# How the `/setup` Command Populates the Candidate Profile in AI-Job-Search > Discover how the /setup command populates candidate profiles in AI-Job-Search. Learn about ingesting documents, parsing CVs, guided interviews, and smart data merging. - Repository: [Mads Lorentzen/ai-job-search](https://github.com/MadsLorentzen/ai-job-search) - Tags: how-to-guide - Published: 2026-08-31 --- **The `/setup` command populates the candidate profile by either ingesting existing documents (Path A), parsing a single CV (Path B), or conducting a guided interview (Path C), then applying an idempotent change-set algorithm that merges new data into canonical skill files without overwriting existing content.** The AI-Job-Search repository by MadsLorentzen provides a structured workflow for automating job applications through Claude Code. At the heart of this system lies the `/setup` command defined in [`.claude/commands/setup.md`](https://github.com/MadsLorentzen/ai-job-search/blob/main/.claude/commands/setup.md), which transforms raw professional data into a structured candidate profile that serves as the single source of truth for downstream commands like `/scrape`, `/rank`, and `/apply`. ## The Four-Layer Onboarding Architecture The `/setup` command operates through four logical layers that map directly to the specification in [`.claude/commands/setup.md`](https://github.com/MadsLorentzen/ai-job-search/blob/main/.claude/commands/setup.md). Each layer represents a distinct phase of the profile population pipeline, from initial path selection to final file generation. ### Layer 0: Path Selection Logic Lines 9-31 of the command specification define how the system determines the onboarding method. The command first checks for a `--section ` argument. If provided, it skips the interactive chooser and targets a specific profile component for updates. Otherwise, it inspects the Git remote configuration and the `documents/` folder to present three distinct onboarding paths: - **Path A**: Read from a populated `documents/` folder (recommended for users with existing CVs, LinkedIn exports, or portfolios) - **Path B**: Import from a single CV file (optimized for quick starts) - **Path C**: Guided interview mode (conversational data collection) ### Path A: Document Ingestion with Merge Intelligence Path A represents the most comprehensive ingestion strategy, implemented across six sequential steps that ensure data integrity through intelligent merging: **1. Inventory (Lines 78-94)** The command executes a glob pattern `documents/**/*` to catalog all existing files, establishing a complete inventory of available source material. **2. Read Existing Skill Files (Lines 96-107)** Before processing new data, the command loads the seven job-application-assistant skill files. This establishes the current state of the candidate profile, enabling the system to detect additive versus conflicting information. **3. Parse Documents (Lines 110-126)** The ingestion engine parses each document type—CVs, LinkedIn exports, diplomas, references, and past applications—extracting structured facts including contact information, education history, work experience, language proficiency, and certifications. **4. Cross-Reference (Lines 130-140)** The system surfaces inconsistencies such as date mismatches or title differences between sources. These conflicts are flagged for user resolution rather than being automatically merged. **5. Build Change Sets (Lines 56-68)** Extracted facts are classified into two categories: *additive* data (new information not present in existing files) and *conflicting* data (information that contradicts existing entries). This classification occurs specifically against the baseline established in [`01-candidate-profile.md`](https://github.com/MadsLorentzen/ai-job-search/blob/main/01-candidate-profile.md). **6. Present and Confirm** The command displays the proposed change set to the user. Only after explicit approval does the system proceed to write operations, ensuring human oversight of all profile modifications. ### Path B: Single CV Import Defined at Lines 49-57 of the specification, Path B streamlines onboarding for users with a single comprehensive CV. The command parses the supplied document using the same extraction engine as Path A, identifies missing fields in the data model, asks follow-up questions to complete the profile, and proceeds directly to the file generation step without the cross-referencing complexity of multi-document ingestion. ### Path C: Guided Interview Mode Lines 62-122 specify the conversational flow for Path C. The AI conducts a structured interview covering identity, education, experience, skills, career goals, and preferences. Each answer maps directly to a field in the canonical profile schema, building the candidate profile from scratch through dialogue rather than document parsing. ## Canonical Profile Generation (Step 3) Regardless of the ingestion path chosen, Step 3 (Lines 48-71 of the specification) generates or updates eight canonical files that constitute the complete candidate profile: - [`CLAUDE.md`](https://github.com/MadsLorentzen/ai-job-search/blob/main/CLAUDE.md) – Overall metadata and assistant configuration - [`01-candidate-profile.md`](https://github.com/MadsLorentzen/ai-job-search/blob/main/01-candidate-profile.md) – The core structured profile (primary source of truth) - [`02-behavioral-profile.md`](https://github.com/MadsLorentzen/ai-job-search/blob/main/02-behavioral-profile.md) – Inferred and stated behavioral traits - [`04-job-evaluation.md`](https://github.com/MadsLorentzen/ai-job-search/blob/main/04-job-evaluation.md) – Skill-match scoring criteria and preferences - [`05-cv-templates.md`](https://github.com/MadsLorentzen/ai-job-search/blob/main/05-cv-templates.md) – CV structure and formatting preferences - [`07-interview-prep.md`](https://github.com/MadsLorentzen/ai-job-search/blob/main/07-interview-prep.md) – Interview preparation data and talking points - `cv/main_example.tex` – LaTeX template with populated placeholders - [`search-queries.md`](https://github.com/MadsLorentzen/ai-job-search/blob/main/search-queries.md) – Tailored search queries consumed by the `/scrape` agent The write mechanism employs a **read-before-write** strategy. It loads existing content, replaces only specific placeholders with new data, and preserves all manual edits made by the user outside the automated workflow. ## The Change-Set Algorithm and Idempotent Design The profile population mechanism relies on a sophisticated merge algorithm centered on [`.claude/skills/job-application-assistant/01-candidate-profile.md`](https://github.com/MadsLorentzen/ai-job-search/blob/main/.claude/skills/job-application-assistant/01-candidate-profile.md). During Path A ingestion, Step A2 loads the current file state, while Step A5 constructs the change set by comparing extracted facts against this baseline. **Additive facts**—such as a newly discovered certification or language skill—are queued for insertion. **Conflicting facts**—such as two different graduation dates for the same degree—become conflict items requiring user resolution. Step A7 executes writes only for approved additions, ensuring that: - Re-running `/setup` never creates duplicate entries - Existing manual edits remain intact - The profile always reflects the most up-to-date information without data loss This idempotent design makes the `/setup` command safe to execute repeatedly as the user's career evolves. ## Usage Examples Initiate the full setup workflow with interactive path selection: ```text /user> /setup ``` Update only the search query configuration without touching the core profile: ```text /user> /setup --section search ``` The assistant responds by detecting the section flag and targeting only [`.claude/skills/job-scraper/search-queries.md`](https://github.com/MadsLorentzen/ai-job-search/blob/main/.claude/skills/job-scraper/search-queries.md) for updates. ## Summary - The `/setup` command offers three distinct data ingestion paths: multi-document analysis (Path A), single CV import (Path B), and conversational interview (Path C) - Path A employs a six-step process that inventories documents, parses structured data, cross-references for inconsistencies, and builds classified change sets - The command generates eight canonical files including [`01-candidate-profile.md`](https://github.com/MadsLorentzen/ai-job-search/blob/main/01-candidate-profile.md), which serves as the single source of truth for the entire AI-Job-Search workflow - An idempotent change-set algorithm ensures re-running the command never duplicates data and preserves manual edits - Section-specific updates via `--section ` allow granular profile maintenance without full re-ingestion ## Frequently Asked Questions ### What specific files does the `/setup` command modify? The command creates and updates eight primary files: [`.claude/skills/job-application-assistant/01-candidate-profile.md`](https://github.com/MadsLorentzen/ai-job-search/blob/main/.claude/skills/job-application-assistant/01-candidate-profile.md) (core profile), [`02-behavioral-profile.md`](https://github.com/MadsLorentzen/ai-job-search/blob/main/02-behavioral-profile.md), [`04-job-evaluation.md`](https://github.com/MadsLorentzen/ai-job-search/blob/main/04-job-evaluation.md), [`05-cv-templates.md`](https://github.com/MadsLorentzen/ai-job-search/blob/main/05-cv-templates.md), [`07-interview-prep.md`](https://github.com/MadsLorentzen/ai-job-search/blob/main/07-interview-prep.md), [`CLAUDE.md`](https://github.com/MadsLorentzen/ai-job-search/blob/main/CLAUDE.md), `cv/main_example.tex`, and [`.claude/skills/job-scraper/search-queries.md`](https://github.com/MadsLorentzen/ai-job-search/blob/main/.claude/skills/job-scraper/search-queries.md). All writes use a read-before-write strategy that preserves existing content. ### Can I run `/setup` multiple times without duplicating my data? Yes. The command implements an idempotent change-set algorithm. It loads existing skill files before processing, classifies new data as either additive or conflicting, and only writes approved additions. Re-running the command with updated documents will merge new facts without creating duplicates of existing entries. ### How does the system handle conflicting information between documents? During Path A ingestion, the cross-reference step (Lines 130-140) detects inconsistencies such as date mismatches or differing job titles. These become *conflict* items in the change set that require explicit user resolution before writing. The system does not automatically overwrite existing data with conflicting new data. ### What is the difference between Path A and Path B ingestion? Path A analyzes an entire `documents/` folder containing multiple files (CVs, LinkedIn exports, diplomas), performs cross-referencing to detect inconsistencies, and builds a comprehensive change set. Path B processes a single CV file, asks follow-up questions for missing fields, and bypasses the complex merge logic, making it faster for users starting with one comprehensive document.