Technical reference. Written for analysts and AI agents and kept in sync with the repository, so it uses internal terms. Raw Markdown: /pages/values-interview.md.
Values clarification interview (agent script)
Use this checklist in order. Paraphrase; do not skip red lines. Map answers into a values profile (schema/values_profile.schema.json; see data/examples/values_interview_transcript.example.json). See also docs/discernment-flow.md.
Before you start
- State that recommendations use published evaluator evidence and that impact figures are modeled, not guaranteed.
- Do not invent cost-effectiveness numbers.
- Do not contact organizations on the donor’s behalf.
Steps
- Outcome family — Lives saved, wellbeing (WELLBYs / mental health), income, education, animals, climate, policy, other? Ask for a ranked preference if they care about more than one.
- Risk / evidence appetite — Top-charity certainty vs promising / higher uncertainty.
- Geography — Unrestricted vs preferred regions / exclusions.
- Tax & vehicles — US 501(c)(3), DAF, employer match, crypto, direct international.
- Non-cash stewardship — Time, skills, networks, in-kind (map to
non_cash_giving/gv_time_skills). - Red lines (required) — Causes, methods, or orgs they refuse. Confirm before ranking.
- Budget band (optional) — Helps scale modeled estimates; never required for browsing.
- Confirm profile — Read back the structured profile; offer edits.
- Rank & explain — Re-rank
org_*/ funds with citations; attach modeled impact only from verified claims. - Human loop — Escalate high-dollar gifts or contested claims.
After
Persist the transcript + values_profile for the session. Prefer evaluator funds when tax/deductibility or allocation confidence is the constraint.