dennisthiessenandClaude Opus 5
7953884810
No Tier 1 truth findings, validator PASS, and the claim audit clears every material claim including the PP-1 block, which carries its personal-project label three times and no performance figure. The one real weakness is that the document under-uses the JD's own language for the responsibility it matches best. R2 - ingestion, transformation, storage and lifecycle management of large datasets - is the single core responsibility scored Direct and is close to a plain description of SW-1 and SW-7, yet none of those four words appears in the resume body. Two related misses: "distributed systems" never appears as a phrase even though Q4 is a required qualification scored Direct, so the nearest exact match sits on an eight-year-old Vizrt bullet rather than current work; and "distributed databases" was called a Direct preferred hit in Phase 0 but is only ever implied through product names. Three Tier 1 fixes, all honestly available, worth roughly +4. Recorded what must NOT be added, since the obvious way to raise keyword coverage is the wrong one: "scalable" is an unverified scale claim that ai_fingerprint_rules forbids, and SDK, high-throughput, data-intensive, simulation, model development and self-service have zero canonical evidence. Their absence is correct, not a defect. Flagged the headline "Production Data Platforms on AWS" as Tier 2. It carries no ownership verb so it is not a scope violation, but it is the most prominent line in the document and leans toward exactly the platform framing R1 is a gap on - the likeliest opening challenge from a technical reviewer. Scored against critique_framework.md section 9. The SKILL.md 8-dimension table with "Publications 10%" is a stale CV-era scheme and was not used, the same conflict class as its "all bullets 2L" line. The verdict does not change the decision. Document quality is not the constraint here: the working model is still unresolved and the channel is still cold. 86 is not submit-ready. 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.