dennisthiessenandClaude Opus 5
34299bce54
Second-pass review of this session's work by another agent. It corrected several
real mistakes of mine and finished F06 gaps I had left open. Reviewed, verified
and committed as-is.
THE MISS THAT MATTERS
`job_scout/state/applications.json` — the authoritative application registry
introduced by F02 — was never actually tracked. `job_scout/state/*` in
.gitignore silently excluded it, so my checkpoint commit afdef7e claimed to add
it and did not. Only `application_cohort.json`, the GENERATED projection, was
version-controlled; the source of truth and the migration history backup were
not. .gitignore now carries explicit negations for both.
MY ERRORS, CORRECTED
- "kastellaun" was in CH_LOCATION_KEYWORDS. Kastellaun is in Rhineland-Palatinate.
I took it from a Bundeswehr reserve listing and filed it under Switzerland -
precisely the class of bug F12 existed to fix.
- The F12 fix only widened the keyword list; SmartRecruiters still discarded the
structured country, which was the concrete case the review named
(city=Baar, country=CH). The adapter now preserves it, and a COUNTRY_NAMES
mapping gives country precedence over a bare town name.
- F15 was applied to Taleo and Prospective only. Greenhouse still wrote
`updated_at` into `posted`; it now uses `published_at` and carries provenance.
- My negation guard `\b(no|not|...)\b[^.;]{0,60}$` was too loose - an unrelated
"not" within 60 characters could mask a real claim. Tightened to require the
negation to govern the term.
- F18's retirement of the macro-count rule missed "Four to six compact skills
lines" in the workflow; removed now.
F06 GAPS I LEFT OPEN, NOW CLOSED
Date-format validation on education/employment periods (the 2035-99 case named
in the review), evidence-structure validation, referential integrity on evidence
source files, and the missing-identity KeyError.
F10 TAKEN FURTHER, AND RIGHTLY
I demoted the US-GPA fields to *_orientation_only. They are now removed
entirely, with grade_output_rule saying what to do instead: use the German grade
and scale, and if a recipient requires a US GPA, use THEIR method and record it.
Keeping a number derived by a non-recognised method invites its use.
Also: the Capgemini omit-preference now sits in experience_capgemini.md itself,
alongside the instruction not to fill or explain the intentional gap.
BIS BASEL - REJECTED 2026-09-11
jr100429, applied 2026-07-10, no interview, no reason given. Recorded in the
registry with a dated event and in the decision log; the "prep interview brief
when screening lands" next action is cleared. This also settles the 2026-09-04
observation that the req had vanished from the BIS board - it was a real
rejection, not just a closed posting.
VERIFICATION I RAN MYSELF BEFORE COMMITTING
49/49 tests. Validator PASS with 0 advisory warnings. Package manifest and
registry views current. All 12 F12 location cases still correct, including
Kastellaun now rejecting. The F05 counter-proof still catches all 9 violations.
SBB, NVIDIA, RUAG and Kdo Cy resumes still scan clean. Diffs are net-additive
across every reference file - the deletions are replacements, not losses.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Qej5SBvhcbGXHwLqNWiskM
3.5 KiB
Generate a Resume
Input
Accept a JD file, pasted JD, or URL. Obtain the real posting text before analysis. If it cannot be retrieved, ask the user to paste it; never reconstruct it.
Phase 0 — Canonical and JD Preflight
- Read
resume_builder/reference/shared_ops.md. - Read
resume_builder/canonical/claims.jsoncompletely. - Read
config.md,AGENTS.md,application_strategy.md,resume_reference.md, andcritique_framework.md. - Run
python resume_builder/helpers/validate_resume_system.py. - Verify and save the verbatim JD; record source/date/status.
- Create the output folder and session from
session_file_template.md.
Phase 1 — Fit Gate Before Writing
Build a requirement table with Required/Preferred and Direct/Adjacent/Gap/Constraint classifications. Cite canonical claim IDs for every Direct or Adjacent match.
Compute Evidence Fit and identify hard gates using critique_framework.md. Then assign:
- Core.
- Adjacent.
- Stretch.
- No-go.
Record Channel Strength and a warm-channel action for Core roles.
Stop and present the fit decision before resume planning when:
- a hard gate fails;
- the role is Stretch;
- a practical constraint is unresolved.
Proceed on a no-go only after the user explicitly overrides the stated reason. An override does not change the fit classification.
Phase 2 — Audience and Content Plan
Select the audience profile from resume_reference.md: International Tech, Swiss/DACH, Denmark, Norway or Employer-specific. Employer requirements take precedence.
Read the matching role bundle, skills_taxonomy.md, achievement_reframing_guide.md, and only the experience files needed for selected claims.
Plan:
- Optional summary sized to the selected audience profile.
- Compact evidence-backed skills block, normally 4–8 rendered lines, verified in the PDF.
- Swisscom 4--5 bullets.
- Bosch 3--4 bullets.
- Zero or one bullet for each older role.
- Normally 11--14 bullets total, with no page-fill quota.
For each proposed bullet show canonical ID, evidence type, scope, and why it belongs. Identify any impact metric that still needs user confirmation.
Apply cl_reference.md and propose Cover Letter Decision: YES/NO with one reason.
Present the plan and wait for explicit user confirmation before generation.
Phase 3 — Generate
Copy resume.cls and resume_template.tex into the output folder. For a Swiss/DACH audience, also read resume_template_dach.tex as an audience-policy overlay; it is not a standalone document. Write fresh content from canonical claims and experience records; never copy a historical output.
Rules:
- Employer, formal/transparent title, dates and location come first.
- Do not use tailored themes as job titles.
- Mix natural bullet lengths.
- Use only verified metrics and outcomes.
- Preserve allowed verbs and ownership scope.
- Never add a skill to mirror the JD.
- Certifications appear once.
Phase 4 — Validate and Inspect
Run:
python resume_builder/helpers/validate_resume_system.py --document <resume.tex>
Fix every failure. Compile the LaTeX, inspect both pages, and run pdftotext to verify parsing and information order. Do not add filler to reduce whitespace.
Update the session with the as-built content, validation results, page count, cover-letter decision, and next action.
Present the finished resume and the original Evidence Fit classification. Wait for approval. If the cover-letter decision is YES, point to /make-cl; otherwise point directly to /critique.