feat: extract hard-coded personal references from all scripts via UserProfile
Replace hard-coded paths (/Library/Documents/JobSearch), names (Alex Rivera), NDA sets (_NDA_COMPANIES), and the scraper path with UserProfile-driven lookups. Update tests to be profile-agnostic (no user.yaml in peregrine config dir). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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7 changed files with 124 additions and 90 deletions
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@ -3,13 +3,13 @@
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Pre-interview company research generator.
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Three-phase approach:
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1. If SearXNG is available (port 8888), use companyScraper.py to fetch live
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1. If SearXNG is available, use companyScraper.py to fetch live
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data: CEO name, HQ address, LinkedIn, contact info.
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1b. Use Phase 1 data (company name + CEO if found) to query SearXNG for
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recent news snippets (funding, launches, leadership changes, etc.).
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2. Feed all real data into an LLM prompt to synthesise a structured brief
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covering company overview, leadership, recent developments, and talking
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points tailored to Alex.
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points tailored to the candidate.
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Falls back to pure LLM knowledge when SearXNG is offline.
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@ -24,25 +24,32 @@ from types import SimpleNamespace
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sys.path.insert(0, str(Path(__file__).parent.parent))
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# ── SearXNG scraper integration ───────────────────────────────────────────────
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_SCRAPER_DIR = Path("/Library/Development/scrapers")
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_SCRAPER_AVAILABLE = False
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from scripts.user_profile import UserProfile
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_USER_YAML = Path(__file__).parent.parent / "config" / "user.yaml"
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_profile = UserProfile(_USER_YAML) if UserProfile.exists(_USER_YAML) else None
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if _SCRAPER_DIR.exists():
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sys.path.insert(0, str(_SCRAPER_DIR))
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# ── SearXNG scraper integration ───────────────────────────────────────────────
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# companyScraper is bundled into the Docker image at /app/scrapers/
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_SCRAPER_AVAILABLE = False
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for _scraper_candidate in [
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Path("/app/scrapers"), # Docker container path
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Path(__file__).parent.parent / "scrapers", # local dev fallback
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]:
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if _scraper_candidate.exists():
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sys.path.insert(0, str(_scraper_candidate))
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try:
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from companyScraper import EnhancedCompanyScraper, Config as _ScraperConfig
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_SCRAPER_AVAILABLE = True
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except (ImportError, SystemExit):
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# companyScraper calls sys.exit(1) if bs4/fake-useragent aren't installed
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pass
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break
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def _searxng_running() -> bool:
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def _searxng_running(searxng_url: str = "http://localhost:8888") -> bool:
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"""Quick check whether SearXNG is reachable."""
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try:
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import requests
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r = requests.get("http://localhost:8888/", timeout=3)
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r = requests.get(f"{searxng_url}/", timeout=3)
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return r.status_code == 200
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except Exception:
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return False
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@ -186,9 +193,13 @@ def _parse_sections(text: str) -> dict[str, str]:
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_RESUME_YAML = Path(__file__).parent.parent / "aihawk" / "data_folder" / "plain_text_resume.yaml"
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_KEYWORDS_YAML = Path(__file__).parent.parent / "config" / "resume_keywords.yaml"
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# Companies where Alex has an NDA — reference as generic label unless
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# the role is security-focused (score >= 3 matching JD keywords).
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_NDA_COMPANIES = {"upguard"}
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def _company_label(exp: dict) -> str:
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company = exp.get("company", "")
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score = exp.get("score", 0)
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if _profile:
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return _profile.nda_label(company, score)
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return company
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def _score_experiences(experiences: list[dict], keywords: list[str], jd: str) -> list[dict]:
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@ -214,8 +225,7 @@ def _build_resume_context(resume: dict, keywords: list[str], jd: str) -> str:
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"""
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Build the resume section of the LLM context block.
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Top 2 scored experiences included in full detail; rest as one-liners.
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Applies UpGuard NDA rule: reference as 'enterprise security vendor (NDA)'
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unless the role is security-focused (score >= 3).
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NDA companies are masked via UserProfile.nda_label() when score < threshold.
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"""
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experiences = resume.get("experience_details", [])
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if not experiences:
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@ -225,11 +235,7 @@ def _build_resume_context(resume: dict, keywords: list[str], jd: str) -> str:
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top2 = scored[:2]
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rest = scored[2:]
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def _company_label(exp: dict) -> str:
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company = exp.get("company", "")
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if company.lower() in _NDA_COMPANIES and exp.get("score", 0) < 3:
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return "enterprise security vendor (NDA)"
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return company
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candidate = _profile.name if _profile else "the candidate"
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def _exp_header(exp: dict) -> str:
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return f"{exp.get('position', '')} @ {_company_label(exp)} ({exp.get('employment_period', '')})"
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@ -238,14 +244,14 @@ def _build_resume_context(resume: dict, keywords: list[str], jd: str) -> str:
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bullets = [v for resp in exp.get("key_responsibilities", []) for v in resp.values()]
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return "\n".join(f" - {b}" for b in bullets)
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lines = ["## Alex's Matched Experience"]
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lines = [f"## {candidate}'s Matched Experience"]
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for exp in top2:
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lines.append(f"\n**{_exp_header(exp)}** (match score: {exp['score']})")
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lines.append(_exp_bullets(exp))
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if rest:
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condensed = ", ".join(_exp_header(e) for e in rest)
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lines.append(f"\nAlso in Alex's background: {condensed}")
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lines.append(f"\nAlso in {candidate}'s background: {condensed}")
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return "\n".join(lines)
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@ -359,7 +365,10 @@ def research_company(job: dict, use_scraper: bool = True, on_stage=None) -> dict
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# ── Phase 2: LLM synthesis ────────────────────────────────────────────────
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_stage("Generating brief with LLM… (30–90 seconds)")
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prompt = f"""You are preparing Alex Rivera for a job interview.
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name = _profile.name if _profile else "the candidate"
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career_summary = _profile.career_summary if _profile else ""
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prompt = f"""You are preparing {name} for a job interview.
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{f"Candidate background: {career_summary}" if career_summary else ""}
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Role: **{title}** at **{company}**
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@ -404,12 +413,12 @@ Assess {company}'s commitment to disability inclusion and accessibility. Cover:
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- Any public disability/accessibility advocacy, partnerships, or certifications
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- Glassdoor or press signals about how employees with disabilities experience the company
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If no specific signals are found, say so clearly — absence of public commitment is itself signal.
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This section is for Alex's personal decision-making only and will not appear in any application.
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This section is for the candidate's personal decision-making only and will not appear in any application.
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## Talking Points for Alex
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## Talking Points for {name}
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Five specific talking points for the phone screen. Each must:
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- Reference a concrete experience from Alex's matched background by name
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(UpGuard NDA rule: say "enterprise security vendor" unless the role has a clear security/compliance focus)
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- Reference a concrete experience from {name}'s matched background by name
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(NDA rule: use the masked label shown in the matched experience section for any NDA-protected employer)
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- Connect to a specific signal from the JD or company context above
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- Be 1–2 sentences, ready to speak aloud
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- Never give generic advice
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@ -432,7 +441,7 @@ Five specific talking points for the phone screen. Each must:
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"competitors_brief": sections.get("Funding & Market Position", ""), # competitor landscape is in the funding section
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"red_flags": sections.get("Red Flags & Watch-outs", ""),
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"accessibility_brief": sections.get("Inclusion & Accessibility", ""),
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"talking_points": sections.get("Talking Points for Alex", ""),
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"talking_points": sections.get(f"Talking Points for {name}", ""),
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"scrape_used": scrape_used,
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}
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@ -1,7 +1,7 @@
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#!/usr/bin/env python3
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# scripts/finetune_local.py
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"""
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Local LoRA fine-tune on Alex's cover letter corpus.
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Local LoRA fine-tune on the candidate's cover letter corpus.
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No HuggingFace account or internet required after the base model is cached.
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Usage:
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@ -17,24 +17,32 @@ import os
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import sys
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).parent.parent))
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# Limit CUDA to GPU 0. device_map={"":0} in FastLanguageModel.from_pretrained
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# pins every layer to GPU 0, avoiding the accelerate None-device bug that
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# occurs with device_map="auto" on multi-GPU machines with 4-bit quantisation.
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# Do NOT set WORLD_SIZE/RANK — that triggers torch.distributed initialisation.
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os.environ.setdefault("CUDA_VISIBLE_DEVICES", "0")
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from scripts.user_profile import UserProfile
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_USER_YAML = Path(__file__).parent.parent / "config" / "user.yaml"
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_profile = UserProfile(_USER_YAML) if UserProfile.exists(_USER_YAML) else None
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# ── Config ────────────────────────────────────────────────────────────────────
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DEFAULT_MODEL = "unsloth/Llama-3.2-3B-Instruct" # safe on 8 GB VRAM
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LETTERS_JSONL = Path("/Library/Documents/JobSearch/training_data/cover_letters.jsonl")
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OUTPUT_DIR = Path("/Library/Documents/JobSearch/training_data/finetune_output")
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GGUF_DIR = Path("/Library/Documents/JobSearch/training_data/gguf")
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OLLAMA_NAME = "alex-cover-writer"
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_docs = _profile.docs_dir if _profile else Path.home() / "Documents" / "JobSearch"
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LETTERS_JSONL = _docs / "training_data" / "cover_letters.jsonl"
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OUTPUT_DIR = _docs / "training_data" / "finetune_output"
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GGUF_DIR = _docs / "training_data" / "gguf"
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OLLAMA_NAME = f"{_profile.name.split()[0].lower()}-cover-writer" if _profile else "cover-writer"
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SYSTEM_PROMPT = (
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"You are Alex Rivera's personal cover letter writer. "
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"Write professional, warm, and results-focused cover letters in Alex's voice. "
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"Draw on her background in customer success, technical account management, "
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"and revenue operations. Be specific and avoid generic filler."
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f"You are {_profile.name}'s personal cover letter writer. "
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f"{_profile.career_summary}"
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if _profile else
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"You are a professional cover letter writer. Write in first person."
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)
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# ── Args ──────────────────────────────────────────────────────────────────────
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@ -48,7 +56,7 @@ parser.add_argument("--max-length", type=int, default=1024, help="Max token leng
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args = parser.parse_args()
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print(f"\n{'='*60}")
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print(f" Alex Cover Letter Fine-Tuner")
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print(f" Cover Letter Fine-Tuner [{OLLAMA_NAME}]")
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print(f" Base model : {args.model}")
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print(f" Epochs : {args.epochs}")
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print(f" LoRA rank : {args.rank}")
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@ -1,6 +1,6 @@
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# scripts/generate_cover_letter.py
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"""
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Generate a cover letter in Alex's voice using few-shot examples from her corpus.
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Generate a cover letter in the candidate's voice using few-shot examples from their corpus.
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Usage:
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conda run -n job-seeker python scripts/generate_cover_letter.py \
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@ -16,30 +16,21 @@ import re
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import sys
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from pathlib import Path
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LETTERS_DIR = Path("/Library/Documents/JobSearch")
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from scripts.user_profile import UserProfile
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_USER_YAML = Path(__file__).parent.parent / "config" / "user.yaml"
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_profile = UserProfile(_USER_YAML) if UserProfile.exists(_USER_YAML) else None
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LETTERS_DIR = _profile.docs_dir if _profile else Path.home() / "Documents" / "JobSearch"
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LETTER_GLOB = "*Cover Letter*.md"
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# Background injected into every prompt so the model has Alex's facts
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SYSTEM_CONTEXT = """You are writing cover letters for Alex Rivera, a customer success leader.
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Background:
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- 6+ years in customer success, technical account management, and CS leadership
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- Most recent role: led Americas Customer Success at UpGuard (cybersecurity SaaS), managing enterprise + Fortune 500 accounts, drove NPS consistently above 95
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- Also founder of M3 Consulting, a CS advisory practice for SaaS startups
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- Attended Texas State (2 yrs), CSU East Bay (1 yr); completed degree elsewhere
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- Based in San Francisco Bay Area; open to remote/hybrid
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- Pronouns: any
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Voice guidelines:
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- Warm, confident, and specific — never generic
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- Opens with "I'm delighted/thrilled to apply for [role] at [company]."
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- 3–4 focused paragraphs, ~250–350 words total
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- Para 2: concrete experience (cite UpGuard and/or M3 Consulting with a specific metric)
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- Para 3: genuine connection to THIS company's mission/product
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- Closes with "Thank you for considering my application." + warm sign-off
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- Never use: "I am writing to express my interest", "passionate about making a difference",
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"I look forward to hearing from you", or any hollow filler phrases
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"""
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# Background injected into every prompt so the model has the candidate's facts
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SYSTEM_CONTEXT = (
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f"You are writing cover letters for {_profile.name}. {_profile.career_summary}"
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if _profile else
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"You are a professional cover letter writer. Write in first person."
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)
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# ── Mission-alignment detection ───────────────────────────────────────────────
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@ -69,21 +60,23 @@ _MISSION_SIGNALS: dict[str, list[str]] = {
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],
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}
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_candidate = _profile.name if _profile else "the candidate"
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_MISSION_NOTES: dict[str, str] = {
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"music": (
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"This company is in the music industry, which is one of Alex's genuinely "
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"ideal work environments — she has a real personal passion for the music scene. "
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f"This company is in the music industry, which is one of {_candidate}'s genuinely "
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"ideal work environments — they have a real personal passion for the music scene. "
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"Para 3 should warmly and specifically reflect this authentic alignment, not as "
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"a generic fan statement, but as an honest statement of where she'd love to apply "
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"her CS skills."
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"a generic fan statement, but as an honest statement of where they'd love to apply "
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"their CS skills."
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),
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"animal_welfare": (
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"This organization works in animal welfare/rescue — one of Alex's dream-job "
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f"This organization works in animal welfare/rescue — one of {_candidate}'s dream-job "
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"domains and a genuine personal passion. Para 3 should reflect this authentic "
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"connection warmly and specifically, tying her CS skills to this mission."
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"connection warmly and specifically, tying their CS skills to this mission."
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),
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"education": (
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"This company works in children's education or EdTech — one of Alex's ideal "
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f"This company works in children's education or EdTech — one of {_candidate}'s ideal "
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"work domains, reflecting genuine personal values around learning and young people. "
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"Para 3 should reflect this authentic connection specifically and warmly."
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),
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@ -138,7 +131,7 @@ def build_prompt(
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) -> str:
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parts = [SYSTEM_CONTEXT.strip(), ""]
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if examples:
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parts.append("=== STYLE EXAMPLES (Alex's past letters) ===\n")
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parts.append(f"=== STYLE EXAMPLES ({_candidate}'s past letters) ===\n")
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for i, ex in enumerate(examples, 1):
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parts.append(f"--- Example {i} ({ex['company']}) ---")
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parts.append(ex["text"])
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@ -183,7 +176,7 @@ def generate(title: str, company: str, description: str = "", _router=None) -> s
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def main() -> None:
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parser = argparse.ArgumentParser(description="Generate a cover letter in Alex's voice")
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parser = argparse.ArgumentParser(description=f"Generate a cover letter in {_candidate}'s voice")
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parser.add_argument("--title", help="Job title")
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parser.add_argument("--company", help="Company name")
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parser.add_argument("--description", default="", help="Job description text")
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@ -18,8 +18,22 @@ import yaml
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from bs4 import BeautifulSoup
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from notion_client import Client
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from scripts.user_profile import UserProfile
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_USER_YAML = Path(__file__).parent.parent / "config" / "user.yaml"
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_profile = UserProfile(_USER_YAML) if UserProfile.exists(_USER_YAML) else None
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CONFIG_DIR = Path(__file__).parent.parent / "config"
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RESUME_PATH = Path("/Library/Documents/JobSearch/Alex_Rivera_Resume_02-19-2025.pdf")
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def _find_resume(docs_dir: Path) -> Path | None:
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"""Find the most recently modified PDF in docs_dir matching *resume* or *cv*."""
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candidates = list(docs_dir.glob("*[Rr]esume*.pdf")) + list(docs_dir.glob("*[Cc][Vv]*.pdf"))
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return max(candidates, key=lambda p: p.stat().st_mtime) if candidates else None
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RESUME_PATH = (
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_find_resume(_profile.docs_dir) if _profile else None
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) or Path(__file__).parent.parent / "config" / "resume.pdf"
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def load_notion() -> tuple[Client, dict]:
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# scripts/prepare_training_data.py
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"""
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Extract training pairs from Alex's cover letter corpus for LoRA fine-tuning.
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Extract training pairs from the candidate's cover letter corpus for LoRA fine-tuning.
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Outputs a JSONL file where each line is:
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{"instruction": "Write a cover letter for the [role] position at [company].",
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@ -16,10 +16,17 @@ import re
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import sys
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from pathlib import Path
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LETTERS_DIR = Path("/Library/Documents/JobSearch")
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from scripts.user_profile import UserProfile
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_USER_YAML = Path(__file__).parent.parent / "config" / "user.yaml"
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_profile = UserProfile(_USER_YAML) if UserProfile.exists(_USER_YAML) else None
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_docs = _profile.docs_dir if _profile else Path.home() / "Documents" / "JobSearch"
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LETTERS_DIR = _docs
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# Use two globs to handle mixed capitalisation ("Cover Letter" vs "cover letter")
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LETTER_GLOBS = ["*Cover Letter*.md", "*cover letter*.md"]
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DEFAULT_OUTPUT = LETTERS_DIR / "training_data" / "cover_letters.jsonl"
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DEFAULT_OUTPUT = _docs / "training_data" / "cover_letters.jsonl"
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# Patterns that appear in opening sentences to extract role
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ROLE_PATTERNS = [
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@ -64,16 +64,22 @@ def test_build_resume_context_top2_in_full():
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def test_build_resume_context_rest_condensed():
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"""Remaining experiences appear as condensed one-liners, not full bullets."""
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ctx = _build_resume_context(RESUME, KEYWORDS, JD)
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assert "Also in Alex" in ctx
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assert "Also in" in ctx
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assert "Generic Co" in ctx
|
||||
# Generic Co bullets should NOT appear in full
|
||||
assert "Managed SMB portfolio" not in ctx
|
||||
|
||||
|
||||
def test_upguard_nda_low_score():
|
||||
"""UpGuard name replaced with 'enterprise security vendor' when score < 3."""
|
||||
"""UpGuard NDA rule: company masked when score < 3 and profile has NDA companies configured."""
|
||||
from scripts.company_research import _profile
|
||||
ctx = _build_resume_context(RESUME, ["python", "kubernetes"], "python kubernetes devops")
|
||||
assert "enterprise security vendor" in ctx
|
||||
if _profile and _profile.is_nda("upguard"):
|
||||
# Profile present with UpGuard NDA — company should be masked
|
||||
assert "UpGuard" not in ctx
|
||||
else:
|
||||
# No profile or UpGuard not in NDA list — company name appears directly
|
||||
assert "UpGuard" in ctx or "enterprise security vendor" in ctx or "previous employer" in ctx
|
||||
|
||||
|
||||
def test_load_resume_and_keywords_returns_lists():
|
||||
|
|
|
|||
|
|
@ -89,17 +89,14 @@ def test_find_similar_letters_returns_top_k():
|
|||
|
||||
|
||||
def test_load_corpus_returns_list():
|
||||
"""load_corpus returns a list (may be empty if LETTERS_DIR absent, must not crash)."""
|
||||
"""load_corpus returns a list (empty if LETTERS_DIR absent) without crashing."""
|
||||
from scripts.generate_cover_letter import load_corpus, LETTERS_DIR
|
||||
|
||||
if LETTERS_DIR.exists():
|
||||
corpus = load_corpus()
|
||||
assert isinstance(corpus, list)
|
||||
if corpus:
|
||||
assert "company" in corpus[0]
|
||||
assert "text" in corpus[0]
|
||||
else:
|
||||
pytest.skip("LETTERS_DIR not present in this environment")
|
||||
|
||||
|
||||
def test_generate_calls_llm_router():
|
||||
|
|
|
|||
Loading…
Reference in a new issue