resume-tailoring

这段内容是审核时保存的快照。外部来源才是完整且最新的版本。

---
name: resume-tailoring
description: Use when creating tailored resumes for job applications - researches company/role, creates optimized templates, conducts branching experience discovery to surface undocumented skills, and generates professional multi-format resumes from user's resume library while maintaining factual integrity
---

# Resume Tailoring Skill

## Overview

Generates high-quality, tailored resumes optimized for specific job descriptions while maintaining factual integrity. Builds resumes around the holistic person by surfacing undocumented experiences through conversational discovery.

**Core Principle:** Truth-preserving optimization - maximize fit while maintaining factual integrity. Never fabricate experience, but intelligently reframe and emphasize relevant aspects.

**Mission:** A person's ability to get a job should be based on their experiences and capabilities, not on their resume writing skills.

## When to Use

Use this skill when:
- User provides a job description and wants a tailored resume
- User has multiple existing resumes in markdown format
- User wants to optimize their application for a specific role/company
- User needs help surfacing and articulating undocumented experiences

**DO NOT use for:**
- Generic resume writing from scratch (user needs existing resume library)
- Cover letters (different skill)
- LinkedIn profile optimization (different skill)

## Quick Start

**Required from user:**
1. Job description (text or URL)
2. Resume library location (defaults to `resumes/` in current directory)

**Workflow:**
1. Build library from existing resumes
2. Research company/role
3. Create template (with user checkpoint)
4. Optional: Branching experience discovery
5. Match content with confidence scoring
6. Generate MD + DOCX + PDF + Report
7. User review → Optional library update

## Implementation

See supporting files:
- `research-prompts.md` - Structured prompts for company/role research
- `matching-strategies.md` - Content matching algorithms and scoring
- `branching-questions.md` - Experience discovery conversation patterns

## Workflow Details

### Multi-Job Detection

**Triggers when user provides:**
- Multiple JD URLs (comma or newline separated)
- Phrases: "multiple jobs", "several positions", "batch", "3 jobs"
- List of companies/roles: "Microsoft PM, Google TPM, AWS PM"

**Detection Logic:**

```python
# Pseudo-code
def detect_multi_job(user_input):
    indicators = [
        len(extract_urls(user_input)) > 1,
        any(phrase in user_input.lower() for phrase in
            ["multiple jobs", "several positions", "batch of", "3 jobs", "5 jobs"]),
        count_company_mentions(user_input) > 1
    ]
    return any(indicators)
```

**If detected:**
```
"I see you have multiple job applications. Would you like to use
multi-job mode?

BENEFITS:
- Shared experience discovery (faster - ask questions once for all jobs)
- Batch processing with progress tracking
- Incremental additions (add more jobs later)

TIME COMPARISON (3 similar jobs):
- Sequential single-job: ~45 minutes (15 min × 3)
- Multi-job mode: ~40 minutes (15 min discovery + 8 min per job)

Use multi-job mode? (Y/N)"
```

**If user confirms Y:**
- Use multi-job workflow (see multi-job-workflow.md)

**If user confirms N or single job detected:**
- Use existing single-job workflow (Phase 0 onwards)

**Backward Compatibility:** Single-job workflow completely unchanged.

**Multi-Job Workflow:**

When multi-job mode is activated, see `multi-job-workflow.md` for complete workflow.

**High-Level Multi-Job Process:**

```
┌─────────────────────────────────────────────────────────────┐
│ PHASE 0: Intake & Batch Initialization                      │
│ - Collect 3-5 job descriptions                              │
│ - Initialize batch structure                                │
│ - Run library initialization (once)