dennisthiessenandClaude Opus 5
a0ec2fedb5
Scout run 2026-09-04 (36 companies, 778 eligible) plus the NVIDIA package it surfaced, and the tooling and policy fixes found along the way. Application - NVIDIA Senior DevOps Engineer, Zurich (JR2024256): submitted 2026-09-05. Evidence Fit 64/100 Stretch, hard gate FAIL x2, channel Weak. Sent on an explicit user override with the working model unresolved, so hybrid-vs-onsite is a live screening topic. 2pp resume + 1pp cover letter, both validators PASS, 0 boxes, 0 Tier 1 findings open. Policy - Drop the 7 Core / 2 Adjacent / 1 Stretch cohort caps. They had no derivation and were overridden whenever they bound, and the cohort was confounded: it tested fit class while holding channel constant at Weak across all six applications. Keep the recording, drop the rationing. - Channel replaces the slot budget as the rationed variable: no application goes out cold when a named contact is published and reachable. - Propagated through shared_ops, session_file_template, cohort_tracker (counts, never counts-against-a-target) and application_cohort.json. Tool fixes - fetch_workday emitted 404 URLs for every Workday company (NVIDIA, Novartis, Equinor): externalPath is site-relative and needs the locale + site prefix. 6/6 short-form 404, 3/3 long-form 200. 773 dead links repaired across 42 archived reports. - find_decision_url silently de-linked decisions on two boards, which is why ruled-out roles kept resurfacing as undecided. RUAG rotates its UUID on repost; BIS migrated its vacancy URL scheme, orphaning 5 decisions including a live application. Fixed as a two-tier match, not a slug key -- slugs collide (two live RUAG reqs shared one). Knowledge base - claims.json: add Bash / shell scripting, the third canonical gap of this shape after Ansible and Linux administration. - bundle_data_platform: BS-4 was framed as a delivered observability stack over what claims.json calls a proof of concept -- the defect the 2026-08-21 sweep was told to look for. - CLAUDE.md and AGENTS.md: correct the RUAG Data Lakehouse shortlist claim, which the JD does not support and which both files still asserted. - resume_template: comment-guard the missing babel/ngerman language block. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01QFb8tTCig5r1KzkQiNXcYt
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.