setup-profile

name	setup-profile
description	Build profile.json from your CV or LinkedIn PDF by extracting work history, generating 3 CV variants, and writing per-job bullets automatically

setup-profile

Bootstrap profile.json from a CV, resume, or LinkedIn PDF export. Extracts your work history, infers three audience-specific CV variants (AI/LLM, Data Platform, Backend), and writes one default bullet set plus three audience-specific variants so you are ready to generate tailored applications immediately.

Triggers

User says: /setup-profile, /career-agent:setup-profile, set up my profile, build my profile from my CV, import my resume, create profile.json

In Codex, invoke this skill with $setup-profile, the skills/plugin selector, or natural language. Slash-command examples are Claude Code aliases, not Codex built-ins.

Arguments

/setup-profile [optional: path to PDF or DOCX]

No argument required: the agent accepts a file upload or pasted text in the same message.

Inputs (priority order)

1. LinkedIn PDF export: most structured source. Export via LinkedIn → Me → Settings & Privacy → Data privacy → Get a copy of your data → select "The works" or use the browser print-to-PDF on your profile page.
2. CV or resume PDF / DOCX: uploaded directly to the conversation.
3. Plain text CV: pasted directly into the chat.
4. Nothing provided: the agent asks: "Please paste your CV text or upload your CV/LinkedIn PDF and I'll build your profile.json."

Steps

1. Accept and validate input

Check what the user provided:

• If a file is attached, read it.
• If text is pasted, use it.
• If neither, prompt the user and stop until they provide input.

Do not proceed with fabricated data. Every field written to profile.json must come from the source material or be explicitly flagged as an assumption.

2. Parse: extract structured data

Extract the following from the CV or LinkedIn export:

Contact info:

• Full name (split into first, last, preferred)
• Email address
• Phone number (extract country code if present; default to +1 only if location is clearly US/CA, otherwise leave blank and flag)
• Location (city, country)
• Relocation preference (infer from "open to relocation" language; leave blank if not stated). This is ATS form metadata, not the CV banner — keep it factual ("Yes, willing to relocate" or similar).
• Openness banner (shown under the headline on the generated CV). If the source mentions remote availability or relocation interest, summarise into a one-sentence banner; otherwise default to "Open to fully remote roles globally and relocation for the right opportunity.".
• LinkedIn URL
• GitHub URL
• Personal website / portfolio URL
• Twitter / X URL
• Blog URL

Professional summary:

• Total years of experience (calculate from earliest role start date to today; state the calculation explicitly)
• Current or most recent headline / job title
• Top-level summary paragraph if one exists

Work history: for each role:

• Job title
• Company name
• Industry / team (if stated)
• Location
• Start date and end date (or "Present")
• All bullet points or responsibility statements verbatim

Education: for each entry:

• Degree
• Institution
• Graduation year

Skills: capture all listed skills, grouping them as they appear (Languages, Frameworks, Cloud & Infra, etc.)

3. Infer CV variants

Analyse the parsed work history and skills to determine which of the three audience variants is the strongest primary fit:

• Variant A: AI/LLM/Evaluation: experience with ML, LLMs, model evaluation, alignment, AI safety, embeddings, RAG, prompt engineering, evals frameworks, Weights & Biases, MLflow
• Variant B: Data Platform/Pipelines: experience with data engineering, ETL/ELT, Airflow, Spark, dbt, Kafka, data warehouses, AWS Glue, Redshift, BigQuery, pipeline orchestration
• Variant C: Senior Backend/APIs: experience with REST/gRPC APIs, microservices, distributed systems, backend frameworks (FastAPI, Django, Flask, Express, Go), open-source contributions, developer tooling

For each variant, produce:

• label: short audience label (e.g. "AI/LLM/Evaluation")
• headline: 1-line headline tailored to that audience, ≤ 12 words
• summary: 2-3 sentence paragraph: lead with years of experience, name the most relevant technical stack for that audience, close with availability/location preference

If a variant fit is ambiguous (e.g. no clear AI or data experience), say so: "I could not find strong evidence for Variant A (AI/LLM). I've written a best-effort headline and summary: please review and adjust."

4. Write per-job bullets × 4

For every role in experience, produce four bullet sets:

• default: neutral, achievement-focused bullets suitable for any audience. 3-5 bullets per role.
• A: same role reframed for AI/LLM/Evaluation audiences: emphasise data quality pipelines that fed models, evaluation logic, experiment tracking, inference infra, or anything ML-adjacent. If the role has no AI angle, write the closest honest approximation and note it.
• B: reframed for Data Platform audiences: emphasise pipeline reliability, data volumes, orchestration complexity, schema design, warehouse optimisation, SLA adherence.
• C: reframed for Backend/APIs audiences: emphasise API design, latency, throughput, service reliability (uptime/SLA), open-source contribution, developer experience.

Rules:

• Never invent facts not in the source material.
• It is acceptable to emphasise different aspects of the same fact for different audiences.
• If a bullet truly has no honest angle for a variant, write the default bullet and note the assumption in the report.
• Each bullet should begin with a strong past-tense verb (Built, Designed, Reduced, Led, Migrated, Shipped, etc.).
• Aim for 3-5 bullets per role per variant; 2 is acceptable for short-tenure or early-career roles.

5. Check before overwriting

Before writing profile.json:

If profile.json already exists, ask:

profile.json already exists. Overwrite it? (yes/no)

If the user says no, stop. If yes, proceed.

If profile.json does not exist, proceed without asking.

6. Write profile.json

Write a complete, valid JSON file to profile.json in the project root. The file must match the exact schema from profile.example.json:

{
  "name": { "first": "...", "last": "...", "preferred": "..." },
  "email": "...",
  "phone": { "country_code": "+X", "number": "...", "formatted": "+X..." },
  "location": "City, Country",
  "relocation": "...",
  "openness": "Open to fully remote roles globally and relocation for the right opportunity.",
  "links": {
    "linkedin": "...",
    "github": "...",
    "website": "...",
    "twitter": "...",
    "blog": "..."
  },
  "years_experience": 7,
  "headline": "...",
  "summary": "...",
  "variants": {
    "A": { "label": "...", "headline": "...", "summary": "..." },
    "B": { "label": "...", "headline": "...", "summary": "..." },
    "C": { "label": "...", "headline": "...", "summary": "..." }
  },
  "experience": [
    {
      "id": "job_1",
      "title": "...",
      "company": "...",
      "company_line": "Company · Industry · Location · Start – End",
      "bullets": {
        "default": ["..."],
        "A": ["..."],
        "B": ["..."],
        "C": ["..."]
      }
    }
  ],
  "education": [
    { "degree": "...", "institution": "...", "year": "..." }
  ],
  "skills": [
    { "label": "Languages", "items": "Python · TypeScript · ..." }
  ]
}

Do not include the _comment field from the example: it is only for the template.

If a field cannot be populated from the source (e.g. GitHub URL not found), write an empty string "" and flag it in the report.

Roles must be listed in reverse chronological order (most recent first). Assign IDs job_1, job_2, ... starting from the most recent.

7. Report

After writing profile.json, output a structured report:

profile.json written successfully.

Extracted:
  Name:             Jane Doe
  Email:            [e-mail]
  Phone:            [phone]
  Location:         Berlin, Germany
  LinkedIn:         https://www.linkedin.com/in/janedoe/
  GitHub:           [not found: update manually]
  Years experience: 7 (calculated from 2017-06 to 2024-06)
  Roles parsed:     3 (job_1, job_2, job_3)
  Education:        1 entry
  Skills groups:    3

Variant fit assessment:
  Primary:   C: Senior Backend/APIs (strongest signal: 5 backend API roles)
  Secondary: B: Data Platform (evidence: 2 years data pipeline work at Acme)
  Tertiary:  A: AI/LLM (limited evidence: mentioned LLM integration in job_1 bullets)

Assumptions made:
  - Phone country code defaulted to +44 based on location (London, UK)
  - Relocation field left blank: no mention in source
  - Variant A summary is best-effort: no dedicated ML/AI role found

Fields left blank (update manually):
  - links.github
  - links.twitter
  - links.blog

8. Offer edit loop

After the report, ask:

Tell me what to adjust: for example:
  - "Rewrite job_1 bullets for AI roles"
  - "Update my LinkedIn URL to https://..."
  - "Make Variant B the primary"
  - "Add a GitHub URL: https://github.com/..."

Apply any requested edits and re-write profile.json. Repeat until the user is satisfied or says "done" / "looks good".

Evidence standard

• Never invent facts not present in the CV or LinkedIn export.
• State assumptions explicitly in the report (e.g. "I inferred 7 years experience from date ranges 2017–2024").
• Flag ambiguity rather than guess silently (e.g. "The source lists 'machine learning' but no specific models or frameworks: I've placed this under Variant A with low confidence").
• If a URL is partially visible (e.g. linkedin.com/in/jane) but not fully qualified, write what you can and flag it.

Error handling

• No input provided → ask the user for their CV or LinkedIn PDF before proceeding.
• File cannot be read (corrupt PDF, unsupported format) → tell the user and ask them to paste the text instead.
• profile.json exists and user declines overwrite → stop cleanly: "No changes made. Your existing profile.json is untouched."
• Variant fit completely unclear → write best-effort variants and flag every uncertain field in the report.
• Skills section is missing from source → write an empty skills array and note it: "No skills section found: add your skills manually."