dennisthiessenandClaude Opus 5
f8b4cd3473
Cover letter: 1 page, 270 words, 3 paragraphs, validator PASS. Carries the one thing the resume structurally cannot - why a Swisscom data engineer credibly wants a research-platform seat at a market maker, which against an ex-FAANG field is the main differentiator. Deliberately uses no external hooks. cl_reference.md says to omit an unnecessary hook rather than spend words proving company familiarity, so the only hook is the JD's own language, scraped verbatim and first-party. No named executives: nothing to verify, and nothing that reads as name-dropping. The letter also states the R1 gap plainly rather than hiding it, because a technical reviewer will find it in the first question anyway and owning it is stronger than being caught by it. PP-1 appears with its personal-project label and an explicit "I make no claims for its results". Separately, loading the bundles for this letter surfaced two live scope traps. The 2026-08-21 correction that fixed the SW-1 violation in bundle_data_engineer.md ended with "check the other four bundles for the same pattern". That sweep was never done, and two of them carried it: bundle_data_platform.md - the S5 cover-letter hook read "migrating Swisscom's legacy ETL stack to a cloud-native AWS platform", and its narrative thread said "migrating an entire ETL infrastructure". Both pair a full-ownership framing with a company-scale object, which claims.json SW-1 forbids outright. bundle_ml_ai_engineer.md - the SW-1 reframing row read "Built cloud-native data infrastructure on AWS ... the scalable data layer", which is both a full-ownership verb on an org-scale object and an unverified scale claim. Both rewritten to the scoped form with inline scope warnings, matching the data_engineer fix. bundle_analytics_engineer.md and bundle_semiconductor.md were checked and are clean. These were loaded traps: any future cover letter built from either bundle would have started from a sentence claims.json forbids. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01MHtzyTKBcg6BWhD5qFegtK
Evidence-first resume kit
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.
The workflow separates three questions that should never be confused:
- Evidence Fit: does the candidate actually meet the role, including hard gates?
- Document Quality: does the resume communicate the supported evidence clearly?
- Channel Strength: is the application cold, referred, or supported by a warm contact?
A polished resume cannot repair a failed qualification gate. A strong fit can still be hidden by a weak document. The system records both.
Source hierarchy
source documents
-> structured extractions
-> canonical claims registry
-> experience records and role bundles
-> session plan
-> generated resume / optional cover letter
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.
Default application strategy
- Target Staff/Senior Data Engineering first.
- Use ML Platform/MLOps, data platform, analytics, and semiconductor profiles only where direct evidence covers the actual requirements.
- Treat direct LLM engineering, formal model evaluation, Terraform-heavy SRE, and similar missing required experience as gates rather than keyword opportunities.
- Build application cohorts with roughly 70% core, 20% adjacent, and 10% deliberate stretch roles.
- Add a warm-channel action for core applications.
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.
Workflow
Add evidence
- Put employment records, project material, papers, or notes under
knowledge_base/. - Run
setup-extractto create a source-grounded extraction. - Review it, then run
setup-build-kbto update canonical claims, experience files, bundles, and support data.
Apply to a role
- Run
make-resumewith the real JD. - Review the requirement table and fit gate before approving content generation.
- Generate and validate the LaTeX resume.
- Run
make-clonly when the session records a justifiedYESdecision. - Run
critiquefor separate Evidence Fit, Document Quality, and Channel Strength findings. - Run
edit-resumefor approved repairs.
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.
Validation
Validate the knowledge system:
python resume_builder/helpers/validate_resume_system.py
Validate a generated document:
python resume_builder/helpers/validate_resume_system.py --document output/Role/resume.tex
Track the active 7/2/1 cohort:
python resume_builder/helpers/cohort_tracker.py summary
Character counts are available only as readability diagnostics. There are no fixed character bands, equal-length bullet rules, or page-fill quotas.
Output and prerequisites
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.
See DOCS.md for the architecture and policy reference.
License
MIT; see LICENSE.