Auto-Scrape and Score LinkedIn Jobs with Claude + Apify

Input resume and preferences → Claude scrapes LinkedIn via Apify connector → deduplicates, filters, and outputs a scored spreadsheet with a priority apply list

Who

Job seekers who need to batch-screen LinkedIn listings, especially those tracking visa sponsorship and salary floors

You get

One run narrows ~700 scraped listings down to 10 high-priority apply targets, with a scored spreadsheet

You need

Apify

Steps

1. Upload resume and summarize background

   Give Claude your resume text and have it summarize your stack, experience level, and seniority in exactly 3 lines.

   Execution prompt · Source text。Claude conversation input

   Copy prompt

   STEP 1 — Resume
   Summarize my stack, experience and level in exactly 3 lines.

2. Answer preference questions and wait

   Claude will ask: dream job title(s), preferred US location (remote/hybrid/on-site + cities), seniority level, whether you need visa sponsorship, and minimum base salary in USD. Answer, then wait. Do not let it proceed until you confirm.

   Execution prompt · Source text。Claude conversation input

   Copy prompt

   STEP 2 — Interview (then STOP and wait for my answers)
   Ask me: dream job title(s), preferred US location (remote/hybrid/on-site + cities),
   seniority level, whether I need visa sponsorship, and minimum base salary in USD.

3. Scrape and filter jobs via Apify

   Claude uses the Apify connector to run a LinkedIn Jobs scraper. Execute 4–7 keyword searches (variations of your title) for the United States, past month; then keep only jobs posted within the last 14 days and deduplicate/exclude per the rules.

   Execution prompt · Source text。Claude conversation input / Apify connector configuration

   Copy prompt

   STEP 3 — Scrape
   Use the Apify connector with a LinkedIn Jobs scraper.
   - Run 4–7 keyword searches (variations of my title) for United States, past month.
   - Keep only jobs whose posted date is within the last 14 days.
   - Dedupe in this order: (a) LinkedIn job ID, (b) same company + same normalized
     title in different cities (merge locations into one row), (c) recruiter or
     aggregator reposts of the same description.
   - If I need sponsorship, EXCLUDE roles whose description says any of:
     "no sponsorship", "will not / cannot / unable to sponsor", "not eligible for
     sponsorship", "not open to visa sponsorship", "without the need for current or
     future sponsorship", "U.S. citizens only", "must be a U.S. citizen",
     "U.S. person", "may not be able to... support H-1B", any security clearance.
   - Do NOT exclude a role just because an equal-opportunity statement mentions
     "citizenship".
   - Label sponsorship as "Offered" ONLY if the text says so explicitly.
     Otherwise "Not stated". Never assume.

4. Generate the scored spreadsheet

   Claude outputs an .xlsx with: Title, Company, Location, Work Type, Salary (USD), Date Posted, Applicants, Sponsorship, Required Stack, Match Score (0–100), Apply Priority (High/Medium/Low), Skill Gaps, Apply Link. Sort by priority, then score.

   Execution prompt · Source text。Claude conversation input / spreadsheet generation instruction

   Copy prompt

   STEP 4 — Spreadsheet (.xlsx)
   Columns: Title, Company, Location, Work Type, Salary (USD), Date Posted,
   Applicants, Sponsorship, Required Stack, Match Score (0–100),
   Apply Priority (High/Medium/Low), Skill Gaps, Apply Link.
   - Salary: base range only. Ignore ranges labeled equity, bonus, stock or sign-on.
   - Applicants: keep LinkedIn's buckets ("<25", "200+").
   - Match Score: title fit (30) + share of the JD stack my resume covers (40)
     + core AI/ML present (10) + years-of-experience fit (20).
   - High = score 72+, posted within 7 days, under 100 applicants,
     and salary band not below my minimum.
   - Sort by priority, then score. Add a "Method" tab explaining every rule.

5. Get apply recommendations and resume tweaks

   Claude returns: top 10 jobs to apply to first (one-line reason each), the 5 keywords most often missing from your resume (with counts), 3 rewritten resume bullets using only facts from your resume, and typical base salary ranges for your level computed from listings that published one.

   Execution prompt · Source text。Claude conversation input

   Copy prompt

   STEP 5 — Advice
   Top 10 jobs to apply to first (one line each on why), the 5 keywords most
   often missing from my resume (with counts), 3 rewritten resume bullets using
   only facts from my resume, and typical base salary ranges for my level
   computed from the listings that published one.

STEP 1 — Resume
Summarize my stack, experience and level in exactly 3 lines.

STEP 2 — Interview (then STOP and wait for my answers)
Ask me: dream job title(s), preferred US location (remote/hybrid/on-site + cities),
seniority level, whether I need visa sponsorship, and minimum base salary in USD.

STEP 3 — Scrape
Use the Apify connector with a LinkedIn Jobs scraper.
- Run 4–7 keyword searches (variations of my title) for United States, past month.
- Keep only jobs whose posted date is within the last 14 days.
- Dedupe in this order: (a) LinkedIn job ID, (b) same company + same normalized
  title in different cities (merge locations into one row), (c) recruiter or
  aggregator reposts of the same description.
- If I need sponsorship, EXCLUDE roles whose description says any of:
  "no sponsorship", "will not / cannot / unable to sponsor", "not eligible for
  sponsorship", "not open to visa sponsorship", "without the need for current or
  future sponsorship", "U.S. citizens only", "must be a U.S. citizen",
  "U.S. person", "may not be able to... support H-1B", any security clearance.
- Do NOT exclude a role just because an equal-opportunity statement mentions
  "citizenship".
- Label sponsorship as "Offered" ONLY if the text says so explicitly.
  Otherwise "Not stated". Never assume.

STEP 4 — Spreadsheet (.xlsx)
Columns: Title, Company, Location, Work Type, Salary (USD), Date Posted,
Applicants, Sponsorship, Required Stack, Match Score (0–100),
Apply Priority (High/Medium/Low), Skill Gaps, Apply Link.
- Salary: base range only. Ignore ranges labeled equity, bonus, stock or sign-on.
- Applicants: keep LinkedIn's buckets ("<25", "200+").
- Match Score: title fit (30) + share of the JD stack my resume covers (40)
  + core AI/ML present (10) + years-of-experience fit (20).
- High = score 72+, posted within 7 days, under 100 applicants,
  and salary band not below my minimum.
- Sort by priority, then score. Add a "Method" tab explaining every rule.

STEP 5 — Advice
Top 10 jobs to apply to first (one line each on why), the 5 keywords most
often missing from my resume (with counts), 3 rewritten resume bullets using
only facts from my resume, and typical base salary ranges for my level
computed from the listings that published one.

RULES: Only use real listings returned by the connector. Never invent jobs,
salaries, applicant counts or links. If data is missing, write "Not listed".