dennisthiessenandClaude Opus 5
b8c5141279
F18 - page-one weighting - Across four compiled packages the first employment entry began at 45-55% of page one. A reader reaching the middle of the page had seen a profile and a skills block, and no evidence. - The old skills rule counted `\skillline` MACROS, which does not control density: six categories with long value lists wrap to twelve or more rendered lines. Replaced with a rendered-line budget verified in the compiled PDF, plus the rule that a skills line repeating what three bullets below already prove is spending the first screen to say it twice. - Employer chronology stays reverse-chronological; the ordering freedom is WITHIN a position, where the bullet closest to the target role leads. - New helper `page_metrics.py` measures page-one geometry from the PDF via pdftotext -bbox. F19 - education and phrasing - Degree + focus + institution crammed into one cell with a right-aligned date produced visible word spacing and mid-word breaks in SBB, NVIDIA and Aker BP. Split the entry, as RUAG_AI_Engineer_C5I already did. Template carries the rule. - Grades and thesis stay only where concretely relevant. claims.json grade_output_rule already keeps grades off by default; the M.Eng. thesis earns its place on condition-monitoring or sensor-data roles and otherwise does not. Validated by measurement rather than assertion - page_metrics.py first reproduced the review's own figures (NVIDIA 55.4%, SBB 50.6%, RUAG 50.3%). That is what makes the after-figure meaningful. - The NVIDIA CV was rebuilt FROM A COPY with the guidance applied: experience moved 55.4% -> 44.0% of page one, page-one text lines 47 -> 39, still two pages, zero overfull boxes, validator PASS. - The submitted package itself was not modified. F17 classifies archives as evidence, not as drafts. The percentage is a diagnostic, not a target: no pixel goal and no rule that page two must be filled. All 19 review findings are now complete. 37/37 tests pass. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Qej5SBvhcbGXHwLqNWiskM
60 lines
2.4 KiB
Python
60 lines
2.4 KiB
Python
# -*- coding: utf-8 -*-
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"""Measure where the first real employment evidence starts on page one (review finding F18).
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Usage: python resume_builder/helpers/page_metrics.py <resume.pdf> [more.pdf ...]
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Requires pdftotext (ships with MiKTeX).
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The review reported ~50-55% for four packages. This reproduces that measurement from the
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compiled PDF so a change can be checked rather than asserted. The percentage is a diagnostic,
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never a target: no pixel goal, no forced page-two filling.
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"""
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import io
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import re
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import subprocess
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import sys
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import tempfile
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from pathlib import Path
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sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8", errors="replace")
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PDFTOTEXT = Path(r"C:\Users\Dennis\AppData\Local\Programs\MiKTeX\miktex\bin\x64\pdftotext.exe")
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HEADINGS = ("professional experience", "berufserfahrung", "experience")
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def measure(pdf: Path) -> dict:
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with tempfile.TemporaryDirectory() as work:
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out = Path(work) / "bbox.xml"
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subprocess.run([str(PDFTOTEXT), "-bbox", "-f", "1", "-l", "1", str(pdf), str(out)],
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capture_output=True, check=True)
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xml = out.read_text(encoding="utf-8", errors="replace")
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page = re.search(r'<page width="([\d.]+)" height="([\d.]+)"', xml)
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height = float(page.group(2))
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words = [(float(m.group(2)), m.group(5).lower())
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for m in re.finditer(
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r'<word xMin="([\d.]+)" yMin="([\d.]+)" xMax="([\d.]+)" yMax="([\d.]+)">([^<]*)</word>',
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xml)]
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first_y = None
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for index, (y, text) in enumerate(words):
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if text in ("professional", "berufserfahrung", "experience"):
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following = " ".join(w for _, w in words[index:index + 2])
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if any(h.startswith(following.split()[0]) for h in HEADINGS):
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first_y = y
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break
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lines = len({round(y, 1) for y, _ in words})
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return {
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"height": height,
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"experience_y": first_y,
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"experience_pct": round(100 * first_y / height, 1) if first_y else None,
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"page1_text_lines": lines,
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"page1_words": len(words),
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}
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if __name__ == "__main__":
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for argument in sys.argv[1:]:
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pdf = Path(argument)
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data = measure(pdf)
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pct = data["experience_pct"]
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print(f"{pdf.parent.name:<34} experience starts at "
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f"{pct if pct is not None else '?':>5}% of page 1 | "
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f"{data['page1_text_lines']:>3} text lines, {data['page1_words']:>4} words")
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