一键导入
一键导入
| name | image-generation |
| description | Generate images and iteratively edit saved image artifacts. |
Use the generate_image tool when the user asks you to create, render, draw, design, generate, or edit an image.
If the generate_image tool is not available in the current tool list, tell the user that image generation is not enabled for this nanobot instance.
generate_image with a concrete prompt.reference_images.message tool with the artifact paths in the media parameter to deliver them to the user.Write prompts with enough detail for image models:
The tool stores generated images as persistent artifacts under nanobot's media directory and returns structured metadata:
id: generated image id, such as img_ab12cd34ef56.path: local file path for internal follow-up edits.mime: image MIME type.prompt, model, and source_images: provenance for follow-up edits.In normal user-facing replies, do not expose local filesystem paths. Keep the reply natural, for example "Done, I generated it." You may include the short image id when it helps the user refer to a specific image, but keep raw path internal unless the user explicitly asks for debug details or a local artifact reference. Never paste base64.
For follow-up edits, pass the prior artifact path to reference_images. If the user provides a new uploaded image, use that path as the reference instead.
Do not include internal replay markers such as [Message Time: ...], [image: /local/path], generate_image(...), or message(...) in user-facing replies.
Generate a new image:
generate_image(
prompt="A minimal app icon for nanobot: friendly robot head, rounded square, soft blue and white palette, clean vector style, no text",
aspect_ratio="1:1",
image_size="1K"
)
Edit the latest generated artifact:
generate_image(
prompt="Use the reference image. Keep the same robot and composition, but change the palette to warm orange and add a subtle sunrise background.",
reference_images=["/home/user/.nanobot/media/generated/2026-05-08/img_ab12cd34ef56.png"],
aspect_ratio="1:1",
image_size="1K"
)
Sustained objectives via long_task / complete_goal — idempotent goal wording, project-style modular work, early web/doc research, Runtime Context metadata.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
One-time setup wizard for the nanobot upgrade skill. Triggers: setup update, configure update, 切设置更新, 初始化更新.
Check and set the agent's own runtime state (model, iterations, context window, token usage, web config). Use when diagnosing why something doesn't work ("why can't you search the web?", "why did you stop?"), checking resource limits before complex tasks, adapting configuration for long or simple tasks, or remembering user preferences across turns. Also use when the user asks what model you are running, how many tokens you've used, or what your settings are.
Two-layer memory system with Dream-managed knowledge files.
Schedule reminders and recurring tasks.