---

name: prompt-optimization description: "Operate ProTeGi text prompt optimization and Promptist text-to-image prompt rewriting workflows for LMOps." disable-model-invocation: true metadata: disco-role: operating license: MIT

Prompt Optimization

Use this operating sub-skill when the user asks for LMOps prompt optimization, prompt rewriting, or prompt-optimization run planning.

Route by task shape

• ProTeGi text prompt optimization: use for automatic optimization of zero-shot binary classification prompts with labeled train/test data, markdown seed prompts, and an approved chat/completion provider budget.
• Promptist prompt rewriting: use for text-to-image prompt rewriting with the pretrained Promptist prompter model, but plan offline first to avoid accidental model downloads.
• Promptist RL training: use only for planning or explicitly approved heavy execution; it needs training prompt data, a supervised prompter checkpoint, Stable Diffusion/CLIP/aesthetic reward assets, Accelerate/TRL, GPUs, and credentials.
• Retrieval-based prompt or demonstration selection: route to ../example-retrieval/SKILL.md.
• Domain/instruction corpus conversion or model adaptation before prompt work: route to ../adaptation-and-training/SKILL.md.
• Broad LMOps project routing: consult ../../references/project-index.md if the user names a different LMOps project.

Required first checks

1. Classify the request as ProTeGi optimization, Promptist pretrained rewrite planning, Promptist RL training planning, or a route-out case.
2. Confirm required inputs before any expensive or external action:
   • ProTeGi: task name, task data directory, seed prompt markdown file(s), output log path, evaluator, scorer, provider credentials, overwrite consent, and budget/concurrency limits.
   • Promptist rewrite: plain text prompt(s), model id, tokenizer id, model cache or network consent, and CPU/GPU expectation for any real model load.
   • Promptist RL training: data directory, supervised prompter checkpoint, Stable Diffusion model access, TRL/PPO config, checkpoint directory, distributed launch plan, GPU availability, Hugging Face access, and W&B/logging policy.
3. Use the bundled safe planners before any source checkout run:
   • python scripts/protegi_command_builder.py --help
   • python scripts/promptist_rewrite_skeleton.py --help
4. Read the focused runtime references:
   • references/protegi-cli-and-api.md
   • references/promptist-workflows.md
   • references/troubleshooting.md

Safe operating defaults

• Do not run model downloads, provider calls, image generation, PPO training, Docker/container startup, or multi-node launches unless the user explicitly asks and provides the needed environment and credentials.
• Treat bundled scripts as planners and validators. They do not import LMOps source code, call model providers, load Promptist models, download weights, generate images, or train.
• Promptist GPU/model-download workflows and multi-node RL training are documented but not creation-time verified by this sub-skill.
• Treat ProTeGi output files as user data: the native program removes an existing output file before writing, so ask before overwriting.

Quick ProTeGi planning pattern

1. Choose one of the supported native task names: ethos, jailbreak, liar, or ar_sarcasm.
2. Stage task data in the required layout and one or more seed prompt markdown files with a # Task section and {{ text }} placeholder.
3. Build a command without executing it:

python scripts/protegi_command_builder.py \
  --task liar \
  --data-dir data/my_binary_task \
  --prompts prompts/seed.md \
  --out runs/my_binary_task.ucb.out \
  --evaluator ucb \
  --scorer 01 \
  --path-policy warn

4. Resolve any warnings, confirm credentials and overwrite behavior, then run the emitted command only in a prepared ProTeGi environment.

Quick Promptist planning pattern

1. Start with the offline skeleton and keep model loading off:

python scripts/promptist_rewrite_skeleton.py \
  --plain-text "A rabbit is wearing a space suit" \
  --model-id microsoft/Promptist \
  --tokenizer-id gpt2 \
  --show-prompts

2. If the user approves a real rewrite run, confirm PyTorch/Transformers availability and whether the Promptist model and tokenizer are already cached or may be downloaded.
3. If the user asks for RL training, move to the training checklist in references/promptist-workflows.md and do not treat it as a small smoke test.