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name: toxigen_prompt_protocol description: Build and validate ToxiGen-style balanced identity-label demonstration prompts for implicit toxicity generation and recovery experiments.

ToxiGen Prompt Protocol

Use this skill when a task needs to construct or audit ToxiGen-style prompts from labeled demonstrations. It is appropriate for paper recovery, dataset construction checks, and controlled prompt fixtures where identity group and toxic/benign label balance matter.

Do not use this skill to generate model continuations, run ALICE decoding, score classifier outputs, or train toxicity classifiers. Those are downstream modules.

Inputs

• Demonstration records with text, group, label, and optional source.
• Prompt parameters: demonstrations per prompt, random seed, and optional forbidden explicit terms.
• Optional group alias map when identity mentions use variants.

Outputs

• Prompt records with stable prompt_id, group, label, sampled demonstrations, prompt text, and source provenance.
• Validation summary with counts by group/label, balance status, identity coverage, short buckets, and explicit-term violations.

Workflow

1. Normalize labels to toxic and benign.
2. Group demonstrations by (group, label).
3. Sample a fixed number of demonstrations per bucket with a deterministic seed.
4. Format prompts as one demonstration per bullet line followed by a final bullet marker for continuation.
5. Validate each record for group alias mention and explicit-term leakage.
6. Carry source file provenance into every prompt record so recovery can prove which resource files were used.

Validation

Run:

python scripts/prompt_protocol.py --self-test
python tests/test_prompt_protocol.py

The test fixture checks deterministic sampling, identity coverage, explicit-term detection, and balance accounting without external packages.

Limitations

This skill only constructs prompt records. It does not guarantee generated text quality, human-likeness, classifier attack success, or downstream classifier improvement.