diff --git a/.~lock.clean_jobs_2025-11-06_16-32.csv# b/.~lock.clean_jobs_2025-11-06_16-32.csv#
new file mode 100644
index 00000000..874168e1
--- /dev/null
+++ b/.~lock.clean_jobs_2025-11-06_16-32.csv#
@@ -0,0 +1 @@
+,victor,Victor,06.11.2025 16:32,file:///home/victor/.config/libreoffice/4;
\ No newline at end of file
diff --git a/README.md b/README.md
index 31e75642..fe52842e 100644
--- a/README.md
+++ b/README.md
@@ -39,7 +39,10 @@ jobs = scrape_jobs(
 print(f"Found {len(jobs)} jobs")
 print(jobs.head())
 jobs.to_csv("jobs.csv", quoting=csv.QUOTE_NONNUMERIC, escapechar="\\", index=False) # to_excel
-```
+
+Already created a file that can be modified for your specific needs hunt.py
+``` 
+
 
 ### Output
 
diff --git a/hunt.py b/hunt.py
new file mode 100644
index 00000000..d2213695
--- /dev/null
+++ b/hunt.py
@@ -0,0 +1,80 @@
+import csv
+import pandas as pd
+from datetime import datetime
+from jobspy import scrape_jobs
+
+search_terms = [
+    "React Native Developer",
+    "Mobile App Developer",
+    "Frontend Developer React Native",
+    "React Developer Mobile"
+]
+
+locations = [
+    "Lagos, Nigeria",
+    "Abuja, Nigeria",
+    "Oyo, Nigeria",
+    "Ogun, Nigeria",
+    "Remote"
+]
+
+all_jobs = []
+
+for term in search_terms:
+    for location in locations:
+        print(f"\n🔍 Searching for {term} in {location}...")
+        try:
+            jobs = scrape_jobs(
+                site_name=["indeed", "linkedin", "google"],
+                search_term=term,
+                google_search_term=f"{term} jobs near {location} since last week",
+                location=location,
+                results_wanted=40,
+                hours_old=168,
+                country_indeed="Nigeria",
+                linkedin_fetch_description=False,
+            )
+            print(f" Found {len(jobs)} jobs for {term} in {location}")
+            if not jobs.empty:
+                all_jobs.append(jobs)
+        except Exception as e:
+            print(f" Error fetching {term} in {location}: {e}")
+
+if all_jobs:
+    final_jobs = pd.concat(all_jobs).drop_duplicates(subset=["title", "company", "site"], keep="first")
+    final_jobs.sort_values(by="date_posted", ascending=False, inplace=True)
+
+    columns_to_keep = [
+        "title", "company", "location", "via", "site", "date_posted",
+        "job_url", "is_remote", "salary", "job_type", "description"
+    ]
+
+    for col in columns_to_keep:
+        if col not in final_jobs.columns:
+            final_jobs[col] = ""
+
+    final_jobs = final_jobs[columns_to_keep]
+
+    final_jobs.rename(columns={
+        "title": "Job Title",
+        "company": "Company",
+        "location": "Location",
+        "via": "Posted Via",
+        "site": "Source Site",
+        "date_posted": "Date Posted",
+        "job_url": "Job URL",
+        "is_remote": "Remote",
+        "salary": "Salary",
+        "job_type": "Job Type",
+        "description": "Description",
+    }, inplace=True)
+
+    final_jobs.fillna("N/A", inplace=True)
+    final_jobs["Description"] = final_jobs["Description"].astype(str) + "..."
+    final_jobs.sort_values(by=["Location", "Job Title"], inplace=True)
+    filename = f"Jobs_results_{datetime.now().strftime('%Y-%m-%d_%H-%M')}.csv"
+    final_jobs.to_csv(filename, quoting=csv.QUOTE_NONNUMERIC, escapechar="\\", index=False, encoding="utf-8-sig")
+
+    print(f"\n Saved clean, formatted results to: {filename}")
+else:
+    print("\n No jobs found. Try adjusting your parameters.")