gpt-image2-prompt-director git:20260509.b4e62eeA

Immutable. This exact content is served forever at /api/v1/blob/20ca6e26477e53bc.

---
name: gpt-image2-prompt-director
description: Use when the user wants to create, improve, evaluate, or systematize GPT image2 prompts that turn weak ideas into high-impact image generation briefs, including prompt director workflows, idea generation, visual prompt frameworks, and benchmark evaluation.
---

# GPT Image2 Prompt Director

Create high-quality GPT image2 prompts from weak ideas, or evaluate generated prompts against the archived Xiaoxiaodong expert prompt benchmark.

## Default Workflow

1. Identify the input mode:
   - **Weak idea**: one theme, title, object, phrase, image concept, or product.
   - **No idea**: user only gives identity, platform, or goal; first generate candidate ideas.
   - **Prompt repair**: user provides an existing prompt; evaluate and revise it.
   - **Evaluation**: user asks to run or inspect the benchmark.
2. Load [references/prompt-framework.md](references/prompt-framework.md) when generating or repairing prompts.
3. If the idea feels ordinary, avatar/IP-like, UI-like, or product-like, also load [references/artifact-spec-framework.md](references/artifact-spec-framework.md). First choose the artifact container before writing the prompt.
4. For avatar, profile-picture, sticker, emoji, meme, or expression-pack tasks, also load [references/avatar-expression-framework.md](references/avatar-expression-framework.md). Treat the output as a reusable identity asset or expression system, not as a generic portrait.
5. Generate one complete final prompt, plus 2-3 optional creative directions when useful.
6. Before finalizing, check the prompt has:
   - role positioning
   - anti-definition
   - input contract
   - internal planning
   - content planning
   - visual system
   - anti-failure constraints
   - output spec
   - self-check
7. If the user asks for validation, run the bundled evaluator.

## No-Idea Mode

When the user has no idea:

1. Ask for no clarification unless the goal is truly ambiguous.
2. Infer a likely publishing context from the user request.
3. Generate 10-20 candidate image plays.
4. Score each by:
   - surprise
   - GPT image2 fit
   - visual clarity
   - shareability
   - ease of execution
5. Pick the strongest direction and expand it into a complete prompt.

## Wow Mode

When the first output feels generic or the user asks for something less ordinary:

1. Stop optimizing adjectives.
2. Pick a stronger artifact container.
3. Decide whether a multi-cell grid/contact sheet would improve the result.
4. Create 3-5 high-concept collisions.
5. Prefer structured spec output for UI, social, avatar identity, poster systems, and product mockups.
6. Add a concrete Definition of Done.

For avatar and personal-brand work, prefer an `avatar identity system` over a standalone portrait unless the user explicitly asks for a single image only.

Use `n x n` grid decomposition when variety is part of the value: avatars, stickers, meme packs, character variants, icon systems, material/style tests, storyboards, or repeated information cards. Prefer `3 x 3` for avatar exploration and `4 x 4` for sticker/expression packs. Avoid grids for cinematic posters, hero key visuals, luxury product shots, or any image that needs one dominant focal point.

## Avatar / Sticker Rules

For avatar prompts:

- Do not default to a cinematic portrait, fashion editorial, or realistic headshot.
- If there is no reference image, prefer a `3 x 3 avatar exploration sheet` first.
- If the user needs one final avatar, make it a platform avatar asset: large head silhouette, simple background, one memorable symbol, one accent color, circular-crop safe, readable at `80px`.
- Keep the symbol secondary to the face. Do not cover most of the face with paper, crystals, UI, geometry, or decorative objects.
- Prefer semi-realistic / semi-graphic / illustrated / badge-like treatments over photographic realism unless the user explicitly asks for a photo.
- If a personal-brand avatar looks like a poster portrait, compare against [examples/personal-brand-avatar-repair.md](examples/personal-brand-avatar-repair.md) and repair toward avatar-asset mechanics.

For sticker and expression prompts:

- Default to a `4 x 4` expression pack.
- Use "consistent imperfection": the same character can be wrong, but must be wrong in the same way.
- Text, when present, should feel hand-drawn with the image, not typeset afterward.
- Each cell must be independently usable and emotionally clear.

## Evaluation Commands

Create a runnable eval pack:

```bash
cd gpt-image2-prompt-director
node scripts/eval_prompt_director.mjs --init-run /tmp/gpt-image2-prompt-director-eval
```

Evaluate the archived expert prompts as a sanity baseline:

```bash
cd gpt-image2-prompt-director
node scripts/eval_prompt_director.mjs --gold --report /tmp/gpt-image2-prompt-director-gold-report.md
```

Evaluate generated prompts saved as `outputs/<case-id>.md`:

```bash
cd gpt-image2-prompt-director
node scripts/eval_prompt_director.mjs \
  --outputs /tmp/gpt-image2-prompt-director-eval/outputs \
  --report /tmp/gpt-image2-prompt-director-eval/report.md \
  --fail-under 80 \
  --strict
```

Evaluate one prompt:

```bash
cd gpt-image2-prompt-director
node scripts/eval_prompt_director.mjs \
  --case-id 09 \
  --prompt-file /absolute/path/to/prompt.md
```

For scoring details, read [references/rubric.md](references/rubric.md).

## Benchmark Data

The benchmark is in [evals/benchmark-cases.json](evals/benchmark-cases.json). It contains 40 cases derived from an archived prompt corpus and regression tests. Each case includes:

- weak input
- source title
- category
- expected capabilities

Use it to test whether a generated prompt reconstructs expert-level capability from weak input. Do not treat it as a memorization target.