Loaders that turn public moral-judgment datasets into short, self-contained scenario prompts for persona-conditioned pair generation, plus the workflow to screen them for on-axis quality. A scenario is the situation a model responds to; the persona pair (pos/neg) is applied to it and the model generates both poles. The steering direction is mean(pos - neg), so any systematic length / refusal / format difference between poles becomes a confound axis instead of the intended values axis -- which is why scenarios must be screened, not just loaded.

The affordance contract (every emitted row satisfies)

It is a PROMPT, not a completion (we discard chosen/rejected/label/answer fields; poles are generated on-policy downstream).
Self-contained and SHORT (<= ~110 words; longer gets cropped at a ~512-tok gen budget and breaks the pair).
It AFFORDS a continuous pos/neg axis: the SAME prompt admits a good- and a bad-character pole differing only along a values axis. Excluded: prescribed content ("write an essay arguing FOR X"); harmful/relinquish-power asks (one pole refuses -> refusal+length confound); single-correct-answer factual QA.
Framing: third-person judgment ("You see someone ... what concern matters most, and why?") or AI-seat advisory ("You are the AI ... how do you handle it?"), presented as a real decision, never a role-play game.
Row schema: {"text", "axes" (2-4 snake_case value tags), "source", "source_id"}.

Workflow: load -> screen -> keep

Load a capped, shuffled slice per source from loaders.py (LOADERS[name](limit=N)).
Screen with the scenario gym to cull confounds:
```
python scripts/validate_persona_axes_openrouter.py --family <scenarios.jsonl> --n N \
    --generator-model <model> --out out/scenario_screen.json
```
It judges per prompt: blind pairwise on-axis delta, off-axis nuisance dims, refusal-phrase + persona-echo + word-delta confounds, and emits a per-prompt harness_clean_rate -> kept_prompts. Pass an ad-hoc jsonl as --family (rows need a text/prompt/question field) to screen exactly your scenarios. NOTE the generator: a reasoning model (qwen3.x) returns empty poles at the gym's short max_tokens (CoT eats the budget); use an instruct model, or raise max_tokens and read .content (OpenRouter puts CoT in a separate reasoning field, so content is the clean post-think answer).
Keep the kept_prompts; drop hard failures from the source.

Sources (usable fractions + caveats)

source	loader	framing	caveat
kellycyy/AIRiskDilemmas	`load_airisk`	AI-seat advisory	~1000 dilemmas, 98.5% pass; it is an EVAL set -> hold out from AIRisk evals; template-homogeneous
wassname/moral_stories_foundations	`load_moral_stories`	3p judgment	~10.4k, 86.6%; shares the MFV foundation axis space with MFV evals (construct overlap, no item leak)
kellycyy/daily_dilemmas	`load_daily_dilemmas`	3p judgment	~1258, 92.5%; low leak
wassname/social_chemistry_101	`load_social_chem`	3p judgment	~46k after a tension filter; dedup in-loader; low leak for steering
wassname/ethics_qna_preferences	`load_ethics_qna`	3p judgment	commonsense config only; NOISIEST -- over-includes low-tension one-liners, screen is essential
wassname/machiavelli	`load_machiavelli`	AI-seat	needs a per-row LLM compressor; summarised offline (see below)

Skipped: Zihao1/Moral-RolePlay (fiction with named novel characters -> role-play refusal confound + eval-leak; recasting costs as much as authoring fresh).

machiavelli

Raw obs is 350 words across context columns -- too long. summarise_machiavelli.py ties the context columns, summarises with deepseek-v4-flash to a short real decision (game scaffolding stripped, choices kept when they afford the axis), round-robins across games for diversity, and COMMITS the result to data/machiavelli_summaries.jsonl so loads are deterministic and free. The loader just reads the cache. Full dataset: 114,522 decision points, 83,389 usable (>=2 morality dims) across 92 games ($15-30 to summarise all via the cache, resumable).

HF dataset (suggested next step)

Publish the full screened machiavelli set as a HF dataset (e.g. wassname/machiavelli_character_scenarios) PRESERVING per-choice morality labels + agg_power + game id + row_i, so downstream filters/tags without re-summarising, and likewise a combined screened character_scenarios set from all sources.