git:20260412.506d714 to git:20260416.dba95ac

---

name: onboarding

description: "Set up your personalized career-manager pipeline. Reads your resume, asks 1 question, then generates all config files so the pipeline is ready to run. Use when first setting up the pipeline or when your career goals change."

---

# Onboarding

Personalize the career-manager pipeline for a new user through a focused setup.

## When to Use

- First time setting up the pipeline after cloning

- Career goals, target roles, or compensation targets have changed

- Want to regenerate search queries or scoring criteria

## Flow

### Step 0: Ensure Local Python Environment

Before reading the resume or generating files, create a repo-local virtual environment if one doesn't already exist:

```bash

uv venv

uv pip install -r requirements.txt

```

Run all subsequent Python commands through `uv run` (e.g., `uv run python3 scripts/smoke_test.py`) so dependencies resolve against the repo-local `.venv` and never install into a global Python, Conda, or system environment.

### Step 1: Get the Resume

Ask: "Where's your resume? Give me the file path (e.g., ~/Desktop/resume.docx)"

When the user provides a path:

1. Verify the file exists and is `.docx` or `.pdf`

2. Copy it to `cv-tailor/data/CV/Master CV/` (create directory if needed)

3. Read the resume and extract: work history, skills, industries, seniority, education, accomplishments

If the file doesn't exist or isn't a supported format, ask again with guidance.

**Do NOT ask the user to copy files manually.** You handle the copy.

### Step 2: One Question

Ask this single combined question:

"Based on your resume, I can see your background. I need four things your resume doesn't tell me:

1. **Target roles** - What roles are you going after? Be specific about seniority (Senior PM, Director of Engineering, Solutions Architect) and domain.

2. **Salary floor** - What's the minimum base salary you'd accept?

3. **Location** - Remote, hybrid, or onsite? Open to relocation?

4. **Job markets** - Which countries should I search for jobs in? (Default: United States)"

### Step 3: Confirm Profile

Present the full profile combining resume inference + user answers:

"Here's your profile:

**Background:** [inferred from resume - key roles, industries, years of experience, strongest skills]

**Target roles:** [from answer]

**Salary floor:** [from answer]

**Location:** [from answer]

**Career paths I'd search:**

Derive paths in two phases:

1. **From resume:** For each sector or employer type the user spent 2+ years in, derive a career path. Group similar employers (e.g., multiple Big 4 firms = one "Professional Services / Advisory" path, not four). If no employer meets the 2-year threshold, use the user's longest-tenured roles regardless.

2. **From targets:** Add paths from the user's stated target roles that aren't already covered by Phase 1.

Merge overlapping paths. List Phase 1 paths first (longest tenure first), then Phase 2 paths. Final count: 3-8 paths.

Anything you'd change?"

Wait for confirmation. If the user tweaks anything, update and re-confirm.

### Step 4: Generate Files

After confirmation, generate all files **silently**. No overwrite warnings. No "about to write..." messages. Just write them.

**IMPORTANT:** Before writing each file, read the existing file first (Write tool requirement). Do this silently. Do NOT tell the user you're reading before writing. Do NOT warn about overwriting.

**Never touch pipeline data files** (target-companies.csv, applications.csv, etc.).

#### File 1: `config.yaml`

Generate from `config.yaml.example` template with:

- `paths.cv_base` set to the directory where the user's resume was found (parent of the file they provided)

- All integrations set to `false`

- Email fields left as placeholders

#### File 2: `job-search/references/criteria.md`

```

## Unique Value Proposition

[2-3 paragraphs: what makes this person uniquely valuable]

## Target Roles

[Role categories with descriptions]

## Target Company Types

[3-8 career paths. Each path has:]

### Path N: [Path Name]

- **Description:** [What kind of companies]

- **Example Companies:** [2-3 well-known examples for context, not monitored]

- **Role Types:** [Specific roles that fit]

- **Why You Fit:** [Connection to background]

- **Compensation Notes:** [Range based on salary floor]

## Evaluation Framework

### Group 1: Core Fit (4 dimensions)

### Group 2: Compensation & Career (3 dimensions)

### Group 3: Culture & Growth (3 dimensions)

## Scoring Guide

```

#### File 3: `job-search/references/background-context.md`

Generate with this structure:

```

## Personal Information

## Technical Capabilities

## Project Portfolio

## Professional Experience

## Education & Certifications

## Positioning Angles

## Travel & Location Preferences

```

#### File 4: `job-search/data/search-config.json`

Generate valid JSON matching this exact structure. **Pay close attention to types. `query_packs` and `path_check_instructions` are dicts, not lists.**

```json

{

"search_locations": ["United States"],

"query_packs": {

"<snake_case_path_key>": {

"label": "Human-Readable Path Name",

"queries": ["search query 1", "search query 2", "..."],

"locations": ["Remote", "United States"],

"job_type": "fulltime"

}

},

"role_include_patterns": ["regex_pattern", "..."],

"role_exclude_patterns": ["regex_pattern", "..."],

"employer_exclude_patterns": ["regex_pattern", "..."],

"location_exclude_patterns": ["regex_pattern", "..."],

"keywords": {

"domain": ["keyword", "..."],

"ai": ["keyword", "..."],

"tech": ["keyword", "..."]

},

"path_check_instructions": {

"1": "Multi-sentence discovery strategy for path 1...",

"2": "Multi-sentence discovery strategy for path 2..."

},

"role_patterns": ["human-readable role title", "..."],

"scoring": {

"domain_keywords": {"keyword": 8},

"ai_keywords": {"keyword": 6},

"role_keywords": {"keyword": 7},

"comp_indicators": {"keyword": 5},

"growth_indicators": {"keyword": 4}

},

"path_aliases": {

"fuzzy variant": "Exact Query Pack Label",

"another variant": "Exact Query Pack Label"

},

"display_groups": {

"Group Name": ["Query Pack Label 1", "Query Pack Label 2"],

"Another Group": ["Query Pack Label 3"]

}

}

```

**Critical rules:**

- `query_packs` is a **dict keyed by snake_case name**, NOT a list. Each value has `label`, `queries`, `locations`, `job_type`

- `path_check_instructions` is a **dict keyed by path number as string** ("1", "2", etc.), NOT a single string

- One query pack per career path (for JobSpy)

- All regex patterns must be valid Python regex, no inline flags like `(?i)`

- Minimum: 3+ paths, 3+ queries per pack

- `display_groups` groups related query pack labels into 3-4 dashboard display categories. Each label must match a query pack's `label` field exactly. Paths not mapped go to "Other"

**Search locations:**

- `search_locations` is the user's answer to "Which countries should I search for jobs in?"

- Default: `["United States"]` if the user doesn't specify

- If user says "Europe", expand to specific countries: Ireland, United Kingdom, Germany, Netherlands, France

- Populate each query pack's `locations` field from `search_locations` (plus "Remote")

- Auto-generate `location_exclude_patterns` to exclude countries NOT in `search_locations`. Do not add exclude patterns for countries the user wants to search in.

**Scoring keywords:** Each scoring dict maps keyword strings to integer weights. Higher weights = stronger signal for that category. Generate weights based on how central each term is to the user's target roles.

**Path aliases:** Map fuzzy LLM path variants to canonical query_pack labels. The LLM sometimes returns shortened or rephrased path names during evaluation. This map catches common variants so they normalize correctly. Generate 3-5 aliases per career path based on likely LLM rephrasings. Keys are lowercase variants, values are exact query_pack labels.

**Path check instructions:** Generate 3-4 sentence discovery strategies per path. Each instruction must include:

- What signals indicate a good fit for this path

- What to look for on careers pages

- What disqualifies a company

- Location requirements (reference `search_locations`)

See `onboarding/references/example-output.md` for a complete working example.

### Step 5: Verify Setup

Run smoke test checks **inline** (do not ask the user to run a separate command):

1. Verify all 4 generated files exist and are non-empty

2. Run `uv run python3 scripts/smoke_test.py` and verify all checks pass (this validates search-config schema, query_packs is a dict, path_check_instructions is a dict, dependencies, and config.yaml)

If all checks pass: "Setup complete."

If any check fails: report the specific issue and fix it.

### Step 6: Start First Search

Automatically hand off to the job-search skill and run the full pipeline. Do not ask the user for confirmation — they just completed setup, so the next step is always discovery.

## Re-run Behavior

If the user has already run onboarding (files exist):

- Ask: "You already have a profile set up. What's changed?"

- Only regenerate files that need updating

- Never touch pipeline data (target-companies.csv, applications.csv)

## Boundaries

- This skill generates config files only. It does not run the pipeline.

- It does not modify pipeline scripts.

- It does not create or modify the Master CV content. It only copies the user's file.