executing-plans
executing-plans
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available
toxigen_alice_decodingApply ToxiGen ALICE classifier-in-the-loop weighted beam scoring for adversarial or detoxifying generation experiments.
Independent GitHub snapshot · Automated classification · Original documentation governs
Use this skill when a task needs ALICE-style candidate ranking or a small classifier-in-the-loop decoding trace. It is suitable for reduced recovery with deterministic fake language models/classifiers and for auditing whether a full generation harness uses the paper's scoring rule.
Do not use this skill to construct demonstration prompts, compute human annotation classes, or train classifiers. It expects candidate continuations and classifier scores to be supplied by the caller.
toxic and benign classes for each candidate.prompt_label, attack mode, lambda_l, lambda_c, and beam size.lambda_l * lm_logprob + lambda_c * classifier_logprob[target_class].Run:
python scripts/alice_decoding.py --self-test
python tests/test_alice_decoding.py
The tests check target-class mapping, classifier-dominated selection, and prompt-copy exclusion.
This skill does not call GPT-3, HateBERT, or external APIs. In full recovery, callers should pass real model scores. In soft reduced recovery, deterministic scores are acceptable only when the result is explicitly marked as a proxy.