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.
Scanned 5/27/2026
Install via CLI
openskills install daypunk/LockedIn---
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`:
1. Writer turn produces the resume markdown.
2. 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
```
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