suzheng/k2f

k2f-cv

Compile a complete academic or senior professional curriculum vitae from notes, publication lists, and appointment history—with correct section order, honest bibliographic detail, and readable long-list hierarchy (not a one-page job resume).

ソースを見る
リポジトリの原文

見出し、例、コード、表、リンク、参照画像を含む原文を表示しています。

K2F Cv — AI-Native Aesthetic Design

Purpose

Teach agents to blueprint CVs in content_and_design.md: scholarly sections, tiered long lists, citation-ready entries, and design intent—without inventing degrees, papers, grants, or metrics.

Built on K2F

This skill produces CVs as .K2F packages. Follow the k2f skill for authoring, pack/verify, and export. K2F keeps agents on sections and list roles while the engine handles multi-page flow and typography.

Before you build, install the core skill: npx skills add suzheng/k2f --skill k2f.

When to Use

  • Building or updating an academic or research CV (faculty, postdoc, PhD, clinician-scientist, senior technical fellow).
  • Merging exports (Scholar, BibTeX, old Word) into one structure.
  • Selected vs full lists for a search—not one-page resumes (k2f-resume).

Workflow

Step 1. Generate the contentanddesign.md

Single blueprint for semantic content and design intent. Use labeled blocks per section (identity, appointments, education, publications, etc.) that the k2f skill can map later.

Sub-steps

  1. CV frame — Discipline, career stage, opportunity type (tenure-track, fellowship, industry lab lead), geography, and page budget (often 2–6+ pages; justify length). Note required section order if the user cites a department or funder template.
  1. Input audit — Inventory appointments, degrees, publications, preprints, grants, teaching, service, talks, patents, supervision, awards, and links. Flag NEEDS USER INPUT for missing DOIs, dates, or author order; never fabricate journals, amounts, or co-authors.
  1. Section architecture (include / cut / merge) — Default academic stack (reorder only when the frame demands it):
  • Identity & contact → Research interests (optional, short) → Education → Appointments / experience (reverse chronological) → Publications → Grants & funding → Teaching → Invited talks → Service & leadership → Awards → Skills / methods (sparse) → Supervision & mentoring.
  • Cut high-school, obsolete jobs unrelated to the search, duplicate talk lists, full grant narratives, hobby sections unless normative in field.
  • Merge thin consulting into appointments; split publications into Selected vs Full when lists exceed ~15–20 items or the user targets a page cap.
  1. Scan layers — Glance: name, affiliation, contact, brief interests. Scan: section titles and entry leaders. Detail: dates, venues, IDs—subordinate in design intent.
  1. Entry patterns — Publications: one style (authors, title, venue, year, DOI); peer-reviewed vs invited; own-name emphasis only if field-appropriate. Appointments: role, unit, dates; 0–3 impact lines with user-supplied metrics. Grants/teaching: agency, amounts, course + term when known.
  1. Tailoring pass — For a stated search, elevate matching themes (methods, disease area, pedagogy) via section order, a short interests line, and Selected publications—not by adding unverified work.
  1. Design intent — Tone, hierarchy, density for long documents:
  • Tone: scholarly, precise, calm; no marketing superlatives; active voice for impact lines, neutral for bibliography.
  • Hierarchy: name and current affiliation dominate; section titles clear and conventional; entry titles stronger than dates and parentheticals.
  • Density: list-forward; avoid essay blocks; publications as continuous list rhythm (hanging indent intent); whitespace between major sections, tight within homogeneous lists.
  • Palette: restrained academic; one accent; readable contrast in long lists.

Rules & constraints

  • Truth gate: every degree, title, paper, grant, and number traces to user input or an explicit stub.
  • Bibliography gate: do not invent DOIs, page numbers, or author lists; use metadata incomplete rather than guessing.
  • Length gate: overflow → shorten via Selected lists and cut ancillary sections in the blueprint, not microscopic type.
  • Consistency gate: one date format, one citation style, stable institution names across sections.
  • Resume boundary: if the user needs a 1-page industry resume, redirect to k2f-resume in the blueprint note.

Common failure modes

  • Resume layout on a faculty CV → scholarly sections and publication emphasis.
  • Publication dump without citation rules → one style block for all entries.
  • Equal weight on every item → Selected plus optional full lists.
  • Missing appointment dates or degree years → halt with NEEDS USER INPUT; do not interpolate.
  • Keyword stuffing in a “Skills” wall → compact methods line or categorized skills tied to real work.
  • Inconsistent bolding of author name or mixed citation formats → single rule in design intent.
  • Burying current affiliation below ancient history → appointments and education ordered reverse-chron within each section.

Quality checklist

  • [ ] Frame: discipline, stage, opportunity, page budget, template constraints noted.
  • [ ] Section stack is conventional for field; cuts and Selected vs Full choices documented.
  • [ ] Glance layer shows identity, affiliation, and positioning without scrolling.
  • [ ] Publications (and grants/talks) share one citation pattern; own-name emphasis rule stated.
  • [ ] Appointments and education are complete on dates and institutions or flagged stubs.
  • [ ] Tailoring (if any) uses reorder/selection only—no fabricated entries.
  • [ ] Design intent covers tone, hierarchy, density, list rhythm, and accent for multi-page read.

Step 2. Use the contentanddesign.md and k2f skill to generate a K2F file and export to an appropriate file format.

Follow k2f to instantiate sections and flowing lists from the blueprint, apply roles from design intent, verify, and export the submission-ready CV (typically PDF or DOCX).

同じリポジトリから

関連する Skills

すべての Skills
suzheng
コミュニティ

k2f-business-report

Create executive-ready business reports from KPIs, sales or operational data, and stakeholder notes—with prioritized findings, trend context, and actionable recommendations. Use when the user asks for a business report, performance analysis, business review, KPI dashboard narrative, weakness analysis, or strategic improvement report from business data. Create business reports with AI-native design and the highest aesthetic standards, powered by the K2F format and engine. Purpose-built for AI age

導入数
13
GitHub Stars
2
更新日
10月1日
suzheng
コミュニティ

k2f-resume

Turn career history, notes, or an existing CV into a tailored, achievement-led resume with clear hierarchy and scannable proof—not a generic duty list or keyword-stuffed template. Use when the user asks for a resume, CV, job application document, or role-targeted career summary (including refresh for a specific job posting). Create resumes with AI-native design and the highest aesthetic standards, powered by the K2F format and engine. Purpose-built for AI agents, K2F lets them focus on semantic

導入数
9
GitHub Stars
2
更新日
10月1日
suzheng
コミュニティ

k2f-checklist

Blueprint operational, launch, diligence, or readiness checklists with phased flow, verifiable items, and scannable hierarchy—not a flat bullet dump or generic productivity template. Use when the user asks for a checklist, pre-flight list, deploy runbook checks, onboarding steps, audit tracker, or "what do we need before we ship." Create checklists with AI-native design and the highest aesthetic standards, powered by the K2F format and engine. Purpose-built for AI agents, K2F lets them focus on

導入数
8
GitHub Stars
2
更新日
10月1日
suzheng
コミュニティ

k2f

Create, design, edit, validate, and export elegant Word (docx), editable PDF, fillable AcroForm PDF, and Adobe InDesign (IDML) documents with K2F. Use when creating aesthetic Word, fillable PDF, AcroForm, IDML, or InDesign documents with AI agents, such as menus, certificates, planners, invitations, or worksheets and much more. K2F is a mature file format designed for AI agents to design and edit elegant aesthetic documents precisely and effortlessly. It has a rich ecosystem.

導入数
6
GitHub Stars
2
更新日
10月1日