Add a Language Gate - no dimension currently checks language requirements (#275)

Nothing in the framework checks a posting's language requirements
against what the candidate actually speaks. It is not one of the five
Scoring Dimensions in 04-job-evaluation.md, it is not checked in
/scrape's Step 3 fit assessment, and it is not a field in /rank's JSON
output - even though /apply's Step 1 already extracts a posting's
required language generically, with nowhere to report a mismatch to.

This adds a Language Gate, structured like the existing Eligibility
Gate (read the posting, classify against profile data, hard-stop on a
real mismatch), built on a new structured Languages table in CLAUDE.md
/ 01-candidate-profile.md. /setup now asks for it directly (Path C), or
infers it from a CV/LinkedIn export (Paths A/B - LinkedIn exports
already carry a self-rated Languages section).

The gate compares a posting's stated language requirements against
that table with three outcomes:

- Requires a language not declared at all -> hard FAIL, never
  presented.
- Requires a higher level in a language that is declared (e.g. "fluent
  English" against a declared B1/B2) -> FLAG, not an auto-reject -
  scored and drafted normally, with the gap surfaced so the candidate
  judges it themselves (a "fluent" bar reads very differently from a
  strict employer vs. one that's flexible on it).
- Requires a language at or below the declared level -> clean PASS.

Wired through the three places that need it: /scrape (Step 3), /rank
(new language_gate/language_note fields alongside the existing
location veto - both are now persisted to seen_jobs.json, not just
used transiently to decide one run's shortlist), and /apply (Step 1's
language extraction now has somewhere to report to).

Out of scope, deliberately: this does not touch the free-form
Deal-breakers list or how it's used elsewhere (e.g. Scoring Dimension
4's relocation check) - that's a separate question this change takes
no position on.

Validated with two live-testing passes against real, unfetched
postings (not fabricated text) across 3 portals and 3 market languages
(Danish, German, Spanish/Argentina): 8/8 postings gated correctly in
the first pass, including ambiguous real-world wording ("you
communicate well in English") a rigid rule would have gotten wrong. A
second pass, run specifically to force a hard-FAIL case, found one
(a Danish posting requiring the ability to read Danish) and confirmed
it persists correctly and would be excluded from /rank's shortlist.

Co-authored-by: Claude Sonnet 5 <noreply@anthropic.com>

.claude/commands/setup.md

@@ -92,9 +92,9 @@ Hold this content in context throughout Path A. Do not re-read.
 
 Read each document found in Step A1. Process subfolders in this order: `cv/`, `linkedin/`, `diplomas/`, `references/`, `applications/`.
 
-**`cv/` documents:** name, contact (email, phone, LinkedIn, GitHub), education (degree, institution, dates, thesis), work experience (title, company, dates, location, bullets), skills, publications, awards, profile/summary.
+**`cv/` documents:** name, contact (email, phone, LinkedIn, GitHub), education (degree, institution, dates, thesis), work experience (title, company, dates, location, bullets), skills, languages (with any stated proficiency), publications, awards, profile/summary.
 
-**`linkedin/` documents:** About/summary section (full text, used for behavioral inference), work experience, education, skills and endorsements, certifications, volunteer work, publications, recommendations received (full text). If multiple LinkedIn exports are present, use the most recently modified file.
+**`linkedin/` documents:** About/summary section (full text, used for behavioral inference), work experience, education, skills and endorsements, **Languages section** (language name + self-rated proficiency level, e.g. "Spanish - Native or bilingual proficiency" - a high-confidence structured source, feeds the Language Gate in `04-job-evaluation.md`), certifications, volunteer work, publications, recommendations received (full text). If multiple LinkedIn exports are present, use the most recently modified file.
 
 **`diplomas/` documents:** official degree title and level, institution name (official spelling), graduation date, grade or distinction or GPA if visible.
 
@@ -218,6 +218,7 @@ Documents cover skills, experience, education, references, and behavioral signal
 - Career goals and target role types
 - What excites the user in their next role
 - Deal-breakers and must-haves
+- Languages you work in professionally, with proficiency levels (only if not already extracted from `cv/` or `linkedin/` above) - this feeds the Language Gate in `04-job-evaluation.md`, so ask directly rather than skipping it
 - Salary expectations / baseline (optional)
 - Commute or location constraints (if not visible from CV)
 - Job search configuration (use the questions from Path C Section 9 below)
@@ -231,9 +232,9 @@ Then proceed to Step 3 to populate the non-skill files (`CLAUDE.md`, `cv/main_ex
 If the user provides a single CV/resume:
 
 1. Read the document thoroughly.
-2. Extract all structured information: name, contact, education, experience, skills, publications, awards.
+2. Extract all structured information: name, contact, education, experience, skills, languages, publications, awards.
 3. Present a summary of what was extracted.
-4. Ask follow-up questions for gaps (behavioral profile, career goals, deal-breakers, salary expectations, references).
+4. Ask follow-up questions for gaps (behavioral profile, career goals, deal-breakers, languages and proficiency levels if not already extracted, salary expectations, references).
 5. Proceed to Step 3 (file generation).
 
 ---
@@ -247,7 +248,7 @@ Ask about:
 - Full name
 - Location (city, country)
 - Phone, email, LinkedIn, GitHub
-- Languages spoken (with proficiency levels)
+- What languages they work in professionally, and roughly what level in each (native, fluent, conversational, a CEFR letter like B2 - whatever's natural for them to describe, doesn't need to be precise). Worth explaining why: a posting requiring a language they don't list at all gets auto-excluded later by the Language Gate, while one asking for a higher level in a language they do list gets flagged for their own judgment instead of silently passed or rejected - so it's worth being honest here rather than optimistic.
 - Current employment status
 - Family/commute constraints (if any)
 
@@ -333,7 +334,7 @@ Once data collection is complete, generate or finish populating the following fi
 Replace all `[PLACEHOLDER]` tokens with the user's actual information. Keep the structure, workflow, and verification checklist intact.
 
 ### 2. Populate `01-candidate-profile.md` *(Path B and C; skip if Path A populated it)*
-Write the full candidate profile with structured sections: Identity, Education, Professional Experience, Independent Projects, Technical Skills, Publications, Awards, References.
+Write the full candidate profile with structured sections: Identity (including Languages, with levels), Education, Professional Experience, Independent Projects, Technical Skills, Publications, Awards, References.
 
 ### 3. Populate `02-behavioral-profile.md` *(Path B and C; skip if Path A populated it)*
 Write the behavioral profile based on assessment results or synthesized answers.