/ip-as-logo

name: ip-as-logo
description: Generate extremely simple, cute, personified square character images with rounded heavy forms, two purposeful character colors, one solid background color, and a dominant lower-corner composition. Use when creating an animal, creature, robot, ghost, plant, object, or other character image, including when the agent should infer three product-relevant directions and propose six independent candidates for approval.

IP as Logo

Create the simplest possible cute IP character: a compact, lovable symbol that remains recognizable at `32 × 32`, not a detailed character illustration.

Workflow

1. Parse the request for an explicit IP subject and available product context. Do not ask the user to choose a color mode unless they explicitly want to control it. 2. When the user has not specified an IP subject and the current workspace is a product repository, inspect relevant read-only context before asking questions. Prefer the README, product docs, package or app metadata, landing-page copy, manifests, and design tokens. Treat context as sufficient when the product purpose, primary audience, and intended personality can be inferred with reasonable confidence. 3. When product context is insufficient, ask one consolidated round of background questions covering what the product does, who it serves, and how it should feel. Do not start a second background questionnaire. Continue with the best supported interpretation after the answer. 4. Once context is sufficient, always present three concise directions before generation and explicitly propose generating six independent candidates in one batch. Do not generate until the user agrees, unless the current request already explicitly authorizes six outputs or asks the agent to proceed without another confirmation. 5. Choose the three proposed directions deliberately:

• When the user explicitly specifies an IP subject, keep that subject and propose three distinct design treatments based on silhouette treatment, secondary color region, defining feature, or personality emphasis.
• When the user does not specify an IP subject, propose three genuinely different IP subjects or metaphors. Tie each one to a different product attribute or brand promise; do not return three arbitrary animals with no rationale.

6. Interpret the user's response exactly:

• If the user accepts all three directions and the six-image proposal, generate two independent variants per direction and label them `A1`, `A2`, `B1`, `B2`, `C1`, and `C2`. Assign `A1`, `B1`, and `C1` to the lower-left and `A2`, `B2`, and `C2` to the lower-right so every direction is tested once from each side.
• If the user selects one direction but accepts six images, generate six controlled variants of that direction and label them `A1` through `A6`. Assign odd-numbered variants to the lower-left and even-numbered variants to the lower-right.
• If the user rejects the proposed quantity, directions, or distribution, follow the user's replacement instructions without arguing for the default.
• For any other even default batch size, split candidates equally between lower-left and lower-right. For an odd batch, assign the extra candidate to either side deliberately and record the imbalance. Do not use bottom-center unless the user explicitly requests it.

7. Default every candidate to exactly three semantic colors in the complete image: exactly two IP base colors plus exactly one background color. Reuse the two IP colors for facial marks rather than introducing additional semantic colors. Follow an explicit user request for another color count. Keep required product cues, identifying features, complexity limits, and any supplied palette consistent enough for useful comparison. 8. Before generation, require a top-tier image model: prefer GPT Image 2; also support Seedance 5.0 Pro, Nano Banana Pro (Gemini Image Pro), or Nano Banana 2 (Gemini Image Flash). If none is available, ask the user to enable a suitable tool or provide its API key. Never fall back to SVG generation. Use another available image model only with the user's explicit consent, and warn that its quality may not match the recommended models. Do not fabricate generated results. 9. If the runtime supports subagents, parallelize the six independent candidates up to the available concurrency. Give every subagent the same product brief, shared constraints, and one assigned direction or variant; run remaining candidates in subsequent waves when capacity is limited. If subagents are unavailable, generate the candidates through separate image-generation calls or jobs. 10. If the user supplies a background palette, reserve every supplied color for backgrounds unless they explicitly say otherwise. Choose exactly two IP base colors independently for the subject and context unless the user also assigns subject colors. Do not treat any historical or example palette as a closed list of allowed backgrounds. 11. Abstract each subject using the complexity budget below. Generate every candidate as a separate full-resolution square asset; never ask an image model to compose a contact sheet, grid, or multi-image sheet. Do not use previous candidates as image references when testing prompt-only reproducibility. 12. Treat each batch as a one-pass creative draw. Generate every requested candidate once, then preserve and deliver every returned result as-is. Do not inspect outputs to block delivery, classify them as recommended or non-recommended, retry them automatically, or repair them with post-processing. 13. Preserve and label every generated result. Report every label, IP direction and rationale, assigned corner, saved path, prompt/color mapping, and dimensions. Present all results together; generate refinements or replacements only when the user explicitly asks for another draw.

When proposing directions before generation, describe each in one compact line: `<IP subject> — <product connection> — <defining silhouett