Can I vibecode Saply?
price $230/moyou'd save $2760/yrbuild time weekendcategory automationreplaced by 0 people
KINDA
MOATproprietary dataintegrationsexecution polish
AI CV formatting, tailoring, matching, and template automation for staffing firms
The Build Prompt
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Build me a local CV formatting pipeline to replace Saply for personal use. Requirements: - A Node + Express app on localhost:4173 with one page to upload a PDF or DOCX, choose a file from templates/, add an optional job description, and run the job. - Extract DOCX text with mammoth and PDF text with pdftotext. Detect image-only files and stop with a clear message instead of producing an empty CV. - Send extracted text to an LLM API of your choice, key from .env, using a strict JSON schema for contact details, summary, skills, experience, education, and certificates. - Never invent employers, dates, qualifications, or skills. Missing values stay null, and every tailored claim must be supported by the source CV. - Render the JSON into the selected tagged Word template using docxtemplater and PizZip, preserving its fonts, colors, tables, headers, footers, and repeating experience rows. - When a job description is present, show a 0-100 match score, strengths, gaps, and questions. Rewrite the summary and bullets only when a Tailor checkbox is enabled. - Save job metadata and structured JSON in SQLite via better-sqlite3. Delete uploaded source files and generated documents after 24 hours. - Bind to localhost only, with no accounts or telemetry. Data leaves the machine only for the documented LLM call. - Out of scope: OCR for scanned CVs, Word or Google Docs add-ins, ATS/email integrations, bulk processing, collaboration, and enterprise compliance controls. - Include a sample tagged template, two fixture CVs, extraction/render smoke tests, and a README covering setup, .env, template tags, retention, and the honest limitations. - Be aware of the hard part: CVs vary wildly in structure (two-column layouts, tables, sidebars, mixed date formats), and a schema that runs fine on the fixtures will silently miss fields on real-world CVs. Test on messy inputs and document what gets dropped.
What you lose
- ✕extraction accuracy across thousands of real-world CV layouts, tuned on years of data so nothing is silently dropped
- ✕OCR for scanned and image-based CVs
- ✕the AI agent that edits any CV in plain language directly inside Word and Google Docs
- ✕Word, Google Docs, email, and ATS integrations (Bullhorn, Carerix, Spott, Loxo)
- ✕EU tender templates (Europass, DIGIT-TM III, ITUSS21) plus anonymisation and translation
- ✕ISO 27001 certified handling of security-sensitive candidate data
- ✕bulk processing, team workflows, SLA, and enterprise support
Prior art / alternatives
Why it still works
extraction accuracy/integrations/workflows