README.md

---
title: JAA ATS Tool
emoji: 🤖
colorFrom: blue
colorTo: indigo
sdk: docker
app_port: 7860
pinned: false
license: mit
---

# Job Automation Agent — PM Edition

Automated Product Manager job search, AI-powered assessment, ATS-optimized resume generation, and Google Sheets reporting — all in one pipeline.

---

## What It Does

| Step | What Happens |
|------|--------------|
| 1 | Parses your PDF resume (Kimi-K2.6, ~5s) |
| 2 | Scrapes PM-only jobs from **LinkedIn, Indeed, Glassdoor, Remotive, WeWorkRemotely** — ALL selected roles × locations, last N days |
| 3 | Filters non-PM roles at scrape time; dedup by URL + (title, company) + 30-day SQLite history; platform-level total cap |
| 4 | Fetches FULL job descriptions in bulk (parallel HTTP / single browser session) |
| 5 | Assesses ALL jobs using 7 parallel AI models (10-model pool via NVIDIA API) |
| 6 | Generates ATS-optimized resumes for ALL jobs — DOCX **and PDF**, After ≥ Before guaranteed, target 95% |
| 7 | Writes everything to your Google Sheet with direct job links |
| 8 | Saves local Excel report + resumes in `data/output/resumes/YYYY-MM-DD/` |
| 9 | Saves run to history — reload past runs in one click from the History panel |

---

## Quick Start

```powershell
# 1. Install dependencies
pip install -r requirements.txt
playwright install chromium

# 2. Copy and fill in your API keys
copy .env.example .env
# Edit .env with your NVIDIA_API_KEY, GOOGLE_SHEET_ID, etc.

# 3. Place your resume PDF
# Copy your resume to: data/resume/resume.pdf

# 4. Connect Google (one-time browser login)
python connect_google.py

# 5. Run a test (5 jobs)
# In config.py: ASSESSMENT["test_mode"] = True
python main.py

# 6. Run full production mode (all 100+ PM jobs)
# In config.py: ASSESSMENT["test_mode"] = False
python main.py

# 7. Or use the Streamlit UI
streamlit run ui.py
```

---

## ever-jobs Integration (160+ Platforms)

This project integrates the [ever-jobs](https://github.com/ever-jobs/ever-jobs) NestJS service, which provides REST API access to 160+ job board scrapers.

### Prerequisites

- **Docker Desktop** (preferred): [Install Docker Desktop](https://docs.docker.com/desktop/install/windows-install/)
- OR **Node.js 24.x** for npm subprocess fallback

### Setup (one-time)

```bash
# Clone ever-jobs to vendor/ directory (done automatically during setup)
git clone https://github.com/ever-jobs/ever-jobs.git vendor/ever-jobs --depth=1

# Start via Docker (preferred)
cd vendor/ever-jobs && docker compose up -d

# Verify API is running
curl http://localhost:3001/health
# OR from Python:
python -c "from src.ever_jobs_bridge.server import is_running; print(is_running())"
```

### Automatic Startup

The pipeline calls `ensure_running()` automatically before any ever-jobs platforms are scraped. It tries Docker first, falls back to `npm run start` if Docker is unavailable.

### Platform Selection

The UI has a single **"🌐 Job Platforms"** section with three groups. Selecting LinkedIn, Indeed, Glassdoor, Remotive, WeWorkRemotely, or Naukri uses their dedicated high-quality scrapers; all other platforms go through the ever-jobs REST API.

| Group | Count | Description |
|-------|-------|-------------|
| Search Boards | 94 | General job boards. India-relevant defaults pre-selected. |
| ATS Platforms | 37 | Greenhouse, Lever, Workday etc. — companies post jobs here. NOT resume scoring. |
| Company Pages | 39 | Direct career pages (Flipkart, Swiggy, Amazon, Google, etc.) |

**India default platforms:** linkedin, naukri, indeed, glassdoor, google, bdjobs, internshala, bayt, iimjobs, foundit

### ATS Clarification

> **Important:** "ATS" in ever-jobs means Applicant Tracking System **PLATFORMS**
> (tools companies use to post jobs, like Greenhouse or Lever).
> It does **NOT** mean ATS resume scoring.
>
> Our ATS resume scoring (`src/ats_scorer.py`) uses a 70% JD keyword match +
> 30% resume quality hybrid and is **NOT changed** by this integration.

### Performance Notes

| Selection | Expected Time |
|-----------|---------------|
| Default 10 platforms | ~1–3 minutes |
| 30 platforms | ~3–5 minutes |
| 100+ platforms | 5–10 minutes (warning shown in UI) |

Playwright-based scrapers (some company pages) are inherently slower than API-based boards.

### Cross-Platform Deduplication

In addition to URL-based dedup, a **content fingerprint** (SHA-256 of normalized title+company) catches cross-platform duplicates. For example, the same "Product Manager at Google" posting on LinkedIn AND Greenhouse (different URLs) is detected and deduplicated.

---

## Project Structure

```
Job Automation Agent/
├── main.py                    # Main pipeline (6-step orchestrator)
├── config.py                  # All configuration — models, platforms, ATS settings
├── ui.py                      # Streamlit UI (4 tabs: Search, Results, Job Details, Research)
├── connect_google.py          # One-time Google OAuth setup
├── setup_google.py            # Service account alternative
├── requirements.txt
├── .env                       # API keys (never commit)
├── .env.example               # Template for .env
│
├── src/
│   ├── resume_parser.py       # PDF → plain text (pdfplumber)
│   ├── llm_client.py          # GLM 5.1 wrapper (profile extract, resume customize)
│   ├── model_pool.py          # 10-model parallel AI pool (NVIDIA API)
│   ├── job_assessor.py        # Phase 1 keyword scoring + Phase 2 LLM assessment
│   ├── job_history.py         # SQLite dedup store (data/job_history.db)
│   ├── ats_scorer.py          # Hybrid ATS scoring (70% JD match + 30% quality)
│   ├── resume_customizer.py   # LLM-tailored DOCX resume generator
│   ├── gsheets.py             # Google Sheets + Drive upload
│   ├── excel_reporter.py      # Local Excel report
│   ├── odysseus_llm_core.py   # Deep research LLM core
│   │
│   ├── ever_jobs_bridge/       # ever-jobs NestJS REST API adapter
│   │   ├── __init__.py        # Package init
│   │   ├── server.py          # Docker/npm server lifecycle (start/stop/health)
│   │   ├── client.py          # HTTP client for POST /api/jobs/search
│   │   ├── mapper.py          # IJob JSON → Job dataclass mapper
│   │   └── platforms.py       # 170 platform catalog with group metadata
│   │
│   ├── scrapers/
│   │   ├── base.py            # Job dataclass + BaseScraper + is_pm_role() filter
│   │   ├── linkedin.py        # LinkedIn scraper (requests + BeautifulSoup)
│   │   ├── indeed.py          # Indeed scraper (Playwright for JS rendering)
│   │   ├── glassdoor.py       # Glassdoor scraper (Playwright)
│   │   ├── naukri.py          # Naukri (disabled — blocked by Akamai)
│   │   └── ever_jobs.py       # EverJobsScraper (REST adapter for 160+ platforms)
│   │
│   └── research/
│       ├── deep_researcher.py # Odysseus IterResearch engine (Think→Search→Extract→Synthesize)
│       └── search.py          # DuckDuckGo + Bing fallback, 12h cache
│
└── data/
    ├── resume/resume.pdf      # Your resume (add this)
    ├── job_history.db         # Dedup SQLite DB (auto-created)
    ├── research_cache/        # 12h DuckDuckGo result cache
    └── output/
        ├── resumes/           # Generated DOCX resumes (Company_JobTitle.docx)
        └── reports/           # Excel reports
```

---

## Configuration (`config.py`)

### AI Models (10-model pool via NVIDIA API)

| Model | Speed | Phase 2 | Notes |
|-------|-------|---------|-------|
| Kimi-K2.6 | ~5s/batch | ✅ | Fastest, handles most work |
| Step-3.7-Flash | ~8-35s | ✅ | |
| Qwen3.5-397b | ~9s | ✅ | |
| Qwen3.5-122b-v2 | ~12s | ✅ | |
| GPT-OSS-120b | ~11s | ✅ | |
| Qwen3.5-122b | ~40s | ✅ | |
| DeepSeek-v4-Pro | ~42s | ✅ | |
| DeepSeek-v4-Flash | ~229s | ❌ | Too slow for phase 2 |
| GLM-5.1 | ~234s | ❌ | Used for resume parsing only |
| MiniMax-M2.7 | ~908s | ❌ | Blocked/rate-limited |

### Key Settings

```python
ASSESSMENT = {
    "min_score_for_llm_resume": 6,   # LLM-tailored resume for score >= this
    "generate_all_resumes":     True, # Template resume for ALL PM jobs
    "max_llm_resumes":          30,   # Max LLM resumes per run
    "dedup_days":               30,   # Skip jobs seen in last 30 days
    "test_mode":                True, # ← Set False for full production run
    "test_jobs_limit":          5,    # Max jobs in test mode
}
```

---

## ATS Scoring Method

Hybrid scoring: **70% JD Match + 30% Resume Quality**

- **JD Match (70%)**: Extract keywords FROM the specific job description → match against resume using word-boundary regex (`(?<!\w)kw(?!\w)`) — same approach as Resume-Matcher
- **Resume Quality (30%)**: 6-factor structural analysis (measurable achievements, contact info, education, etc.)
- **Iterative optimization**: Up to 3 LLM attempts to push score up, gap report fed back each iteration
- **Fallback**: `_inject_missing_keywords()` weaves still-missing terms naturally

### Keyword extraction is JD-driven, skills-only, and honest (industry standard)

- Keywords are extracted **from each JD itself** (not a fixed stored list), but
  **skills-only**: a term is kept only if it's a recognised skill/tool/method/
  domain/soft-skill in our gazetteer. Prose nouns (Goals, Authority, Enterprise,
  Productivity…) can never become "keywords" — exactly how real checkers like
  Jobalytics work (they match against a curated skills list, not every word).
- **Dedicated, categorized SKILLS section** (Tools & Analytics / Methodologies /
  Domains / Core Competencies) — the #1 ATS keyword vehicle, placed after the
  summary, parsed and counted by Jobalytics/Resume Worded.
- **Quality over quantity**: cover the JD's real skills (~20–26), each 1–3×,
  across the skills section + summary + woven bullets. Best practice is 15–25
  keywords at 60–80% coverage — NOT 100 stuffed terms.
- **No cap, distributed smartly.** Every meaningful missing keyword is kept —
  woven into relevant bullets first (the ideal, never-penalised place), then any
  remainder spread across several short sentences (each its own paragraph, ≤10
  items). A single long comma-dump is *deliberately not* produced:
  `_strip_keyword_spam` removes any 15+-separator line before scoring, exactly as
  real ATS checkers and recruiters discount stuffing — so distributing keeps them
  all counted without looking like spam.
- **Buzzwords removed.** Vague abstractions (innovation, solutions, tools,
  leadership, leverage, scalable…) are never injected — real checkers like Resume
  Worded penalise them.
- **Honest expectation:** 90%+ is reached on JDs that genuinely fit the
  candidate's background. Out-of-domain JDs score honestly lower — that reflects
  reality (and matches third-party checkers), rather than a faked number.
- The displayed score is a **conservative estimate** (leans low vs our raw
  internal coverage); always verify the final resume on Jobalytics/Simplify.

**Benchmark results** (correctly differentiates — full resume + capable LLM):
- In-domain PM JDs (Airtel, generic PM) → ~83–87
- Out-of-domain JDs (SecOps, lending) → ~60–70, honestly

---

## Telegram Bot (mobile touchpoint, via Cloudflare relay)

Third touchpoint alongside the Streamlit app and Chrome extension: send a **job
link** (or paste the JD) to your Telegram bot and get back a tailored resume PDF.

**Architecture note:** HF Spaces *block outbound traffic to `api.telegram.org`*, so
the HF app can receive webhooks but can't send replies. The bot therefore runs
through a tiny **Cloudflare Worker** relay (free, always-on, can reach Telegram):
Telegram → Worker → HF `/api/generate` (does JD-fetch + tailoring + Tectonic
compile) → Worker sends the PDF back. Worker code + full setup:
[`relay/cloudflare-worker.js`](relay/cloudflare-worker.js) and
[`relay/README.md`](relay/README.md).

`/api/generate` accepts `jd_url` (server fetches/extracts the JD) or `jd_text`, and
falls back to the bundled default resume when no resume is supplied — so the relay
stays a thin Telegram-I/O layer.

(The legacy in-Space `POST /telegram/webhook` + `GET /telegram/diag` remain for
diagnostics, but the working path is the Cloudflare relay because of the egress block.)

## Chrome Extension + JSON API

Apply one job at a time in the browser (no bulk scraping → no IP-blocking). The
HF Space serves BOTH the Streamlit app and a JSON API from one codebase — so the
**score is identical** to the web app (proven by `tests/test_api_parity.py`).

**Setup:** load `extension/` unpacked → Options → **paste your resume LaTeX** (or
upload a PDF) + set API URL (`https://<your-space>.hf.space`) + `API_SECRET_TOKEN`.
Then open any job → **Run** → tailored resume download.

**Resume LaTeX (recommended):** paste your resume's LaTeX source in Options. When
present it is **prioritised over the PDF** — we extract its text for keyword
matching, inject the honestly-includable JD keywords **distributed across a Summary
sentence, Experience bullets, and one compact competencies line** (never a dump),
and compile it to a **clean PDF** (Tectonic on the Space) you download directly
(plus the modified `.tex`). This fixes the generated-PDF design issues because the
layout is your own LaTeX. Anti-faking is identical: certs, seniority, employers,
and specialised hands-on engineering terms are never injected.
Verify: `python scripts/verify_latex_resume.py`.

**Recruiter-grade placement + resilient Run (Phase 7, v1.4.0):** JD extraction now
strips page/extension UI chrome (Simplify/Jobalytics overlays) so junk like "Show
Match Details" / "People Clicked Apply" never becomes a keyword; the DOCX Skills
section is one capped line per category (18-28 items, no repeated "Core
Competencies:" dumps) with surplus keywords woven into Summary + Experience; and
clicking **Run** now survives the popup closing — the background owns the run and
the popup restores the spinner/result on reopen. Verify:
`python scripts/verify_clean_jd_extraction.py`, `verify_skills_distribution.py`,
`verify_resilient_run.py`.

**Trust + usability (Phase 8, v1.5.0):**
- **Download a real PDF, not just DOCX.** The DOCX path now always renders a PDF
  sidecar (reportlab on Linux/HF) so the PDF button works even without LaTeX; the
  LaTeX path compiles a clean PDF via Tectonic and, if compilation fails, tells you
  why (no engine vs LaTeX error) while still offering the `.tex`.
- **Your resume is preserved — non-destructive tailoring is the default.** Role
  titles, companies, dates, and your existing bullets are kept VERBATIM; keywords
  are added only in the Professional Summary and as ≤3 appended lines at the end of
  each experience. Nothing you wrote is renamed or rewritten.
- **Persistent left side panel (Jobalytics-style).** The UI is now a collapsible
  panel docked to the left of the page instead of a popup that vanishes — click
  **Run**, then click anywhere on the page; the run keeps going and stays visible.
- **History.** A History list in the panel shows your previous generations per job
  (title/company/time/scores/status); click any entry to restore it and re-download
  DOCX/PDF/.tex. Verify: `python scripts/verify_pdf_download.py`,
  `verify_non_destructive.py`, `verify_side_panel.py`, `verify_history.py`.

**API endpoints** (`api_server.py`, port 7860, `X-Api-Token` header):
- `GET /api/health` → `{status: ok}`
- `POST /api/generate` — multipart `jd_text` + (`resume_latex` **or** `resume` PDF;
  LaTeX wins) → tailored resume. PDF path returns base64 DOCX/PDF; LaTeX path
  returns compiled `pdf_b64` + `tex_b64` + external coverage report.
- `POST /api/repair-with-feedback` — **External ATS Feedback Repair Mode**: paste
  Jobalytics/Simplify feedback (score + missing keywords); every missing keyword
  is risk-classified by `candidate_fit` and woven *honestly* into bullets/skills
  (or injected into your LaTeX when `resume_latex` is supplied).

**Feedback-repair honesty rules** (never relaxed):
- LOW → auto-weave; MEDIUM → auto-weave + review flag; **HIGH → not added,
  returned as "needs your confirmation"; BLOCKED → excluded.** No fake
  skills/seniority/employers/degrees/certs.
- Scores come from the re-parsed exported file; READY needs internal AND
  independent ≥ 90 AND ATS readability ≥ 90 AND parse validation.

**Statuses:** `READY_95_EXTERNAL_ALIGNED` (≥90 both + pasted gaps mostly resolved)
· `READY_90_PLUS_REVIEW_RECOMMENDED` (medium-risk added) · `NEEDS_USER_INPUT`
(95 needs high-risk terms you must confirm) · `NOT_ELIGIBLE_LOW_FIT` (can't reach
90/95 without fabrication).

Verify: `PYTHONPATH=. python scripts/verify_feedback_repair.py` (deterministic;
proves blocked/high terms never enter the resume).

### Maximum ATS Mode (User-Confirmed Skill Expansion)

For your target role family (Product / Product Manager / AI Product Manager /
SaaS / B2B), the uploaded resume is treated as an **incomplete base profile**.
Enable **Maximum ATS Mode** (extension toggle, on by default; API field
`maximum_ats_mode=1`) to treat normal PM/Product/AI/agile vocabulary — *AI, ML,
generative AI, prompt design, model evaluation, QA, experimentation, data-driven,
analytics, roadmap, product strategy, product ownership, agile, scrum, user
stories, acceptance criteria, PRD, stakeholder management, SaaS, B2B, enterprise
platform, product discovery, go-to-market, retention, growth*, etc. — as
**user-confirmed / interview-supportable** and aggressively place them across
Summary, Skills, Key Achievements and Experience. It targets **95** external ATS
and treats **anything below 90** for in-family roles as a failure state to keep
repairing (unless the only gaps are genuinely blocked).

**Still never fabricated** (hard boundaries, unchanged): degrees, certifications/
licenses, employers, titles, years/seniority, regulated credentials, and
specialized hands-on engineering/security tools (those surface as **"needs your
confirmation"**, with a one-click **Confirm & regenerate**).

- Both endpoints accept `maximum_ats_mode` / `user_confirmed_expansion`,
  `confirmed_terms`, `target_external_score` (default 95). Backward compatible.
- Responses include a rich **coverage report** (per keyword: category, risk,
  disposition, the resume section it was placed in, and why anything was
  excluded), `still_missing_repairable`, and a plain-English `below_target_explanation`.
- Confirmed expansion terms persist to the **Candidate Vault** (`user_confirmed`)
  so future resumes treat them as safe.

**Maximum-ATS statuses:** `READY_MAX_ATS_95_PLUS` (gates pass + external coverage
≥ 95) · `READY_90_PLUS_EXTERNAL_ALIGNED` (gates pass + coverage ≥ 90) ·
`BELOW_TARGET_REPAIRABLE` (below target, remaining gaps LOW/MEDIUM → keep
repairing) · `NEEDS_USER_CONFIRMATION` (only high-risk-but-supportable terms left).

**External-coverage is the success signal (not the internal score).** Our internal
scorer uses a narrow taxonomy (→ 90%+ easily); external checkers extract a broad
40-46 term set (→ can be ~54% on the same resume). So in Maximum ATS Mode the
system builds a broad Jobalytics-style **expected** set (`src/external_ats.py`),
**physically guarantees** every *includable* term into the exported DOCX (Skills
verbatim + woven into Experience bullets; HIGH/BLOCKED stay gated), re-parses the
file, and **measures coverage from the export**. Status is then `READY_MAX_ATS_95_PLUS`
(≥95) / `READY_90_PLUS_EXTERNAL_ALIGNED` (≥90) / `BELOW_TARGET_REPAIRABLE` — an
internal-96 / external-54 result is treated as a bug, never "done". Both endpoints
return `external_coverage` + a per-term `coverage_report` (keyword / found in export
/ section / why-missing) and log `maximum_ats_mode`.

Verify (deterministic, no keys):
- `PYTHONPATH=. python scripts/verify_maximum_ats.py` — PM/AI terms become
  user-confirmed; certs/seniority/engineering stay gated; coverage improves.
- `PYTHONPATH=. python scripts/verify_max_ats_coverage.py` — the 26/46 live-failure
  regression: exported DOCX covers ≥90% of includable PM terms; credentials/fake
  seniority excluded; status driven by external coverage.

---

## Generation Modes (V1 / V2)

Two resume tailoring modes, selectable per surface:

### V1 — Structured Keyword Placement (default)

Uncapped JD keyword extraction + ordered placement into the hardcoded resume
(Phase 9). Keywords are appended as comma-separated `\resumeItem` lines in a
waterfall order: Summary 15-20, BYJU's PSM 25-30, PS 25-30, ML Edutech 8-12,
Skills Other 15-20, Projects, NxtWave. Fast (no LLM call for placement);
keywords appear verbatim.

### V2 — Natural AI Sentence Integration

Same keyword extraction + waterfall allocation as V1, but an LLM (Kimi-K2.6 by
default, ~5s) generates **natural sentences** that weave the keywords into
genuine-sounding experience bullets. Reads as natural prose rather than keyword
lists; honesty boundaries preserved; slightly slower (one LLM call + compile).
Falls back to V1 comma placement if the LLM call fails.

### Selecting a version

| Surface | How to choose |
|---------|---------------|
| `/api/generate` | `version=v1` or `version=v2` form field; default from `GEN_VERSION_DEFAULT` env (v1) |
| HF Streamlit | V1/V2 radio on the home page; bulk pipeline honors the selection |
| Chrome extension | Default in Options (`gen_version_default`) + per-run toggle in the popup |
| Telegram bot | `/v1`, `/v2` commands set per-user default; `/mode` shows current |

### Env knobs

| Variable | Default | Description |
|----------|---------|-------------|
| `GEN_VERSION_DEFAULT` | `v1` | Default version when not specified |
| `V2_JUDGE_MODEL` | `Kimi-K2.6` | Model used for V2 sentence generation |
| `V2_FAN_OUT_MODELS` | `Kimi-K2.6,Qwen3.5-397b,GPT-OSS-120b` | Models for multi-model fan-out (when explicitly configured) |

---

## Google Sheet Columns

| Column | Description |
|--------|-------------|
| Batch Date | When the run happened |
| Rank | Score rank within this batch |
| Job Title / Company / Location | Job details |
| Platform | LinkedIn / Indeed / Glassdoor |
| Relevance Score | AI assessment (1–10) |
| ATS Before (%) | ATS score on original resume |
| ATS After (%) | ATS score on tailored resume |
| ATS Improvement | After − Before |
| Resume Quality | Structural quality score |
| Priority | High / Medium / Low |
| Matching / Missing Skills | Gap analysis |
| AI Recommendation | LLM reasoning |
| Apply Link | Direct job URL (clickable) |
| Resume Link | Google Drive link to tailored resume |
| Application Status | Dropdown: Not Applied → Offer |

---

## Environment Variables (`.env`)

```
NVIDIA_API_KEY=nvapi-...          # GLM 5.1 + primary key
NVIDIA_API_KEY_2=nvapi-...        # DeepSeek-v4-Pro, MiniMax
NVIDIA_API_KEY_3=nvapi-...        # Kimi-K2.6
NVIDIA_API_KEY_4=nvapi-...        # Qwen3.5-122b
NVIDIA_API_KEY_5=nvapi-...        # GPT-OSS-120b
NVIDIA_API_KEY_6=nvapi-...        # DeepSeek-v4-Flash
NVIDIA_API_KEY_7=nvapi-...        # Qwen3.5-397b, Qwen3.5-122b-v2
NVIDIA_API_KEY_8=nvapi-...        # Step-3.7-Flash
GOOGLE_SHEET_ID=1Ehxt3eo...       # Your Google Sheet ID
RESUME_PATH=data/resume/resume.pdf
```

---

## Logging & Debugging

Every pipeline run writes a timestamped log to `data/logs/run_YYYY-MM-DD_HH-MM-SS.log`.

To diagnose failures:
1. Run a search from the UI
2. Switch to the **📋 Logs** tab
3. Errors show in red, warnings in yellow
4. Use **Download Full Log File** to share or inspect offline
5. Previous runs are also listed in the selector

The log captures:
- Every scrape attempt (role, location, raw result count)
- Full Python tracebacks on any exception
- All `print()` output from scrapers and LLM clients
- Playwright browser output

---

## Known Issues / Pending

| Issue | Status | Notes |
|-------|--------|-------|
| Google Drive upload `'Client' object has no attribute 'auth'` | Pending fix | gspread auth method mismatch |
| LLM resumes = 0 (GLM timeout during customization) | Pending fix | Switch to Kimi/Step for resume generation |
| Naukri blocked by Akamai | Permanent skip | Returns 406 / "Access Denied" with Playwright |
| Google OAuth "Access blocked" | Fixed | Add email as test user at GCP console |

---

## Running the UI

```powershell
streamlit run ui.py
# Opens at http://localhost:8501

# Tabs:
# 1. Search     — configure and run the full pipeline
# 2. Results    — view all assessed jobs with scores
# 3. Job Details — expand any job for full AI breakdown
# 4. Deep Research — Odysseus engine to research companies
```