brute force via aliases, or knock it over with a deep nested query."\nassistant: "GraphQL abuse surface: introspect the schema, alias-batch a login/lookup to bypass per-request rate limits, and send a bounded cyclic nested query as a timing oracle. I'll launch the api-abuse-fuzzer agent to tamper the operation and measure the depth/timing oracle."\n<agent_launch>\nDelegating to api-abuse-fuzzer for GraphQL introspection, alias/batch amplification, and nested-query DoS against the live endpoint.\n</agent_launch>\n</example>\n\nProactively suggest using this agent when: a live base URL + an OpenAPI/Swagger/GraphQL schema (or a captured request) is in hand and the target is authorized in-scope; endpoints take a resource identifier in the path/query/body (/users/{id}, ?account=, {"order_id": ...}) — BOLA/IDOR territory; the user holds 2+ accounts or tokens (low-priv + high-priv, tenant A + tenant B) to run an authorization differential; there are admin/privileged verbs (DELETE, PUT /admin/*, role-changing mutations) and you want to hit them as a non-admin; a write endpoint accepts a JSON object — test mass-assignment of role/is_admin/verified/balance/owner_id; a /graphql endpoint exists (introspection, alias/batch abuse, nested-query DoS, field-level authz); or the user mentions rate limiting, coupon/OTP brute force, idempotency keys, BOLA, BFLA, mass assignment, or "excessive data exposure".
Use this agent when a codebase, PR, or service needs its IMPLICIT TRUST ASSUMPTIONS enumerated and attacked — every place the code silently trusts a header, an "internal-only" route, an "already-validated-upstream" input, an ID that "belongs to the caller," or a value that "can't be null/negative." It specializes in confused-deputy (CWE-441), mass-assignment (CWE-915), input-validation gaps (CWE-20), and second-order/stored injection (CWE-89/CWE-79). This is the agent for trust-boundary audits, multi-service request paths, ORM/serializer review, and proxy/gateway/header-forwarding code.\n\n<example>\nContext: User has a multi-service backend where an edge proxy forwards auth context downstream.\nuser: "Our gateway sets X-User-Id from the JWT and the internal services read it to authorize. Can you check this?"\nassistant: "This is a classic confused-deputy / trust-boundary surface — the downstream services trust a header they can't verify. I'll use the Task tool to launch the assumption-pressure-test agent to enumerate every internal endpoint that reads X-User-Id and prove whether an attacker can reach it directly and forge it."\n<agent_launch>\nDelegating to assumption-pressure-test: the request hinges on an implicit 'this header is trusted because the gateway set it' assumption that must be attacked.\n</agent_launch>\n</example>\n\n<example>\nContext: User just merged an ORM update endpoint.\nuser: "Here's the new profile update handler: User.objects.filter(id=request.user.id).update(**request.data)"\nassistant: "That `**request.data` spread is a mass-assignment sink — it trusts that the request body only contains the fields you intended. I'll launch the assumption-pressure-test agent to map which model columns (is_admin, balance, role) become attacker-writable and confirm reachability."\n<agent_launch>\nDelegating to assumption-pressure-test for the CWE-915 mass-assignment and the implicit 'the body only has safe fields' assumption.\n</agent_launch>\n</example>\n\nProactively suggest using this agent when:\n- Code reads request headers (X-Forwarded-For, X-User-Id, X-Real-IP, X-Internal-*, Host) for trust or authorization decisions\n- A serializer/ORM uses bulk binding: `**req.body`, `Object.assign`, `ModelMapper`, `BeanUtils.copyProperties`, `update_attributes`, `params.permit!`\n- Comments or names assert trust: "internal only", "already validated", "trusted", "comes from gateway", "sanitized upstream"\n- Data is stored then later concatenated into SQL/HTML/shell (second-order injection)\n- An endpoint takes an `id`/`uuid`/`account`/`order` param that maps to a resource (IDOR / object ownership)
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