/lockedin-render-jaso

name: lockedin-render-jaso
description: |
  Writes a Korean 자기소개서 from the user's experience. Two-turn
  writer/reviewer with a 5-dimension Korean rubric and banned-phrase
  filter.

  Activate when the user says "자소서", "자기소개서", "지원동기 써줘",
  "성장과정 써줘", "입사 후 포부", or names a Korean company plus a
  자소서 question.

render-jaso

Research-based calibration. RUBRIC.md ships with five dimensions and score bands. prompt-writer.md, prompt-reviewer.md, and banned_phrases.json (28 cross-source-confirmed entries) all ship.

Use this when

• User names a Korean company and asks to write a 자소서.
• User points at a 자소서 question (e.g., 지원동기 / 성장과정 / 성격의

장단점 / 입사 후 포부).

• User asks to "polish my 자소서" against the rubric.

Do NOT use when

• User wants an English resume → use `render-resume-en`.
• The vault is empty (no ontology nodes to quote) → seed first via

`/lockedin init` or `lockedin init --fixture FILE`.

Required design constraints (locked)

1. **두괄식** — conclusion / 핵심 / 차별점 in the first paragraph; the rest of the answer scaffolds the lead. 2. **구조화된 문맥** — within Korean 4-question convention (지원동기 / 성장과정 / 성격의 장단점 / 입사 후 포부 etc.), use STAR or PAR per paragraph. 3. **두루뭉술한 표현 제거** — banned-phrase regex check runs **before** the reviewer rubric pass. See `banned_phrases.json`. 4. **초개인화된 경험 기반** — every claim quotes a concrete ontology node by slug (e.g., `[[role/lead-pm-fintech-2024]]`). Vague generalities cost the 구체성 dimension. 5. **회사·직무 fit** — query the ontology for nodes with edges to the target company / 직무 / industry; surface the top-3 most relevant before drafting.

Two-turn writer/reviewer pattern

Run as **two separate Claude turns**:

1. **Writer turn** — produce the 자소서 draft. Quote ontology slugs. Apply banned-phrase filter to the draft. 2. **Reviewer turn** — clear the writer context. Re-load `RUBRIC.md` fresh. Score on `두괄식 / 구조화 / 구체성 / 표현 / 적합성` (0–5 each). Emit JSON. If any dimension < 4 OR `revisions_required: true`, return to the writer turn once with the review notes.

Same-turn self-evaluation inflates scores by ~1 point. Do not skip the separation.

Final checklist

• Banned-phrase regex pass ran (and was clean) before rubric.
• Reviewer turn was a separate Claude context with fresh RUBRIC.md load.
• Output JSON has all 5 dimensions ≥ 4 (or one revise cycle ran).
• Concrete ontology slugs are quoted in the rendered text.

Files in this directory

SKILL.md            (this file)
research-notes.md   citations with URL + ISO date + 2-sentence gloss
RUBRIC.md           5-dimension scoring contract + score bands
banned_phrases.json 28 cross-source-confirmed regex entries
prompt-writer.md    writer-turn prompt
prompt-reviewer.md  reviewer-turn prompt (re-loaded fresh)