86 lines
3.8 KiB
Markdown
86 lines
3.8 KiB
Markdown
# Evidence-first resume kit
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This repository turns verified career evidence into role-specific LaTeX resumes and, when useful, cover letters. It is designed to prevent a common failure mode of AI-assisted applications: making a document sound closer to the job description by silently overstating skills, scope, titles, or outcomes.
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The workflow separates three questions that should never be confused:
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1. **Evidence Fit:** does the candidate actually meet the role, including hard gates?
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2. **Document Quality:** does the resume communicate the supported evidence clearly?
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3. **Channel Strength:** is the application cold, referred, or supported by a warm contact?
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A polished resume cannot repair a failed qualification gate. A strong fit can still be hidden by a weak document. The system records both.
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## Source hierarchy
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```text
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source documents
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-> structured extractions
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-> canonical claims registry
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-> experience records and role bundles
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-> session plan
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-> generated resume / optional cover letter
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```
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`resume_builder/canonical/claims.json` is the highest-authority career source. Generated files under `output/` are never treated as evidence. `historical_outputs.json` records whether an older package is safe to consult, must be revalidated, or must not be reused.
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## Default application strategy
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- Target Staff/Senior Data Engineering first.
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- Use ML Platform/MLOps, data platform, analytics, and semiconductor profiles only where direct evidence covers the actual requirements.
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- Treat direct LLM engineering, formal model evaluation, Terraform-heavy SRE, and similar missing required experience as gates rather than keyword opportunities.
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- Build application cohorts with roughly 70% core, 20% adjacent, and 10% deliberate stretch roles.
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- Add a warm-channel action for core applications.
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The default document is a two-page International Tech resume for US-tech/FAANG-style roles in Europe. A Swiss/DACH policy overlay is available for employers that expect local conventions. Cover letters are conditional, not automatic.
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## Workflow
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### Add evidence
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1. Put employment records, project material, papers, or notes under `knowledge_base/`.
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2. Run `setup-extract` to create a source-grounded extraction.
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3. Review it, then run `setup-build-kb` to update canonical claims, experience files, bundles, and support data.
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### Apply to a role
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1. Run `make-resume` with the real JD.
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2. Review the requirement table and fit gate before approving content generation.
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3. Generate and validate the LaTeX resume.
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4. Run `make-cl` only when the session records a justified `YES` decision.
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5. Run `critique` for separate Evidence Fit, Document Quality, and Channel Strength findings.
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6. Run `edit-resume` for approved repairs.
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The skills live in `.agents/skills/`. Each application receives a session file under `output/<Role>/` that preserves the JD, fit decision, claim IDs, content plan, validation results, and application status.
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## Validation
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Validate the knowledge system:
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```powershell
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python resume_builder/helpers/validate_resume_system.py
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```
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Validate a generated document:
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```powershell
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python resume_builder/helpers/validate_resume_system.py --document output/Role/resume.tex
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```
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Track the active 7/2/1 cohort:
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```powershell
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python resume_builder/helpers/cohort_tracker.py summary
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```
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Character counts are available only as readability diagnostics. There are no fixed character bands, equal-length bullet rules, or page-fill quotas.
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## Output and prerequisites
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The system writes `.tex` source only. Compile locally with a LaTeX distribution such as MiKTeX, TeX Live, or MacTeX. Generated resumes should also be checked through PDF rendering and text extraction before submission.
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See [DOCS.md](DOCS.md) for the architecture and policy reference.
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## License
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MIT; see [LICENSE](LICENSE).
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