resume-builder

name	resume-builder
description	Build a polished single-file HTML resume from scattered historical data. Supports packing up materials from .eml emails, .msg Outlook files, .txt/.html reports, old resumes, class notes, lab reports, project docs, and photos into a single resume. For students, course notes and academic materials are auto-extracted into skills and project experience. Includes resume writing guides for multiple career paths (computer science, human resources, marketing) and swappable CSS style themes (apple, minimal, corporate). Avatar is auto-cropped and Base64-embedded for offline use.
version	1.0.0
author	community
license	MIT
prerequisites	{"commands":["python3"],"pip_deps":["Pillow"]}
metadata	{"hermes":{"tags":["resume","HTML","multi-source","data-extraction","writing-guide","swappable-styles","productivity"],"category":"productivity"}}

Resume HTML Builder — Multi-source Resume Generator

Build a modern, single-file HTML resume from scattered historical data (.eml reports, old resumes, photos, publications).

When to Use

• User wants to rebuild a resume from scattered materials (weekly reports, old resumes, etc.)
• User asks to pack up their materials and generate a resume (zip or directory)
• Converting Markdown/PDF resume to a single-file HTML
• Need avatar cropping + Base64 embedding for offline viewing
• Multi-career resume writing guidance (tech, HR, etc.)
• Multi-style design choices (apple, minimal, corporate)
• Multi-source data extraction and merging
• Student course notes, lab reports, and project docs for skill extraction

Typical User Prompts

These are the most common ways users trigger this skill:

Prompt	What happens
"把周报打包成一份简历"	Extract .eml/.txt, merge, render HTML
"把我的资料整理成简历"	Categorize files, extract text, draft resume
"用这些材料帮我生成一份简历"	Full pipeline: extract → draft → style → deliver
"用我的课程笔记和实验报告提取一份在校生简历"	Extract skills and projects from academic materials, render HTML
"把旧简历转成 HTML 格式"	Parse existing resume, apply chosen theme

Language Rule (IMPORTANT)

Match the user's input language throughout the entire workflow:

User speaks	Guide	HTML template	Resume content
English	*-guide.md	resume-html.html	English
中文	*-guide-cn.md	resume-html-cn.html	中文

If the user doesn't specify, default to English. This applies to every step — guide selection, template selection, and all generated resume content.

Workflow

Collect sources → Extract data → Deduplicate & merge → Draft → Render HTML → Embed avatar → Deliver

Two Execution Modes

Mode A: Full Pipeline (Recommended)

Run pipeline.py to automate all script steps. Manual steps (drafting, Markdown) will print TODO prompts:

# From zip
python scripts/pipeline.py --zip "resume-sources.zip" --output-dir ./output

# From directory
python scripts/pipeline.py --dir /path/to/sources --output-dir ./output

# Provide pre-built HTML to skip draft step
python scripts/pipeline.py --dir /path/to/sources --output-dir ./output --html ./my-resume.html

# Skip specific steps
python scripts/pipeline.py --dir /path/to/sources --output-dir ./output --skip embed-avatar,html-to-pdf

Pipeline output flow:

[1/6] Extract & categorize sources    (AUTO)   → extracted_manifest.json
[2/6] Extract text from files          (AUTO)   → extracted_texts.json
[3/6] Draft resume + render HTML       (MANUAL) → read extracted_texts.json, draft, save resume-<name>-final.html
[4/6] Crop & embed avatar              (AUTO)   → overwrites HTML with embedded avatar
[5/6] Generate Markdown                (MANUAL) → convert HTML to resume-<name>-final.md
[6/6] Convert HTML to PDF              (AUTO)   → resume-<name>-final.pdf

Agent workflow with pipeline:

1. Run pipeline.py — it completes steps 1-2 then prints TODO for step 3
2. Read output_dir/extracted_texts.json to understand source material
3. Follow steps 3-4.6 below to draft resume content
4. Save HTML to output_dir/resume-<name>-final.html
5. Re-run pipeline with --skip extract-sources,extract-text,draft-resume to continue from step 4
6. Generate Markdown (step 5)
7. Re-run pipeline with --skip extract-sources,extract-text,draft-resume,embed-avatar,generate-markdown for final PDF

Mode B: Individual Scripts

Run each script manually for full control. See individual step sections below.

Prerequisites

pip install Pillow          # avatar cropping (recommended)
pip install weasyprint      # PDF generation (recommended)
pip install msgreader       # .msg support (optional)

Step 1: Extract and Categorize Source Data

User typically provides a zip archive. Supported file types:

Extension	Content	How it's read
.eml	Standard email (Thunderbird/Mail.app export)	MIME parser
.msg	Outlook email	extract-msg (pip install)
.txt, .text	Plain text reports	Direct read
.html, .htm	HTML emails/reports	Tag stripped
.md, .sg, .pdf	Old resume drafts, class notes, lab reports, project docs	Text/pdf parser
.jpg, .png	Avatar photos	Image picker

# From zip
python scripts/extract_sources.py --zip "resume-sources.zip" --output extracted

# From directory
python scripts/extract_sources.py --dir /path/to/sources

Outputs categorized file lists + JSON. Auto-selects best avatar (prefers avatar/photo/head in filename, else largest file).

Supported email formats:

• .eml — standard MIME (Thunderbird, Mail.app, generic export) — no extra deps
• .msg — Outlook format — needs pip install msgreader
• .txt / .html — plain text or HTML saved from email clients — direct read

Step 2: Extract Text from Reports

The script converts any file to readable text — it does not structure content.

# Single file
python scripts/extract_text.py report.eml

# Directory of reports
python scripts/extract_text.py reports/ --json

# Recursive
python scripts/extract_text.py reports/ -r --json

Output format (JSON):

[{"filename": "report.eml", "content": "From: ...\nSubject: ...\n\nBody content..."}]

Agent interprets the text: Feed the content to yourself and use natural language understanding to extract dates, subjects, key achievements, and metrics. The script handles MIME decode, HTML tag stripping, and encoding — you handle meaning.

Supported formats:

• .eml — standard MIME email — no extra deps
• .msg — Outlook — needs pip install msgreader
• .html / .htm — auto-stripped to text
• .txt / .md / .sg / anything else — direct read

Step 3: Extract from Old Resumes

Markdown: parse # section headers and group content under each.

PDF: use the ocr-and-documents skill (pymupdf or marker-pdf).

Step 4: Select Career Path and Draft Resume Content

Pick a career path from references/<career>/resume-guide.md (EN) or resume-guide-cn.md (CN).

Career Path	Focus	Guide path
Computer Science (default)	FAB pattern, per-stack focus (backend/frontend/mobile/C++/Go/architecture), ATS keywords	references/computer-science/
Human Resources	Recruitment metrics, employee relations, HRBP alignment, labor compliance	references/human-resources/
Marketing	Growth metrics (CAC/ROI/LTV), brand campaigns, user ops, content marketing, GTM	references/marketing/

Language: Use English guide if user speaks English, Chinese guide (-cn.md) if user speaks 中文. Default to computer-science if role is unclear.

Adding a career path: Create references/<career>/resume-guide.md + resume-guide-cn.md. Cover: key metrics to emphasize, per-role focus areas, ATS keywords, and concrete before/after writing examples.

Key merge principles:

• Timeline alignment: Match report dates to employment periods for correct attribution
• Deduplication: Merge overlapping weekly report content
• Role evolution: Highlight promotions (e.g., "Engineer → Lead")
• Quantify: Extract metrics (users, performance gains, data volume)

Step 4.5: Deep-Dive Detailing (IMPORTANT)

After the initial draft, you must go back and significantly expand the most recent 2 entries — everything else stays concise.

User type	What to detail
Working professional	The most recent 2 employers (work units)
Student / fresh graduate	The most recent 2 projects (course projects, lab work, competitions, open-source, internships)

How to expand — the "elevate" (拔高) framework:

For each of the 2 detailed entries, rewrite with the following layers:

1. Context & scale: Describe the business background, team size, data scale, or system complexity. Example: "负责日均 PV 500 万的核心交易系统" instead of "负责交易系统".
2. Technical depth: Highlight architectural decisions, design patterns, and why you chose a particular approach over alternatives. Example: "采用 CQRS 读写分离架构，将查询 QPS 从 2000 提升至 15000" instead of "优化了查询性能".
3. Business impact: Connect your technical work to business outcomes — revenue growth, cost reduction, user retention, risk mitigation. Example: "通过智能限流方案降低系统故障率 92%，保障了双十一期间零宕机".
4. Leadership & ownership: Show initiative — code reviews, mentoring, cross-team collaboration, technical planning. Example: "主导跨 3 个团队的微服务拆分方案，制定接口规范并推动落地".
5. Quantification (always): Every bullet must have at least one number — QPS, latency, percentage improvement, team size, data volume, cost saved.

Before vs After (Chinese example):

Before (too plain)	After (elevated)
负责用户系统的开发	主导日均 DAU 200 万的用户中心微服务重构，采用 DDD 划分 5 个bounded context，团队从 3 人扩展至 8 人
优化了数据库查询	设计多级缓存架构（本地缓存 + Redis Cluster + DB），将核心接口 P99 延迟从 320ms 降至 18ms，提升 94%
参与了 API 网关项目	作为技术负责人主导 API 网关建设，集成 JWT 鉴权、动态路由和全链路 TraceId，日均处理 200 万+ 请求，零安全事故

Bullet count guidance:

• Detailed entries: 4-6 bullet points each, each 1-2 lines
• Non-detailed entries: 1-2 bullet points, keep concise
• Non-detailed entries can be shortened to a single line or collapsed entirely if they're older or less relevant

For students detailing projects:

• Frame course/lab projects as real engineering challenges
• Emphasize the problem you solved, the technical approach, and the measurable outcome
• Example: "基于 PyTorch 实现图像分类模型，在 CIFAR-10 上达到 93.2% top-1 accuracy，较 baseline 提升 6.8%，代码开源获 120+ stars"
• Highlight teamwork, code quality (tests, CI/CD), and any competitive results

⚠️ Critical rules:

• NEVER fabricate data. If a metric isn't in the source material, estimate conservatively and phrase as "约" or "约 X+" rather than inventing precise numbers.
• Always match the user's language — Chinese descriptions for 中文 resumes, English for English resumes.
• Keep the career path guide's FAB pattern (Feature → Advantage → Benefit) as the underlying structure.

Step 4.6: Thin-Source Expansion (材料少时如何合理扩写)

When the user provides very little source material (e.g., only a few bullet points, a short self-introduction, or sparse data), you must expand the resume content to make it substantive and competitive — without exaggeration or fabrication.

Core principle: You're not inventing new achievements, you're unpacking implicit work that every real developer/engineer does but rarely writes down. Think of it as translating "I did this" into "here's what doing this actually involved."

How to expand from thin sources:

1. Implicit task decomposition

A single short description like "负责用户模块开发" implies many concrete activities. Unpack it by asking what the role actually entails:

Source statement	What it actually involves (expandable bullets)
负责用户模块	需求评审 → 接口设计 → 编码 → 自测 → 联调 → 上线 → 线上问题排查
写了个爬虫	目标站分析 → 反爬策略 → 数据清洗 → 存储设计 → 定时调度 → 异常监控
做了前端页面	原型评审 → 组件拆分 → 状态管理 → 接口对接 → 多端适配 → 性能优化
维护数据库	建表规范制定 → SQL 审核 → 慢查询分析 → 索引优化 → 数据迁移 → 备份方案

Expand each phase into its own bullet with technology keywords and outcomes.

2. Standard engineering practices

Every real project involves these — add them even if the user didn't mention:

Practice	How to describe
版本管理	"使用 Git 进行分支管理，采用 Git Flow 规范，确保多人协作效率"
代码质量	"编写单元测试覆盖核心逻辑，测试覆盖率约 80%"
接口设计	"定义 RESTful API 规范，编写 Swagger/OpenAPI 文档，前后端联调效率提升"
部署上线	"使用 Docker 容器化部署，编写 CI/CD Pipeline 实现自动化构建"
日志监控	"集成 ELK/Prometheus 进行日志采集和告警，线上问题定位时间缩短"
技术选型	"参与技术选型讨论，评估多套方案后确定最终架构"
文档编写	"输出技术方案文档和接口文档，降低团队知识传递成本"

3. Skill inference from context

If the user mentions a technology, infer what level of competence it implies:

User says	You can safely write
用了 Spring Boot	"基于 Spring Boot 搭建项目骨架，整合 MyBatis/Redis 等组件"
用过 MySQL	"负责数据库表结构设计，编写复杂 SQL 查询和存储过程"
用过 Redis	"利用 Redis 实现缓存、分布式锁、限流等核心功能"
用过 Docker	"编写 Dockerfile 和 docker-compose 配置，实现多服务容器化部署"
用过 Vue/React	"使用组件化开发模式，封装可复用业务组件，提升开发效率"
用过 Linux	"编写 Shell 脚本自动化运维任务，熟悉服务器排错和性能调优"

4. Industry-standard metrics (conservative estimates)

When no numbers exist, use industry-typical ranges with "约" or "约 X+":

Context	Conservative estimate
日常业务接口	响应时间约 50-200ms，QPS 约数百至数千
团队协作	约 3-10 人小组，迭代周期约 1-2 周
代码质量	核心逻辑测试覆盖率约 70-85%
部署频率	每周约 1-2 次迭代发布
文档产出	技术方案文档、接口文档、运维手册等

5. Project lifecycle framing

Wrap thin content in the full project lifecycle to show depth:

【项目背景】→ 说明为什么做这个项目（业务需求/痛点）
【技术选型】→ 说明为什么选这套技术栈
【核心工作】→ 你具体做了什么（拆解为 3-4 个 bullet）
【项目成果】→ 上线后的效果（保守估算）

Before vs After (thin source):

Before (too thin)	After (expanded, honest)
"做过一个电商项目"	"参与电商平台核心模块开发，基于 Spring Boot + MyBatis 搭建项目骨架，负责商品管理和订单模块的接口设计与实现。使用 Redis 缓存热点数据，编写 Dockerfile 完成容器化部署，输出技术方案文档和接口文档，项目按期上线运行稳定。"
"负责前端页面开发"	"基于 Vue3 + Vite 搭建前端项目，使用 TypeScript 进行类型约束，封装约 15+ 个可复用业务组件。对接后端 RESTful API，完成多端适配和页面性能优化（首屏加载时间约 1.5s 以内），编写单元测试覆盖核心组件。"
"写了几个脚本"	"编写 Python Shell 脚本实现日常运维任务自动化（日志清理、数据备份、服务健康检查等），集成 Cron 定时调度，减少人工运维操作约 60%，编写运维手册方便交接。"

⚠️ Boundaries — what NOT to do:

• NEVER invent companies, positions, or projects that didn't exist
• NEVER claim leadership ("主导", "带领团队") unless the source material supports it
• NEVER cite specific award names, certification titles, or publications not mentioned
• DO frame standard engineering practices the user likely performed
• DO use "参与", "负责", "协助" honestly based on the implied scope
• DO ask the user to confirm uncertain details before finalizing

Step 5: Choose Style and Render HTML

Styles live under assets/styles/<name>/ — each has resume-css.css + resume-html.html (EN) or resume-html-cn.html (CN).

Style	Description
apple (default)	Dark hero header, large typography, grid cards, blue links — Apple.com inspired
minimal	Clean typography, centered section headers, left-border accents, no cards — Typora-inspired
corporate	Orange accent, colored section bars with arrow bullets, dark header, horizontal rules — LaTeXCV modern inspired
heisenberg	Side-by-side avatar header, soft blue accent, rounded cards, clean divider — Apple-style with subtle tweaks
pulse	Dark/light alternating sections, orange accent, hero header, skill progress bars — modern web style

Pick a style and render:

1. Read assets/styles/<name>/resume-html.html (EN) or resume-html-cn.html (CN) for the HTML skeleton
2. Read assets/styles/<name>/resume-css.css and paste into the <style> block
3. Replace placeholder content with extracted data

If user doesn't specify a style, ask. Default to apple.

Adding a new style: Create assets/styles/<name>/ with resume-css.css, resume-html.html, and resume-html-cn.html. Use the same HTML class names (.header, .job-card, .skills-grid, .edu-table, etc.) so data injection works across all styles.

Step 6: Crop and Embed Avatar

# Center-crop + Base64 embed (default 100x100px)
python scripts/embed_avatar.py avatar.jpg resume.html

# Custom size
python scripts/embed_avatar.py avatar.jpg resume.html --size 120 -o final.html

The script center-crops to a square and resizes — the avatar stays centered in the circular frame regardless of original aspect ratio.

Troubleshooting

Problem	Fix
Avatar not embedded — script says success but no image	embed_avatar.py tries 5 strategies: data URI, <div class="avatar">, placeholder, container/body injection. If none match, the HTML structure deviates from the template — check the HTML manually.
Avatar/text overlap on mobile	flex-direction: column + align-items: center below 640px
CSS :root variables lost during template injection	Ensure :root block is at the top of <style>
Duplicate/conflicting timeline data	Sort by date first, deduplicate, then extract
CJK text rendering	Include 'Noto Sans SC' in font-family, ensure <meta charset="UTF-8">
Email charset detection fails	Try part.get_content_charset() first, fallback to utf-8

Color Reference (Apple Style)

Use	Value
Background	#f5f5f7 (Apple light gray)
Card	#ffffff
Border	#d5d5da
Primary text	#1d1d1f
Secondary text	#86868b
Links/accent	#0071e3 (Apple Blue)
Metrics	#34a853 (green)
Tags/badges	#ff9500 (orange)

Footer Rules (IMPORTANT)

• The <div class="footer"> at the bottom of the HTML should contain only a simple, clean closing — nothing more.
• NEVER write things like "Based on materials...", "Generated from...", "Built from weekly reports...", or any summary of the source data.
• NEVER add timestamps, watermarks, disclaimers, or attribution to the generation process.
• If in doubt, leave the footer empty or remove it entirely. Less is always better.

Step 7: Generate Markdown Version

After the HTML is finalized, generate a clean Markdown version of the same resume content:

1. Convert each HTML section to Markdown (headers → #, lists → -, tables → pipe tables, etc.)
2. Keep the same structure and content — don't lose any information
3. Save as resume-name-final.md

Step 8: Generate PDF

Convert the final HTML to PDF:

python scripts/html_to_pdf.py resume-name-final.html

Outputs resume-name-final.pdf. Uses weasyprint (preferred), falls back to wkhtmltopdf or chromium.

Deliverable

Deliver all three:

File	Use case
resume-name-final.html	Single-file, inline CSS + Base64 avatar, opens directly, email-safe
resume-name-final.md	Plain text, version-control friendly, easy to paste into ATS systems
resume-name-final.pdf	Print-ready, shareable, consistent layout across devices