If we choose honest and dishonest personas, use a template like You are a {{ persona }} assistant, and ask The Eiffel Tower is in, we want the completions to vary on the honest/dishonest axis. in Paris versus in Berlin shows on-axis variation. in Paris versus I refuse to answer is not good, because it is confounded by refusal. Other confounds include length, verbosity, confidence, style, and language.

So we try persona/template pairs on one model. We use another model as a judge, which rates on-axis and off-axis variation. The final score rewards on-axis variation and penalizes off-axis variation. Style movement, persona echo, and refusals are kept as audit columns.

This field is pre-scientific in a way: it is still an art. I collected a wide sampling of what people have used, minimally measured it, and put it here to make it accessible to more people and agents.

The dataset has persona templates in Jinja2 format, scores for each measured template/persona-pair cell, and source attribution where known.

Use

Start with the main split on Hugging Face. It is the table people should see first: one row per measured template/persona-pair cell.

Important columns:

template_jinja
score
positive_persona
negative_persona
contrast
source
source_type

Then check examples to see the paired completions behind the score.

Score

score = 100 * on_axis * off_axis_clean

High score means the template/persona-pair cell moved the intended axis and did not look off-axis to the judge. Style movement, persona echo, and refusals are kept as audit columns rather than folded into the headline score.

Provenance

Sources are marked in the dataset as source and source_type. Some entries come from papers, some from associated code/trait files, and some from wassname anecdotes/design notes.

Appendix: Run

uv sync
uv run python scripts/validate_persona_axes_openrouter.py \
  --dry-run \
  --axes data/persona_pairs_v2_candidates.jsonl \
  --templates data/templates_v2_candidates.txt \
  --family data/scenarios_v2_candidates.jsonl \
  --n 2 \
  --out out/dryrun.json

uv run python scripts/build_hf_dataset.py \
  --out /tmp/persona-steering-template-library-hf

Citation

@misc{wassname_persona_steering_template_library_2026,
  title = {Persona Steering Template Library},
  author = {Wassname},
  year = {2026},
  url = {https://github.com/wassname/persona-steering-template-library}
}