> An installable agent skill for AI Content Drop. Machine-readable copy: > https://aicontentdrop.com/skills/image-generation/SKILL.md. > Ships with the aicontentdrop package, and is listed in > /skills/index.json.

Generating images, and not overpaying for drafts

There are 27 image models on one credit balance, and the cheapest costs a single credit while the dearest costs roughly ten times that. Since credits are flat per generation, the difference between a careless workflow and a careful one is about an order of magnitude on the same output.

Read the catalogue first


curl -s "https://aicontentdrop.com/v1/models?type=image"


{
  "type": "image",
  "count": 27,
  "models": [
    { "id": "z_image",       "name": "Z Image",       "credits": 1 },
    { "id": "flux_2",        "name": "Flux 2",        "credits": 3 },
    { "id": "seedream_4_5",  "name": "Seedream 4.5",  "credits": 4 },
    { "id": "nano_banana_2", "name": "Nano Banana 2", "credits": 6 }
  ]
}

Ids and prices change as models are added, so read them rather than hard-coding the list above. To see only what fits a budget:


curl -s "https://aicontentdrop.com/v1/models?type=image&max_credits=4"

The workflow that saves the credits

Iterate cheap, finish expensive. Composition, framing and subject are decided at 1–3 credits a shot; you only need the expensive model for the one image that ships.

  1. Draft the idea on the cheapest model that can express it. Change one thing

    2. per attempt so you learn what actually moved the result.

  2. When the composition is right, re-run *that* prompt on a higher-quality model

    3. for the final.

  3. Quote the final before running it if the user is near their balance.

An agent that starts on the best model and iterates there burns a user's month on drafts nobody keeps.

Generating


curl -sX POST https://aicontentdrop.com/v1/generate/image \
  -H "Authorization: Bearer $ACD_API_KEY" \
  -H "Content-Type: application/json" \
  -H "Idempotency-Key: hero-still-01" \
  -d '{"prompt":"a matte-black espresso machine on a concrete counter, morning window light, shallow depth of field","model":"seedream_4_5"}'

Rehearse first if you are unsure of the shape. Sandbox runs full validation, needs no API key, and charges nothing:


curl -sX POST https://aicontentdrop.com/v1/generate/image \
  -H "Content-Type: application/json" \
  -H "X-Sandbox: true" \
  -d '{"prompt":"…","model":"z_image"}'

A 202 means the request is well formed. A 400 names the problem before any credits are involved.

Writing an image prompt that lands

Say subject, setting, light, lens, treatment — in that order, in plain description. Concrete nouns beat adjective stacks: "a chipped enamel mug on a windowsill, overcast light, 50mm, shallow focus" gives a model far more to work with than "beautiful stunning photorealistic mug, 8k, masterpiece".

Two habits that matter more than any keyword:

If a prompt comes back safety_rejected, the content gate refused it and nothing was charged. Rewrite the prompt; do not retry it unchanged.

Handing an image to a video model

The most valuable thing about generating a still first is that it becomes a *controllable* first frame. Image-to-video preserves the identity in the picture; text-to-video cannot, no matter how detailed the prompt. So when a user wants a specific product, character or composition to move, generate the still first, approve it cheaply, then animate the approved frame.

See the ai-video-model-picker skill for choosing the video model and quoting that second step, and video-prompt-writer for describing the motion.

Reporting back

Give the user the model, the credit cost, and the image. If you iterated, say how many attempts and what the run cost in total — successes only, because failures were free.

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Install the plugin: https://aicontentdrop.com/plugin · All skills: https://aicontentdrop.com/skills