Recruiting ✦ 82outlineNot a hiring decision engine

Resume / recruiting screen

Who: Recruiting ops / ATS integrators. Steps: (1) State = resume JSON + role must-haves. (2) Score/Choice per requirement; Noul for red flags. (3) Code ranks / gates advance; low confidence → human. Expected effect: consistent first-pass filter. Caveat: not a hiring decision engine; no protected-class scoring.

1

Treat this as an outline — adapt state and questions to your data.

2

Implement in code — call System One / Jev; compose answers yourself.

3

Gate on confidence — act, confirm, or escalate before side effects.

Outline sketch

Prompt
Audience: recruiting ops / ATS.
Steps:
1) Parse resume into structured state + role must-haves.
2) Per requirement: Score/Choice; Noul red_flags.
3) Code ranks candidates; low confidence → human review.
Expected effect: consistent first pass.
Ban: protected-class or demographic scoring.

Needs access to: typesafe-sdkats

Who it's for

Recruiting ops / ATS integrators

Steps / how it's set up

Keep high-stakes decisions with humans. Sources: TypeSafe use-case map · recruiting cluster notes in awesome-jev-usecases · community CV entries in cobanov/awesome-jev (label community).

Prefer must-have Scores for hard gates (degree, years, location policy). Keep protected-class attributes out of prompts and scoring inputs; document legal review. Thresholds are not universal — label any published numbers as author-reported.

Sources (wave-2 deepen)

Expected effect

Same closed-label + confidence pattern as triage, applied to must-have checks.

Unofficial outline for learning. Paraphrased from public docs and tutorials — not a production recipe. Review sources before you automate anything.