lockedin-render-resume-en - SKILL.md Agent Skill

name: lockedin-render-resume-en description: | Writes an English resume from the user's experience, tuned to one of 10 built-in personas. Metric-first XYZ/CAR bullets, two-turn writer/reviewer with a 5-dimension rubric.

Activate when the user says "render resume", "make a resume", "polish my resume", or names a target role. The writer turn loads the matching spec from ./personas/ before drafting.

render-resume-en

Research-based calibration. Ships with full rubric, writer and reviewer prompts, and a banned-phrase regex list. Dimension definitions derived from cross-source consensus across 20+ US tech resume guides. See research-notes.md for citations.

Use this when

User asks for an English resume targeting a tech / PM persona.
User wants their existing resume "polished" against the rubric.

Do NOT use when

User wants a Korean cover letter → render-jaso.
The vault has no project / role / achievement nodes yet → seed first.

Required design constraints

Metric-first bullets — every bullet contains a number (%, x, $, count, or duration). Rubric enforces ≥80% metric density via regex.
XYZ or CAR per bullet — XYZ = "Accomplished X as measured by Y, by doing Z"; CAR = Challenge / Action / Result compressed to one bullet line. Active voice, quantified result. (STAR is the implicit story arc; XYZ/CAR is the bullet shape.)
Active voice — banned: "was responsible for", "helped to", "worked on", "was involved in".
No keyword stuffing — ATS-friendly via real verbs and metrics, not hidden keywords.
Target persona — 10 built-in personas under ./personas/ (us-tech-senior, us-tech-mid, pm-product, backend-senior, frontend-senior, mobile-senior, data-engineer-mid, ml-engineer-mid, designer-senior, marketing-mid). Each spec file contains tone guidance, action verb cluster, and persona-specific banned phrases.

Two-turn pattern

Same writer/reviewer split as render-jaso:

Writer turn produces the resume markdown.
Reviewer turn re-loads RUBRIC.md fresh, runs the metric-density regex, scores action-verb diversity, ATS keyword coverage, vagueness banlist. Emits JSON.

Final checklist

Metric-density regex passed (≥80% bullets contain a number).
Reviewer turn was a separate Claude context with fresh RUBRIC.md load.
Concrete ontology slugs quoted (project / role / achievement).
Active voice; no banned phrases.

Files in this directory

SKILL.md
research-notes.md           citations with URL + ISO date + 2-sentence gloss
RUBRIC.md                   5 dimensions; score bands; fixture authoring guide
prompt-writer.md            writer turn instruction
prompt-reviewer.md          reviewer turn instruction (separate Claude context)
banned_phrases.json         regex list of weak / vague / templated phrases
personas/
  us-tech-senior.md         Senior IC / Staff / Principal
  us-tech-mid.md            Mid-level IC (3-7y)
  pm-product.md             Product Manager
  backend-senior.md         Senior backend engineer, distributed systems focus
  frontend-senior.md        Senior frontend engineer, perf + design system focus
  mobile-senior.md          Senior iOS/Android engineer
  data-engineer-mid.md      Mid-level data engineer, dbt/Airflow/warehouse
  ml-engineer-mid.md        Mid-level ML engineer, classical ML productization
  designer-senior.md        Senior product / UX designer
  marketing-mid.md          Mid-level growth / product marketing manager