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Required before calling image_generate. Create and edit images from a prompt — generate an image, make a picture / logo / illustration / icon / banner / poster / thumbnail / hero image / mockup / prod
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add aden-hive/hive --skill image-generation -a cursor -y
Required before calling image_generate. Create and edit images from a prompt — generate an image, make a picture / logo / illustration / icon / banner / poster / thumbnail / hero image / mockup / product shot / social graphic, or edit / restyle / combine existing images from reference images. Uses OpenAI gpt-image-2 through the Hive image service, billed to the user's Hive credits like an LLM call (no API key needed). Teaches the exact call shape, the quality/cost tradeoff (quality="low" is the default and cheapest), reference-image editing, how to show the result to the user with attach_file, and the failure modes (out of credits, model unavailable, moderation).
Required before calling image_generate. Create and edit images from a prompt — generate an image, make a picture / logo / illustration / icon / banner / poster / thumbnail / hero image / mockup / product shot / social graphic, or edit / restyle / combine existing images from reference images. Uses OpenAI gpt-image-2 through the Hive image service, billed to the user's Hive credits like an LLM call (no API key needed). Teaches the exact call shape, the quality/cost tradeoff (quality="low" is the default and cheapest), reference-image editing, how to show the result to the user with attach_file, and the failure modes (out of credits, model unavailable, moderation).
image_generate turns a text prompt into an image (and can edit existing
images). It routes through the Hive image service to OpenAI's gpt-image-2;
the cost is billed to the user's Hive credits exactly like an LLM call, so there
is no API key to configure. Each generated image is also saved to disk.
image_generate(
prompt: str, # required — what to draw
reference_images: list[str] = None,# local paths or http(s) URLs to edit/condition on
size: str = "1024x1024", # 1024x1024 | 1536x1024 (landscape) | 1024x1536 (portrait) | auto
quality: str = "low", # low only (medium & high disabled)
n: int = 1, # 1–4; each image is billed separately
output_format: str = "png", # png | jpeg | webp
model: str = "gpt-image-2",
)
Defaults are deliberately cheap and fast. quality is locked to low —
medium and high are disabled for cost control, and any request for a higher
tier is automatically forced to low. Only raise n when the user explicitly
wants variations.
Be concrete: name the subject, style (photo, flat vector, 3D, watercolor…),
composition/framing, color palette, mood, and any text to render (gpt-image-2
renders text well — quote it exactly, e.g. the words "Launch Day" in bold).
Pass reference_images to edit, restyle, or compose from existing images —
restyle a product photo, place a logo on a mockup, keep a character's identity
across images, or merge elements. Provide up to 10 local file paths or http(s)
URLs; the model conditions on them at high fidelity. Example:
image_generate(prompt="Put this product on a marble kitchen counter, soft morning light",
reference_images=["data/uploads/bottle.png"])
A good source of reference images is something the user attached (read it from
the path in their message) or an image you generated earlier (use its saved
path).
Image generation can take a couple of minutes, so image_generate runs in the
background: it returns immediately with {"status":"started","handle":"bg_…"}.
You then poll the generic collect_result tool with that handle until the
image is ready:
start = image_generate(prompt="A minimalist bee logo, flat vector, amber on white")
# start.handle == "bg_1"
res = collect_result(handle="bg_1", wait_seconds=30)
# → {"status":"pending", ...} ← not done yet; call collect_result again
# → eventually the real result: {"images":[{"path": …}], "usage": …, …}
collect_result waits up to wait_seconds (≤45) per call and returns
{"status":"pending"} until generation finishes — just call it again with the
same handle until you get the real result. It's fine to do other small things
between polls. Don't start a second image while one is pending unless the user
asked for several.
The finished result's JSON has images (each with a path) plus model, n,
and usage; one image is previewed inline. Call attach_file(path) on the
image path to surface a downloadable chip in chat. Do not paste base64 or
write  markdown.
Errors surface in the collect_result result as {"error": ...} (the tool
never raises). Handle these:
status: 402) — tell the user
they're out of Hive credits; do not retry.status: 403) — report that image
generation is currently unavailable; do not loop.status: 400) — the prompt was likely
refused; rephrase it (less explicit, no real-person likeness) and try once.status: 429) — wait a moment and retry once.pending after several minutes — collect_result keeps returning
pending well past ~4 min: the job likely failed. Tell the user and start once
more. ({"error":"Unknown … handle"} means it was already collected or never
started — just start a fresh image_generate.)User: "make us a logo — a friendly robot, simple and modern."
image_generate(prompt="A friendly modern robot mascot logo, simple flat vector, rounded shapes, teal and white, centered, plain background", quality="low") → {"status":"started","handle":"bg_1"}collect_result(handle="bg_1", wait_seconds=30) — repeat until it returns the real result (not {"status":"pending"}).result.images[0].path, call attach_file(that_path).