Fix: Prevent PDF Overwrites for Duplicate Job Titles

CHANGELOG.md

@@ -5,6 +5,11 @@ All notable changes to ApplyPilot will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [Unreleased]
+
+### Fixed
+- **Duplicate PDF filename collisions** (#11) - Appended MD5 hash of the job URL to the filename prefix for tailored resumes and cover letters to prevent overwriting when multiple jobs have identical titles.
+
 ## [0.2.0] - 2026-02-17
 
 ### Added

src/applypilot/scoring/cover_letter.py

@@ -237,10 +237,12 @@ def run_cover_letters(min_score: int = 7, limit: int = 20,
             letter = generate_cover_letter(resume_text, job, profile,
                                           validation_mode=validation_mode)
 
-            # Build safe filename prefix
+            # Build safe filename prefix with job_id to prevent overwrites
+            import hashlib
             safe_title = re.sub(r"[^\w\s-]", "", job["title"])[:50].strip().replace(" ", "_")
             safe_site = re.sub(r"[^\w\s-]", "", job["site"])[:20].strip().replace(" ", "_")
-            prefix = f"{safe_site}_{safe_title}"
+            job_id = hashlib.md5(job["url"].encode("utf-8")).hexdigest()[:6]
+            prefix = f"{safe_site}_{safe_title}_{job_id}"
 
             cl_path = COVER_LETTER_DIR / f"{prefix}_CL.txt"
             cl_path.write_text(letter, encoding="utf-8")

src/applypilot/scoring/tailor.py

@@ -490,10 +490,12 @@ def run_tailoring(min_score: int = 7, limit: int = 20,
             tailored, report = tailor_resume(resume_text, job, profile,
                                              validation_mode=validation_mode)
 
-            # Build safe filename prefix
+            # Build safe filename prefix with job_id to prevent overwrites
+            import hashlib
             safe_title = re.sub(r"[^\w\s-]", "", job["title"])[:50].strip().replace(" ", "_")
             safe_site = re.sub(r"[^\w\s-]", "", job["site"])[:20].strip().replace(" ", "_")
-            prefix = f"{safe_site}_{safe_title}"
+            job_id = hashlib.md5(job["url"].encode("utf-8")).hexdigest()[:6]
+            prefix = f"{safe_site}_{safe_title}_{job_id}"
 
             # Save tailored resume text
             txt_path = TAILORED_DIR / f"{prefix}.txt"