resume-screen

name

resume-screen

description

Simulate the automated resume-screening gate for the 2026Fall job board: for a given job (or several), judge whether the owner's resume would clear the machine screen, against BOTH resume profiles (backend-infra and ai-mlops), with a score + verdict badge each, and write the result to screenings/screenings.json (joined to results/ by job_id). USE THIS when the user says things like "screen my resume", "would I pass the ATS for this job", "check my resume against this role", "run resume screening", "score my resume vs these jobs", or "would this get filtered out". Separate from newgrad-job-refresh (which only fetches jobs). Trigger whenever the user wants to know if a posting would auto-reject them.

Resume Screening Simulator (B fidelity: knockouts + content match)

Simulates the automated screening stage — the keyword/requirements filter and application-form knockouts that auto-reject a candidate before a human reads the resume. Runs per job, against both resume profiles, and writes scores + verdict badges that the board overlays on each job.

Owner is an international new grad — graduates 2026-12-08, needs visa sponsorship, no US citizenship/clearance. That makes work-authorization the single biggest real knockout, so weight it heavily.

Preconditions

• Working directory = project root /Users/kangkang/kypkk/2026Fall.
• Resume profiles present in resume/, as .tex or .pdf (read .tex when both exist — it's cleaner to parse; otherwise read the .pdf directly):
  • resume/backend-infra.tex or .pdf — profile key backend-infra (Backend/Infra)
  • resume/ai-mlops.tex or .pdf — profile key ai-mlops (AI Eng / MLOps) If a profile has neither file, say so and screen only the one(s) present.

Inputs per job

• The job from results/{slug}.json (has title, apply_url, qualifications, sponsorship_note, locations, job_id).
• Fetch the full JD from apply_url (the stored fields are thin — you need the real requirements). Use WebFetch or the browser.

How to screen one job × one profile

Apply these in order. Knockouts first — a single hard fail makes the verdict knockout regardless of content.

1. Knockout filters (hard auto-reject → badge knockout)

• Work authorization / sponsorship: JD says "US citizenship required", "must be a US person", "active clearance", "no sponsorship / will not sponsor" → knockout (owner needs sponsorship). This is the most important check.
• Location: role is non-US / on-site in a country the owner can't work in → knockout.
• Graduation timing: required start date or grad window the owner (Dec 2026) can't meet → knockout.
• Degree: requires a degree the owner lacks (e.g., PhD-required) → knockout.
• Experience: clearly wants senior/N+ years (not a new-grad role) → knockout.

2. Content / keyword match (if no knockout) → score 0–100

• Extract the JD's required + preferred skills, technologies, and qualifications.
• Compare against the resume profile's content. Weight must-haves heavily; preferred items lightly.
• score = how well the resume covers what an ATS/keyword filter would look for.
• Record missing (key required items absent from the resume) and fixes (concrete, honest resume edits — only things the owner could truthfully add/surface, never fabricated experience).

3. Verdict badge

• pass — score ≥ 75 and no knockout.
• borderline — score 50–74 and no knockout.
• knockout — any hard knockout, or score < 50.

Output

For each screened job, write one Markdown report per profile to screenings/reports/{job_id}-{profile}.md (verdict, score, knockouts, missing, fixes, the JD requirements you extracted), then merge the structured result — never hand-edit the JSON:

python3 scripts/save_screening.py entry.json

entry.json:

{ "job_id":"amazon-smpl-bfa6…", "company":"Amazon",
  "title":"Software Development Engineer",
  "profiles":{
    "backend-infra":{"badge":"pass","score":82,"knockouts":[],
      "missing":["Kubernetes"],"fixes":["Surface your k8s coursework project"],
      "report":"screenings/reports/amazon-smpl-bfa6…-backend-infra.md"},
    "ai-mlops":{"badge":"knockout","score":40,
      "knockouts":["US citizenship required"],"missing":["PyTorch"],"fixes":[],
      "report":"screenings/reports/amazon-smpl-bfa6…-ai-mlops.md"}
  }}

scripts/save_screening.py keys it by job_id (joins to results/), stamps screened_at, and merges without disturbing other entries. The board reads screenings/screenings.json and shows the two badges (BE / ML) on each job.

Choosing scope

Ask or infer what to screen: a single job the user names, all jobs at one company (results/{slug}.json), or a batch (e.g. all not-yet-screened jobs, or all Tier 1–2). Screening fetches a JD per job and uses model reasoning, so for big batches prefer the most relevant subset first and tell the user what you covered / deferred.

Each job has a screened boolean in results/{slug}.json — false until it's been screened. save_screening.py flips it to true automatically. To screen only the backlog, list the unscreened jobs:

python3 -c "import json,glob; [print(d['slug'],j['job_id'],j['title']) for f in glob.glob('results/*.json') for d in [json.load(open(f))] for j in d['jobs'] if not j.get('screened')]"

Invariants

• Screen both profiles for every job (unless a profile file is missing).
• Base every judgment on the actual fetched JD and the actual resume — never invent requirements or experience. fixes must be truthful surfacing, not lies.
• job_id must match the one in results/ exactly (that's the join key).
• Idempotent: re-screening a job overwrites its entry.

Later (phase D, not now)

A Python + Claude-API batch scorer (screen_resume.py) to score every job in results/ unattended, and PDF parse-ability checks (C fidelity) using the .pdf.