Ai used resume

ai-used-resume

When to Use

Invoke this skill whenever the user wants to turn their AI-tool usage history into a résumé artefact. Common triggers:

• "Generate my résumé from my AI usage."
• "Render my CV in Japanese / German / Traditional Chinese / Europass."
• "Tailor my résumé for this JD."
• "Review / score my latest résumé."
• "Show my résumé score trend."
• "Which locales am I weakest in?"

Do NOT invoke when the user:

• Asks to write résumé content from scratch without AI-usage history (no signal source).
• Wants a generic CV template — this skill is opinionated about the AI-coding-era narrative.
• Asks for a profile on a platform that doesn't use Markdown/DOCX/PDF (e.g. LinkedIn scraping).

Quick Reference

Intent	Command
Fresh pipeline	uv run vibe-resume extract && uv run vibe-resume aggregate && uv run vibe-resume enrich --locale en_US then process prompts in session, then enrich --ingest --locale en_US && render -f all --locale en_US
Render single locale	uv run vibe-resume render -f md --locale ja_JP
All 10 locales	uv run vibe-resume render --all-locales
JD-tailored run	uv run vibe-resume enrich --tailor data/imports/jd.txt --locale en_US -n 1 && uv run vibe-resume render -f md --locale en_US --tailor data/imports/jd.txt
Persona-biased enrich	uv run vibe-resume enrich --persona tech_lead --locale en_US (keys: tech_lead / hr / executive / startup_founder / academic)
Multi-persona enrich in one run	uv run vibe-resume enrich --persona tech_lead,hr,executive --locale en_US or --persona all — each persona writes its own _project_groups.<persona>.json
Level-tuned enrich	uv run vibe-resume enrich --level senior --locale en_US (keys: new_grad / junior / mid / senior / staff_plus / research_scientist)
Persona render	uv run vibe-resume render --persona tech_lead --locale en_US reads the persona-scoped cache and emits resume_v<NNN>_<locale>_<persona>.md
Compare persona output	uv run vibe-resume personas-compare --locale en_US -n 3 — side-by-side bullets per persona for the top-N groups (quality iteration loop). --locale required since 0.4.0.
Score latest	uv run vibe-resume review
Score with JD echo	uv run vibe-resume review --jd data/imports/jd.txt
Scores as JSON (for agents)	uv run vibe-resume review --json → structured scorecard + resolved target path on stdout; review --variants --json scores every rendered variant (ats/detailed/base) in one call (#91). Review always prints which file it scored (#86).
Score with persona lens	uv run vibe-resume review --persona hr — appends persona-specific review tips
Disclose real signals (self-mine)	uv run vibe-resume evidence --json — per group: candidate metrics, backed terms, human-gate evidence, provenance. --jd <file> adds present-but-omitted vs genuinely-absent keywords. Surface only what's disclosed — never invent.
Ground in the code	uv run vibe-resume scan → process each *.scan.prompt.md with a cheap-model subagent (one per project, parallel) → uv run vibe-resume scan --ingest. Grounds bullets in what the repo actually does. Opt-in; never uploads code, drops secrets.
Fit a page budget	uv run vibe-resume render --max-pages 2 --locale en_US — tighten bullet density, not just --top-n
Standard variant set	uv run vibe-resume render --variants --locale en_US — ATS (page-budgeted) + detailed, same cache
Truth-preserving auto-iterate	uv run vibe-resume iterate --locale en_US — lift the grade via truthful levers, stop honestly at the ceiling, print human-applied suggestions (dry-run; --write to snapshot)
Explore the layout surface	uv run vibe-resume explore --locale en_US --top-n 4,6,8 --page-budget 1.5,2.0,2.5 — sweep the grid, review each cell, surface the Pareto front (score↑ / pages↓). Pure layout/selection — never rewrites bullets. --write '6,2.0' snapshots a cell.
Per-gap JD grounding	uv run vibe-resume jd-check --tailor data/imports/jd.txt --explain — per missing keyword: groundable (with supporting activity snippets + refs) vs honestly absent. Advisory only; never auto-inserts.
Angle-biased candidate bullets	uv run vibe-resume enrich --candidates impact_first,breadth_first,depth_first --locale en_US emits N framings per group; uv run vibe-resume bullets-compare --locale en_US shows them side by side to pick per group. Angle is a prompt prefix — anti-fabrication rules unchanged.
Persona compare with scores	uv run vibe-resume personas-compare --locale en_US --with-scores --tailor data/imports/jd.txt — bullet diff plus a per-persona review-score table; highlights the best-JD-fit persona.
Branch a gate decision	uv run vibe-resume run --branch G2 --decision '{"choice":"top_n","top_n":8}' forks the ledger, recomputes that gate's suffix, auto review-diffs vs the original. run --branches lists forks; run --adopt <id> promotes one.
Curate groups (human-in-loop)	uv run vibe-resume curate then set actions without editing YAML: curate --drop <name> / --merge <src>:<dst> / --keep <name>, then curate --apply (executes the action field, independent of tier) (#87). emphasis 'foreground my security work' sets a free-text bias for the next enrich.
Drive the gate machine (agents)	uv run vibe-resume gates state --json — armed gates, fully-wired vs emit-only, pending gate, each recorded decision + recompute suffix (#90).
Cap bullets per group	uv run vibe-resume render --bullets-per-group N — hard cap; warns when bullets are dropped instead of silently truncating (#88). The detailed variant is no longer floored by the global page budget.
Compare two versions' scores	uv run vibe-resume review-diff v001 v002 --jd data/imports/jd.txt — per-check scorecard delta.
Per-locale trend	uv run vibe-resume trend --locale zh_TW

Locale quick map
en_US	default, XYZ verbs, ATS flat skills
en_EU	Europass labelled personal-info, CEFR
en_GB	UK spelling, Personal statement
zh_TW	繁中,中英技術混排
zh_HK	bilingual EN + 繁 headings
zh_CN	简体,大厂 ATS-friendly
ja_JP	DOCX = JIS Z 8303 履歴書 grid; md = 職務経歴書
ko_KR	이력서 + photo expected
de_DE	Lebenslauf + Persönliche Daten, photo expected
fr_FR	Profil / Compétences / Expérience

Procedure

1. Locate the repo. The CLI binary is vibe-resume; check with which vibe-resume or uv run vibe-resume --help. If not installed, ask the user whether to clone https://github.com/easyvibecoding/vibe-resume and run uv venv && uv pip install -e ".[dev]".

2. Verify profile.yaml.

   • If still the example (Your Name placeholder), ask for name, email, target_role, summary. Optional: experience, education, languages, custom_sections.
   • For locale-specific text, use <field>_<locale> overrides (e.g. title_zh_TW, summary_ja_JP, bullets_de_DE). See profile.example.yaml for the full list including locale-conditional personal fields (dob, gender, nationality, mil_service, photo_path, marital_status) — only rendered when the active locale's personal_fields includes them.

3. Confirm config.yaml knobs.

   • scan.mode — full for all $HOME .git repos, whitelist to restrict to scan.roots (use if first run is slow).
   • privacy.blocklist — project names to exclude from extractor output.
   • privacy.abstract_tech — true to hide concrete tech names.
   • render.locale — team default.
   • render.all_locales_formats — formats per locale for --all-locales (default ["md"]).

4. Run extractors → aggregate.

   uv run vibe-resume extract
   uv run vibe-resume status              # sanity-check per-source counts
   uv run vibe-resume aggregate           # → data/cache/_project_groups.json
   

4a. (Default, uses subscription quota) Emit + session-driven enrich.

uv run vibe-resume enrich --locale <L>
# → writes data/enrich_jobs/<persona-or-default>/<L>/manifest.json
#   + one *.prompt.md per project group

In the current Claude Code session:

1. Read data/enrich_jobs/<persona>/<L>/manifest.json
2. For each status: pending entry, read its *.prompt.md
3. Produce strict YAML matching the schema the prompt requires
4. Write to the entry's output_path (NNN_<name>.yaml)

When all entries are done:

uv run vibe-resume enrich --ingest --locale <L>
# → merges *.yaml into _project_groups.<persona>.<L>.json

Add --level <key> to bias bullet ambition for the seniority bracket (e.g. enrich --level senior --locale en_US).

Multi-locale runs are independent (per-locale subdir + per-locale cache):

uv run vibe-resume enrich --locale en_US
uv run vibe-resume enrich --locale zh_TW      # does NOT overwrite en_US
# process both locales' yaml in session…
uv run vibe-resume enrich --ingest --locale en_US
uv run vibe-resume enrich --ingest --locale zh_TW
uv run vibe-resume render --locale en_US      # reads _project_groups.default.en_US.json
uv run vibe-resume render --locale zh_TW      # reads _project_groups.default.zh_TW.json

Pass --tailor <JD.txt> to bias bullets toward JD keywords (up to 12 extracted via tech-priority dict + capitalised fallback — see references/tailor-keyword-extraction.md for the strategy and the 12-keyword cap rationale).

Stacks with --persona/--company per references/strategic-resume.md §Axis matrix.

See tests/fixtures/enrich_jobs_sample/ for a reference of what the manifest + prompt + yaml triple looks like.

4b. (Fallback for CI / non-interactive) Spawn claude -p subprocess.

uv run vibe-resume enrich --mode subprocess --locale <L>

This bills against the Anthropic Agent SDK monthly quota pool (Pro $20 / Max 20x $200, separate from the Claude Code subscription quota — change effective 2026-06-15). The CLI prints a red warning at startup.

--mode rule-based skips LLM entirely and uses heuristic summaries (works without any claude binary).

5. Render.

   uv run vibe-resume render -f md  --locale en_US
   uv run vibe-resume render -f all --locale ja_JP     # DOCX = JIS 履歴書 grid
   uv run vibe-resume render --all-locales             # fan out over 10 locales
   uv run vibe-resume render --all-locales --tailor data/imports/jd.txt
   

   Locale resolution chain (same chain applies to enrich):

   1. CLI --locale
   2. profile.preferred_locale
   3. config.render.locale
   4. en_US fallback

6. Review & trend.

   uv run vibe-resume review --jd data/imports/jd.txt   # 8-point scorecard, graded A–F
   uv run vibe-resume trend --locale ja_JP              # ASCII sparkline across all prior runs
   

   Bar is grade B / 80% before sending a draft to a real reviewer.

7. Self-mine the real signals, then iterate truthfully. Don't guess what to strengthen — disclose it:

   uv run vibe-resume evidence --jd data/imports/jd.txt   # what's REALLY there
   uv run vibe-resume iterate --locale en_US              # truth-preserving lift, stops honestly
   

   evidence discloses, per group, the candidate metrics, backed terms, human-gate evidence, and provenance the tool can actually see — so you (the agent) surface only disclosed signals and never invent. iterate tightens the page budget to lift the grade and reports the honest ceiling + human-applied suggestions. The score is a proxy: never trade truthfulness or human-in-the-loop for points — see docs/PRINCIPLES.md.

Interactive Gate Mode (run --interactive / --preset)

For a traceable human-in-the-loop run, run can pause at named gates instead of going end-to-end. Arm a gate set, decide each gate's emitted *.gate.json, then resume with --continue:

uv run vibe-resume run --preset full_review --locales en_US --tailor data/imports/jd.txt
#   ⏸ pauses at the first armed gate, writing data/gates/<Gn>.gate.json
#   edit that file's `decision` (set "choice"; G5 also takes a per-metric `pick`)
uv run vibe-resume run --continue --preset full_review --locales en_US   # advances to the next gate

• Presets: autopilot (no gates — same as no flags) · checkpoints (G1, G2, G8 — the default for a bare --interactive) · full_review (all of G1–G8).
• Gates: G1 freshness · G2 grouping/top_n · G3 overwrite mode · G4 bullets (the enrich emit) · G5 metrics · G6 redaction · G7 variants · G8 acceptance.
• Inspect/replan: uv run vibe-resume gates show / gates plan show the armed set + each gate's recompute suffix. run --resume-from G5 re-runs only that gate's suffix and prints a review-diff. run --branch <Gn> forks an alternative decision (see Quick Reference).
• The run writes data/run_ledger.json so it is replayable; --gates G1,G5,G8 arms an explicit set overriding the preset.

Pitfalls

The full catalogue of failure modes and their fixes — mixed-script locale leaks, --all-locales format quirk, first-run extraction slowness, CJK contact-line wrapping, the claude -p optional fallback, and the privacy rules around profile.yaml / data/imports/ — lives in references/troubleshooting.md.

• personas-compare requires --locale (the enriched cache split per-locale in 0.4.0); see CHANGELOG.
• Truthful-lever guarantees (don't misuse for points): explore and iterate only change layout/selection — they NEVER rewrite a bullet or invent a metric. jd-check --explain is advisory: it shows where a keyword could be grounded but never inserts it. G5 metrics are opt-in per-metric (confirm + pick), and a pick is always intersected with safe_to_surface, so a secret/UI/hash fragment can't be woven even if picked. See docs/PRINCIPLES.md.

Verification

After a full run, confirm:

# 1. Render produced the expected files
ls data/resume_history/ | tail -n 10

# 2. Last review was ≥ B grade
uv run vibe-resume review --locale <L> | tail -n 5

# 3. Trend trending up
uv run vibe-resume trend --locale <L>

For a JD-tailored run, spot-check that the output bullets surface the JD's key nouns:

grep -iE "$(awk '/[A-Z][A-Za-z]{3,}/{print $1}' data/imports/jd.txt | head -5 | paste -sd '|' -)" data/resume_history/resume_v*_<L>.md

For multi-locale batch runs, make sure there's no cross-script leak:

# ja_JP render must not contain Traditional-only Chinese (e.g. 設計, 處理, 實作)
grep -cE "設計|處理|實作|業務" data/resume_history/resume_v*_ja_JP.md    # expect 0 or very low
# zh_CN must not contain Traditional-only characters (e.g. 設, 實, 業)
grep -cE "設|實|業" data/resume_history/resume_v*_zh_CN.md              # expect 0

End-to-end example — "one JD, every market"

# LLM pass per locale
for loc in en_US ja_JP de_DE zh_TW; do
  uv run vibe-resume enrich --tailor data/imports/jd.txt --locale "$loc" -n 3
done

# Batch render
for loc in en_US ja_JP de_DE zh_TW; do
  uv run vibe-resume render -f all --locale "$loc" --tailor data/imports/jd.txt
done

# Review each and show the trend
for loc in en_US ja_JP de_DE zh_TW; do
  uv run vibe-resume review --locale "$loc"
done
uv run vibe-resume trend

Strategic résumé: --company <key> --level <key>

enrich and review accept two extra axes — a named target employer (70 bundled profiles in src/vibe_resume/core/profiles/*.yaml) and a seniority bracket (6 career-level archetypes) — stacked on top of --locale / --persona / --tailor. Block injection order is tailor → persona → level → company. Every apply auto-checks last_verified_at and warns loudly if older than 90 days.

See references/strategic-resume.md for the full axis reference, company list/show/audit/verify/ mark-verified commands, and the drop-in recipe for adding a new employer profile.

Extending the pipeline

When the user asks to add a new extractor, locale, or persona, consult references/extending.md for the Activity schema contract, registration steps, and the "never invent activities" rule.

Useful sibling commands

• uv run vibe-resume status — per-source activity counts
• uv run vibe-resume list-versions — résumé version history from internal git
• uv run vibe-resume diff v001 v002 — diff two versions
• uv run vibe-resume completion zsh --install — shell completion so --locale <tab> expands
• uv run vibe-resume doctor — diagnose CLI/plugin version drift, profile/config presence, optional-dep availability
• scripts/backup_claude_projects.sh (macOS/Linux) / scripts/backup_claude_projects.ps1 (Windows) — back up ~/.claude/projects before Claude Code's 30-day cleanup