--- 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` Generate with this structure. The rubric must be CALIBRATED (tight, evidence-based) rather than loose; a well-calibrated rubric produces a spread of scores, not a cluster at the top. The average company the user's agents encounter should score around 50, not 70. ``` ## 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 10 scoring dimensions, each scored 0-10. Total score = sum (0-100). ### Group 1: Core Fit (4 dimensions) ### Group 2: Compensation & Career (3 dimensions) ### Group 3: Culture & Growth (3 dimensions) For EACH dimension you generate, produce: - **Name** and 1-line purpose. - **Score anchors** for 0, 3, 5, 7, 9 — each anchor is a concrete observable example, not an adjective. "Score 9 = public Levels.fyi data showing $200K+ TC at this level" is good. "Score 9 = very high comp" is bad. - **Evidence required for 7+.** Named source (job posting, employee review, funding announcement, public statement, etc.). If evidence is unavailable, score 5 or below — not 7+ by default. - **Default when unknown:** 5 (middle-of-band neutral), not 7. Emphasize in the rubric itself: "If you cannot find specific evidence, default to 5 rather than assuming the best case. A company without disclosed comp is a 5, not a 9." ## Scoring Guide Thresholds align with `pipeline.action_list.*_min_score` in `config.yaml`: - **85-100 (HIGH):** Pursue aggressively. Cold outreach or immediate apply. - **70-84 (MED):** Strong fit with current evidence — apply if role is open. - **60-69 (LOW):** Moderate fit. Watch list or skip unless specific role matches. - **<60:** Skip. **Handling unknowns:** If fewer than 5 of 10 dimensions are assessable, set `llm_flags: "needs_research"` and omit the score. Otherwise, unknown dimensions default to 5, not the top of the scale. **Distributional check:** When this rubric is applied to the user's existing target list, expect roughly: - ~20% score ≥85 (genuine A-tier) - ~30% score 70-84 (B-tier, apply-worthy) - ~35% score 60-69 (C-tier, watch) - ~15% score <60 (skip) If the distribution is heavily skewed high, the rubric is too loose — tighten anchors. ``` #### 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": { "": { "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.