feat(ats): extract PDF text with pypdf before Poppler (#369)

* feat(ats): extract PDF text with pypdf before Poppler

Lead the ATS text-layer check with pypdf (BSD, optional pip install). Fall back to pdftotext -layout -enc UTF-8. No cache directory, no installer, no AGPL pymupdf. Windows users without Poppler still get a mechanical parseability check; visual review remains the last resort.

* Update verify_pdf.py

* Update apply.md

* Update verify_pdf.py

* Update verify_pdf.py

.claude/commands/apply.md

@@ -258,15 +258,19 @@ Do not proceed to Step 6 until both PDFs pass inspection.
 
 An ATS parser reads the PDF's embedded **text layer**, not the rendered page — a CV that passed visual inspection can still extract as garbage (icon glyphs where the contact details should be, scrambled reading order in multi-column layouts). This step verifies what a parser actually sees. It applies to the **CV only**; cover letters rarely go through keyword screening.
 
-**Availability check:** run `pdftotext -v`. `pdftotext` (poppler) is an optional dependency, not part of TeX distributions. If it is missing, print a one-line warning that the mechanical parse check is skipped, do the keyword-coverage check (item 3 below) against your visual Read of the PDF instead, and note the degraded mode in the Step 6 report. Same graceful-skip pattern as the salary lookup. Keep the `-enc UTF-8` flag: Xpdf-based builds default to Latin-1 output, and without it a correct non-ASCII CV fails the replacement-character check below.
+**Availability check:** extract with `python tools/verify_pdf.py` (tries **pypdf** first — BSD, `pip install pypdf` — then Poppler `pdftotext`). If both are missing, print a one-line warning that the mechanical parse check is skipped, do the keyword-coverage check (item 3 below) against your visual Read of the PDF instead, and note the degraded mode in the Step 6 report. Same graceful-skip pattern as the salary lookup. If a documented fallback still shells out to `pdftotext -layout`, keep the `-enc UTF-8` flag: Xpdf-based builds default to Latin-1 output, and without it a correct non-ASCII CV fails the replacement-character check below.
 
 **1. Extract the text layer:**
 
 ```bash
-cd cv && pdftotext -layout -enc UTF-8 main_<company>_<role>.pdf main_<company>_<role>.txt
+python tools/verify_pdf.py cv/main_<company>_<role>.pdf --dump-text cv/main_<company>_<role>.txt
 ```
 
-Read the `.txt` file.
+The command prints `extractor: pypdf` or `extractor: pdftotext`. Record that name in the Step 6 report. Read the `.txt` file. If that tool is unavailable, the Poppler fallback is:
+
+```bash
+cd cv && pdftotext -layout -enc UTF-8 main_<company>_<role>.pdf main_<company>_<role>.txt
+```
 
 **2. Parseability checks** on the extracted text:
 
@@ -288,6 +292,10 @@ Failures here are template-level problems: fix them in the `<CV_EXT>` source (e.
 - **missing (have it)** — the profile shows the candidate genuinely has this skill but the CV never says it: add it where it fits naturally, preferring experience bullets (concrete evidence) over the profile statement, then re-run 5a–5c.
 - **missing (gap)** — a genuine gap: leave it missing. **Never stuff keywords.** This is the same honesty rule the reviewer follows — a gap gets acknowledged in the cover letter's framing, not hidden in the CV.
 
+
+> **Note:** A multi-word phrase reported missing may be a punctuation-spacing artifact between extractors (pypdf sometimes inserts spaces around punctuation that Poppler does not). Re-check against the other extractor before concluding the text is absent.
+
+
 **4. Clean up:** delete the extracted `.txt` file.
 
 ### 5e. Clean up build artifacts