voice-export

name	voice-export
description	Generate a recruiter simulation prompt for the Claude App (voice mode) from a CV and job ad URL
argument-hint	<path-to-cv> <job-ad-url>
user-invocable	true
allowed-tools	Read(*), Glob(*), Grep(*), WebFetch

Voice Export — Generate Recruiter Simulation Prompt

Generate a self-contained recruiter screening simulation prompt that can be pasted into the Claude App (voice mode). The recruiter stays in character for the entire call — no coaching, no interruptions. Coaching happens afterwards in Claude Code.

Arguments

• $ARGUMENTS (required): Two arguments separated by space:
  1. Path to the CV file (e.g. output/20260210-target-role-slug.md)
  2. Job ad URL (e.g. https://www.upwork.com/project/...)

Instructions

Step 1: Parse Arguments

Extract the CV path and job ad URL from $ARGUMENTS. If only one argument is provided, ask the user for the missing one.

Step 2: Load Sources

1. Read the CV file.
2. Fetch the job ad from the URL using WebFetch.
3. Auto-detect a deep review file: take the CV filename, append -DEEP-REVIEW before the extension.
   • Example: CV output/20260210-target-role-slug.md → look for output/20260210-target-role-slug-DEEP-REVIEW.md
   • If the file exists, read it. If not, skip — the question pool will rely on gap analysis only.
4. Auto-detect a cheat sheet file: take the CV filename, append -cheatsheet before the extension.
   • Example: CV output/20260210-target-role-slug.md → look for output/20260210-target-role-slug-cheatsheet.md
   • If the file exists, read only the header section above the first --- for recruiter persona enrichment (name, company context, intermediary vs. end client). Do NOT read below the first --- — that contains coached answers and candidate prep.
5. Read framework/voice-export.md for the export prompt structure and quality rules.

Step 3: Detect Language

Determine the CV language by scanning section headers and body text. Common header patterns:

• English: "Summary", "Skills", "Certifications", "Projects" → EN
• German: "Kurzprofil", "Fachkenntnisse", "Zertifizierungen" → DE
• French: "Compétences", "Expérience", "Formation" → FR
• Dutch: "Vaardigheden", "Werkervaring", "Opleiding" → NL
• Spanish: "Habilidades", "Experiencia", "Formación" → ES
• Other languages → infer from content
• If uncertain → default to EN

All generated prompt text must match this language.

Step 4: Extract Recruiter Persona

From the job ad, enriched by cheat sheet header if available:

• Company name and brief context (1-2 sentences) — cheat sheet may clarify intermediary vs. end client
• Role title / project description
• Start date, duration, utilisation, remote/onsite
• Contact name — from cheat sheet header or job ad; if neither has one, generate a plausible recruiter name matching the job ad's market/language

Step 5: Build Question Pool

Assemble the question pool from three sources:

A. Deep Review Questions (if file exists)

• Extract the "Top 10 Probing Interview Questions" section from the deep review file
• Include all questions verbatim — these are the highest-value probes

B. Gap-Derived Questions

• Compare job ad requirements against the CV
• Generate 3-5 questions targeting: technical gaps, experience depth mismatches, role-fit concerns
• Do NOT duplicate topics already covered by deep review questions

C. Standard Recruiter Topics

Always include these topics (the recruiter weaves them in naturally):

• Compensation expectations and flexibility (rate for freelance/contract roles, salary for permanent — derive from the CV and job ad context)
• Availability, notice period, or earliest start date
• Remote/onsite preferences and travel willingness
• Motivation for this specific role
• Current employment or engagement status
• Invoicing entity / contracting setup (for freelance/contract roles only, if relevant based on CV)

Step 6: Assemble the Prompt

Build the prompt following this exact section order (use ## headers):

1. System Instruction — Role assignment: realistic recruiter, no coaching, stay in character
2. Recruiter Persona — From Step 4
3. Candidate CV — Full CV text inlined (the recruiter "has it on their desk")
4. Question Pool — From Step 5. Mark this section as internal to the recruiter: "These are topics and questions for you to draw from during the call. Weave them into natural conversation — do not read them as a list."
5. Call Flow Guidelines — Natural pacing: intro → candidate pitch → technical/experience questions → compensation/logistics → closing with next steps. Target 15-20 minutes. Go deeper on fewer questions rather than rushing through all.
6. Session Rules — Language match, stay in character, natural behaviour, ending instruction ("End of simulation. Take this conversation to Claude Code for a full debrief with your coaching files.")
7. Start Instruction — match CV language. Provide the start instruction in the detected language. Examples — EN: Say "Start" to begin the call. / DE: Sag "Start" um das Gespräch zu beginnen. / For other languages, translate accordingly.

Step 7: Quality Check

1. Count words. If > 8,000, apply compression strategies from framework/voice-export.md in priority order.
2. Scan for file references. If any path-like string (data/..., coaching/..., output/...) appears in the prompt, remove it — everything must be inline.
3. Check language consistency. No mixing DE/EN within the prompt.
4. Verify no coached answers leaked. The prompt must NOT contain any prepared candidate answers.

Step 8: Output

Output the assembled prompt inside a single fenced code block (```markdown ... ```) so the candidate can copy it directly into the Claude App.

Before the code block, print a short summary:

• Role name
• Language (DE/EN)
• Word count
• Deep review questions included? (Yes/No) — if No, add: 💡 Run /review-cv-deep <cv-path> first for more substantial probing questions in the simulation.