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name: configuration-and-prompts description: "Use this sub-skill for DiscoArt config files, prompt schema v1, schedule strings, CLIP guidance routing, cheatsheets, and config import/export helpers." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

DiscoArt Configuration and Prompts

Use this sub-skill when the user needs to inspect, validate, write, normalize, or explain DiscoArt configuration and prompt behavior without starting an artwork generation run.

Triggers

• Validate or repair a DiscoArt YAML/dict config.
• Write text_prompts using schema version "1" or legacy "text:weight" strings.
• Explain prompt schedules, prompt weights, clip_guidance, or which prompts are active at selected steps.
• Diagnose unknown argument, unsupported prompt schema/type, unsafe scheduling string, wrong 1000-step schedule length, or CLIP guidance mismatch errors.
• Use load_config, save_config, show_config, save_config_svg, export_python, or cheatsheet.
• Convert a config between YAML-like dict form and Python create(...) keyword arguments while preserving DiscoArt semantics.

Quick workflow

1. Classify the request. Stay here for config/prompt validation and conversion. Route actual diffusion/image creation to sibling artwork-generation; route python -m discoart config, python -m discoart create, service, Docker, or endpoint usage to sibling cli-and-serving.
2. Load normalized facts. Use references/configuration-and-prompts.md for helper signatures, accepted keys, default values, cut schedule groups, prompt schema, schedule grammar, and active-prompt rules.
3. Validate safely. Prefer scripts/validate_discoart_config.py --config CONFIG.yml --check-prompts --show-non-default for a no-generation check. Add --json when another tool needs structured output.
4. Explain errors with fixes. Use references/troubleshooting.md to map runtime errors to likely config, schedule, prompt schema, spellcheck, or CLIP-model causes.
5. Hand off if execution is requested. A valid config can be passed to Python create(**load_config(path)) under artwork-generation, or to CLI/service workflows under cli-and-serving; do not start generation from this sub-skill.

Owned capabilities

• Config API helpers: load_config, save_config, show_config, save_config_svg, export_python, cheatsheet.
• Default YAML facts, cut schedule groups, selected parameter docstring facts, and config normalization rules.
• Prompt schema version "1", legacy prompt strings, scalar and scheduled prompt weights, per-prompt schedules, per-prompt spellcheck, and clip_guidance subset validation.
• Safe schedule-string grammar and expansion to exactly 1000 steps for schedules used by cuts, guidance scales, secondary model use, prompt weights/schedules, and CLIP model schedules.

Boundaries and sibling routing

• Actual diffusion generation: use sibling artwork-generation for create(), model/cache/CUDA behavior, output images, DocumentArray results, go_big(), skip/stop events during a run, or W&B result logging.
• CLI and serving: use sibling cli-and-serving for python -m discoart config, python -m discoart create, python -m discoart serve, Jina Flow, service endpoints, Docker, or persistent server behavior.
• Reference-only source tooling: do not bundle or run the upstream docstring generator as a runtime helper; the distilled docstring facts in this sub-skill are sufficient.