Snapshot: full project state

README.md

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+# Procyon — Job Hunt Workflow Engine
+
+Multi-step job-hunt pipeline with a web GUI, running on a Proxmox LXC (never on the MacBook).
+
+## Pipeline
+1. **Discover** — pull jobs from RemoteOK + Indeed RSS (+ optional extra feeds), store in SQLite.
+2. **Score** — fit score (0–1) + a one-line "why" opinion, LLM-first, keyword fallback.
+3. **Tailor** — clone the master resume and reorder/re-emphasize for the role (honest-only:
+   no fabricated employers/dates/skills). Draft a tailored cover email.
+4. **Self-Audit** — automatic checklist (employer name, no placeholders, contact info, subject,
+   resume targets the job). Every item must PASS.
+5. **Approve (human gate)** — you review and click Approve. Nothing sends without this.
+6. **Send** — SMTP email with tailored resume attached. Send is BLOCKED unless approved AND
+   the audit passed AND SMTP is configured.
+7. **Track** — log outcomes (response/interview/offer) and surface response-rate insights.
+
+## Design rules
+- Inference runs on the LAN Ollama host (`llm_base_url`), NEVER on this CT (drjones rule).
+- Graceful degradation: if the model is saturated, scoring/tailoring falls back to
+  deterministic keyword matching — the pipeline never blocks on inference.
+- Honest resumes only. The LLM system prompt forbids fabricating credentials.
+- Optional outbound proxy (his own Nord proxy CT) for discovery fetches — not an evasion rig.
+
+## Setup
+- `systemctl restart procyon` after deploy.
+- Configure SMTP (iCloud app-specific password) in Settings before sending.
+
+## Files
+- `app.py` — Flask routes + pipeline orchestration
+- `db.py` — SQLite schema + helpers
+- `scraper.py` — discovery (RSS/JSON + proxy)
+- `llm.py` — Ollama client + keyword fallback
+- `tailor.py` — scoring + resume tailoring + email drafting
+- `audit.py` — pre-send self-audit
+- `emailer.py` — SMTP send
+- `improve.py` — outcome tracking + insights