Cv Creator

CV Creator

Use this skill for repo-local career materials. Keep the workflow shared, but keep the person's actual career facts inside the current repo.

Repo contract

Durable career memory and disposable tailored packets are kept in two different places on purpose:

• Durable (tracked, committed): the career root holds the source of truth — base templates, profile facts, and tailoring rules. The skill auto-detects the career root in this order based on existing files: memory/areas/career/, then memory/areas/builder/career/. Use --career-root <path> when a repo intentionally has multiple career areas.
• Disposable (gitignored, throwaway): per-company tailored packets are renderings of that canon for one application, not memory. The CLI writes them under the repo's tmp/ so they are never committed and clear out when the application is sent. Do not move tailored packets back into the tracked career root.

Expect this layout unless the repo clearly documents a different one:

• <career-root>/overview.md
• <career-root>/profile.md
• <career-root>/tailoring-guide.md
• <career-root>/job-tracker/
• <career-root>/cv/latex/.gitignore
• <career-root>/cv/latex/base/resume.tex
• <career-root>/cv/latex/base/cover-letter.tex
• <repo-root>/tmp/cv/tailored/<company>/job-description.md (disposable)
• <repo-root>/tmp/cv/tailored/<company>/resume.tex (disposable)
• <repo-root>/tmp/cv/tailored/<company>/cover-letter.tex (disposable)

The CLI resolves these paths for you — call cv.py init/build/review --company <slug> and let it place files. cv.py clean removes the disposable tmp/cv/ tree (and the render temp) when the work is done. If the durable contract (base templates, profile, tailoring guide) is missing, create that minimal structure first instead of improvising files in random places.

Read references/structure-contract.md when you need the exact folder expectations or migration pattern.

Read references/template-patterns.md when you need to understand or modify the LaTeX template's structure, preamble, custom commands (\role, \labelrow, \sep), spacing system, or section conventions. This is the canonical reference for "how does a base resume look in this skill" and "how do I add a new section without breaking the rhythm."

Core workflow

1. Read <career-root>/profile.md first for source-of-truth career facts.
2. Read <career-root>/tailoring-guide.md if tailoring or summary changes are needed. Pay particular attention to the "Competitor awareness" section, which governs how to handle product-level references to the target company's rivals (e.g. do not send an Anthropic application full of Codex name-drops).
3. If a tailored packet is already in progress, read its tmp/cv/tailored/<company>/job-description.md first when reviewing or continuing it.
4. If the tailored JD snapshot does not exist yet, read the relevant JD from <career-root>/job-tracker/ or the user-provided source, then store a frozen copy beside the tailored files as tmp/cv/tailored/<company>/job-description.md so later review is self-contained.
5. When starting a new tailored packet, initialize it with cv.py init --company <slug> instead of manually copying files. The CLI writes it into the disposable tmp/cv/tailored/<company>/.
6. Keep the base LaTeX files in cv/latex/base/ clean and generic.
7. Tailored, company-specific versions live in the disposable tmp/cv/tailored/<company>/; never commit them into the tracked career root. Run cv.py clean when the application is sent.
8. Never invent experience, skills, tools, certifications, customers, or outcomes.
9. Compile and visually review before calling the output done.
10. Before committing the final PDF, run a competitor-awareness grep against the target's rival products and companies. See the "Competitor awareness" section of <career-root>/tailoring-guide.md for the current map and the audit checklist.

Build and review

Use the bundled script when possible:

python3 ~/GitHub/agents/skills-source/owned/cv-creator/scripts/cv.py init --company synthesia
python3 ~/GitHub/agents/skills-source/owned/cv-creator/scripts/cv.py init --company synthesia --no-input --json
python3 ~/GitHub/agents/skills-source/owned/cv-creator/scripts/cv.py build --kind resume
python3 ~/GitHub/agents/skills-source/owned/cv-creator/scripts/cv.py build --kind resume --company synthesia
python3 ~/GitHub/agents/skills-source/owned/cv-creator/scripts/cv.py review --kind resume --company synthesia
python3 ~/GitHub/agents/skills-source/owned/cv-creator/scripts/cv.py build --kind resume --company synthesia --no-input --json
python3 ~/GitHub/agents/skills-source/owned/cv-creator/scripts/cv.py review --kind resume --company synthesia --no-input --json
python3 ~/GitHub/agents/skills-source/owned/cv-creator/scripts/cv.py clean

The script expects to run from somewhere inside the target repo and will discover the repo root automatically. JSON is the default contract. Use --plain only for quick operator inspection. The CLI also accepts --no-input and --version. cv.py review is the self-contained review path for this skill: it finds the repo-local compiled PDF for the requested kind and optional company, renders per-page PNGs into a temp review directory, and returns the source path, PDF path, review directory, and page image paths in JSON. The PDF is the primary review artifact. The colocated job-description.md is role-fit context for the review, not a separate review mode. This is the intended loop for both the main agent and any spawned reviewer/subagent.

Tailoring rules

• Start a new tailored version with cv.py init --company <slug>, which copies the current base files and creates a colocated job-description.md placeholder. Do not reformat from scratch. The base carries the canonical preamble, custom commands, spacing system, section ordering, and capitalization conventions; tailoring is content work, not template work.
• Change the summary first. Sentence 1 should name the identity the role is hiring for. Sentence 2 should state what you want to do next, framed in the role's vocabulary.
• Reorder Experience bullets to surface role-relevant signals first. Customer-facing or domain-specific bullets should appear in position 1 or 2, not buried at the bottom.
• Prefer emphasis shifts over content expansion. Reword and reorder before adding new claims.
• Trim the Skills lines per category so each fits on one visual line. Wrapping reads as keyword stuffing.
• The Relevant Public Work section is OPTIONAL and role-dependent. Keep it for roles that explicitly value public teaching, community contribution, technical writing, developer advocacy, or forward-deployed engineering. Remove it for formal corporate or non-public-facing roles.
• Save tailored versions under the disposable tmp/cv/tailored/<company>/ rather than overwriting the base. Never commit them into the tracked career root.
• Store the exact role snapshot as tmp/cv/tailored/<company>/job-description.md in the same folder as the tailored resume and cover letter.
• The colocated JD snapshot is the audit artifact for that tailored packet. job-tracker/ can still hold broader search notes, intake, or discovery links, but the reviewer should not have to leave the tailored folder to see what the resume was tailored against.
• Keep cover letters alongside the tailored resume for the same company.
• If the user has only raw notes, normalize them into profile.md before heavy tailoring.
• Never invent experience, skills, tools, certifications, customers, or outcomes.
• Competitor awareness: before compiling, audit the .tex files for prominent references to the target company's direct rivals. Do not send a resume that reads as a love letter to the target's competitor. See the repo-local <career-root>/tailoring-guide.md "Competitor awareness" section for the current competitor map, rewrite strategies, and audit checklist. Reframe product-level name-drops into category-level language (agent harness, coding agent, LLM application engineering) when the target is a rival's rival.

For the full structural and formatting rules, read references/template-patterns.md.

Visual checks

After compiling, render and inspect the PDF pages. Review the PDF itself, not extracted text.

Quick checks:

• clipped text
• overlapping elements
• inconsistent spacing
• awkward page breaks
• dates drifting out of alignment
• obvious underfilled second pages
• orphaned single-line fragments at the top of page 2
• links that do not look clickable because the link color is too dark

For a full audit, including typography, spacing, color, and a scoring rubric that can be run by a reviewer or a sub-agent, read references/visual-review-heuristics.md. Use that file when polishing a tailored resume for a specific role or when the user is not happy with how the resume looks but cannot name the issue.

When the resume is tailored for a specific role, review the PDF against the colocated job-description.md with two lenses:

• visual quality from the rendered pages
• role fit from what the PDF actually surfaces on page 1 and page 2

Do not run a separate primary "text review" workflow. Use the PDF as the single source of review truth, then edit the .tex only after issues are identified.

Pre-submission audit (run before every batch submit)

When multiple tailored packs are ready for submission, run a cross-pack audit before the human clicks submit. This catches mistakes that per-pack review misses (wrong company name leaked from a copy-paste, competitor name-drops that slipped through, contact info drift).

Audit checklist (grep across all tmp/cv/tailored/*/resume.tex and tmp/cv/tailored/*/cover-letter.tex)

1. Wrong company name: does each file reference only its own target company? A Cresta cover letter must not say "Synthesia."
2. Competitor name-drops in prose: grep for product-level rival names. Flag any occurrence NOT inside a literal quoted blog title or URL slug. The competitor map lives in <career-root>/tailoring-guide.md "Competitor awareness" section. The examples below are AI-market defaults; extend or replace them for non-AI career tracks:
   • Applying to Anthropic: no OpenAI, Codex, ChatGPT, GPT-4, GPT-5
   • Applying to OpenAI: no Anthropic product names
   • Applying to Mistral/Cohere/HuggingFace: no rival assistant or GPT product names
   • Applying to Cognition/Poolside/Cursor: no other coding-agent product names
   • Applying to Synthesia: no HeyGen, D-ID, Runway, Pika
   • Applying to Speechmatics: no Deepgram, AssemblyAI, Whisper
   • Applying to Deepgram: no Speechmatics, AssemblyAI, Whisper
   • General: category-level language ("frontier coding agents", "voice AI platform") is always safe
3. Em-dashes: grep for ---, \textemdash, —, – in visible prose (not LaTeX comments). Zero tolerance.
4. AI-smell words: grep for delve, underscore, pivotal, realm, harness (as verb/metaphor), illuminate, facilitate, shed light, bolster, differentiate, streamline. Zero tolerance.
5. Contact info: every resume must use the candidate name, email, phone, location, and work-authorization facts from <career-root>/profile.md or the repo-local canonical career notes. Do not copy another person's contact details across repos.
6. Page count: every resume PDF must be exactly 2 pages (not 1, not 3). Check with pdfinfo.
7. Sign-off: every cover letter must end with "Thanks," (not "Sincerely," or "Best regards," or "Warm regards,").
8. Invented content: spot-check 5-6 packs for claims not supported by <career-root>/profile.md. Tools, customers, certifications, outcomes, revenue figures must all trace back to profile.md.

How to run

# Disposable tailored packets live under the repo's tmp/ (run from repo root)
TAILORED=tmp/cv/tailored

# Competitor names (adjust per target)
grep -rn "Codex\|ChatGPT\|GPT-4\|GPT-5\|OpenAI\|Anthropic\|Cursor\|Devin\|Windsurf\|Poolside\|Deepgram\|Speechmatics\|AssemblyAI\|Whisper" "$TAILORED"/*/resume.tex "$TAILORED"/*/cover-letter.tex

# Em-dashes
grep -rn "textemdash\|—\|–" "$TAILORED"/*/resume.tex "$TAILORED"/*/cover-letter.tex | grep -v "^%"

# AI-smell
grep -rni "delve\|underscore\|pivotal\|realm\|illuminate\|facilitate\|shed light\|bolster\|differentiate\|streamline" "$TAILORED"/*/resume.tex "$TAILORED"/*/cover-letter.tex

# Sign-off
grep -rn "Sincerely\|Best regards\|Warm regards\|Kind regards" "$TAILORED"/*/cover-letter.tex

# Page count
for f in "$TAILORED"/*/resume.pdf; do echo "$f: $(pdfinfo "$f" 2>/dev/null | grep Pages)"; done

Report format

### CRITICAL (fix before submit)
- [wrong company, competitor name-drops in wrong context, invented content]

### WARNING (cosmetic)
- [em-dashes in comments, template residue, borderline framings]

### CLEAN
- [packs that passed everything]

### STATS
Total checked / competitor violations / em-dash / AI-smell / contact / sign-off / page count / invented

Lesson from 2026-04-15 batch

The mistral-fde resume was tailored in the first wave before the competitor-awareness rule was established. It named "OpenAI, and Anthropic" in an AIP experience bullet sent TO Mistral. This was caught in the post-submission audit but could not be unsubmitted. The rule: always run the competitor grep BEFORE submitting, not after.

Output hygiene

Track only the durable base/ .tex templates, profile.md, tailoring-guide.md, and job-tracker/ notes. Ignore generated PDFs and LaTeX build artifacts via <career-root>/cv/latex/.gitignore. Tailored per-company packets are disposable and live under the repo's gitignored tmp/cv/ — never commit them; run cv.py clean when the application is sent.

Submitting applications via browser

When the user asks you to actually submit a compiled CV to a web form, use the bundled browser tooling available in the active Codex session. Prefer the Codex in-app Browser for ordinary public forms and Chrome when the task needs the user's normal profile, cookies, extensions, or logged-in state.

Workflow notes

• Combobox selection is usually a two-step dance: set or type the value, then click the matching option after the menu appears.
• Before every final submit action, take a screenshot and show it to the user. Submission is irreversible.
• After submission, capture a confirmation screenshot as evidence and note the result in the relevant tracker file (for example <career-root>/job-tracker/<batch>/STATUS.md).
• If the form has many essay questions, draft all answers before opening the form and fill them in a single pass. Reviewing drafts in source is easier than scrolling a populated form.
• Browser element identifiers can change after DOM updates such as combobox selection, radio clicks, and file uploads. Re-read the page state before each batch of interactions and verify filled content before submission.
• Ashby has invisible reCAPTCHA and will flag automated submissions as spam. The form will appear to submit successfully, then replace itself with "Your application submission was flagged as possible spam. If you believe this was a mistake, please submit your application again." The reCAPTCHA token is generated from user gesture signals (mouse movement, genuine clicks, focus patterns) that programmatic fills do not produce. Retry does not help. Presence of textarea[name=g-recaptcha-response] on the form is the tell.
  • Workaround: fill everything, verify the filled state in a screenshot, then hand the already-filled form to the human and ask them to click Submit.
  • Do not close the browser window after filling. The human needs the populated tab still open to click Submit.
  • Greenhouse and Lever do not generally trip this. Ashby is the problem surface.