job-resume-agent

An evidence-grounded resume tailoring system. Given a job description detected in the browser and a structured experience bank the user has curated, it produces an ATS-readable LaTeX/PDF resume where every bullet is traceable to a piece of user-provided evidence. Any wording the model inferred, generalized, or reworded is visibly flagged and must be approved by the user before export.

The product refuses to fabricate. If a required skill or achievement is not backed by evidence, it says so instead of inventing one.

Status: proof of concept. The repository includes a deterministic MCP server, evidence linker, safe LaTeX renderer, CLI, and tests. It does not yet include the Chrome extension or external LLM provider adapters.

Quickstart

Requires Python 3.11+.

python -m pip install -e ".[dev]"
resume-agent tailor --bank docs/example-experience-bank.yaml --jd path/to/job-description.txt --json
resume-agent tailor --bank docs/example-experience-bank.yaml --jd path/to/job-description.txt --export --out ./out

To run the MCP server over stdio:

resume-agent serve

For private use, point RESUME_AGENT_BANK_PATH at a local bank containing your own information. Keep the filled resume and private bank under .private/; that directory is ignored by Git. The committed example bank uses placeholder contact information and is safe to publish.

Why this exists

Most "AI resume tailoring" tools optimize for keyword match and paragraph fluency at the cost of factual integrity. They will happily add "led a team of 10" or "improved latency by 40%" when the user never claimed either. That is a career risk for the user and a trust problem for the recipient.

This project takes the opposite stance: the model is a retriever and rewriter over the user's own evidence, not a generator of achievements. Tailoring means selecting, reordering, and rephrasing — never inventing. See docs/product-requirements.md for the acceptance criteria that operationalize this.

Product shape

Three components, loosely coupled:

1. Chrome extension. Detects when the active tab is a job description on a supported site (LinkedIn, Greenhouse, Lever, Ashby, Workday, plain text). Offers a Generate Tailored Resume action. Sends the JD to the local backend; opens a review UI when the draft is ready.

2. Local backend + web app. Owns the experience bank, the tailoring pipeline, the evidence matrix, and the LaTeX/PDF renderer. Runs on localhost by default. Ships a review UI: side-by-side diff vs. base resume, evidence links per bullet, keyword coverage heatmap, unsupported-claim warnings, approve/reject/edit per bullet.

3. Provider adapters (BYO account). The user connects their own OpenAI, Anthropic, or local (Ollama) account. We do not proxy through our servers and, in local-first mode, we do not store provider keys in a hosted DB — see docs/security-and-privacy.md.

The public reference LaTeX template lives at templates/resume.template.tex and is used as the visual/structural target for the renderer. Personal identifiers (name, email, links, phone, citizenship) are declared as macros in the template so they can be overridden by a private overlay. The maintainer's own resume source, which contains real PII, lives at .private/alan-resume-source.tex and is git-ignored — treat it as the canonical worked example of what a filled-in template looks like, not as a file that ships with the repository. A minimal experience bank derived from that private source is at docs/example-experience-bank.yaml.

Non-goals (v1)

• No fabrication assist. No "suggest a bullet you might have done." No "fill in a plausible metric."

• No hosted multi-tenant SaaS. MVP is local-first. Cloud sync is deferred (see roadmap).

• No auto-apply. We do not submit applications, message recruiters, or write cover letters. Out of scope.

• No scraping of gated job boards. The extension reads the currently open page in the user's own browser session; it does not crawl.

• No storage of provider API keys server-side in v1. Keys live in OS keychain or extension local storage.

• No résumé "score." Numeric ATS scores are pseudoscience without the ATS in the loop. We show keyword coverage and evidence coverage, not a grade.

• No LinkedIn scraping to seed the experience bank. Import is manual or from user-uploaded PDF/LaTeX only.

Documentation map

Repository layout (target)

job-resume-agent/
├── templates/
│   └── resume.template.tex       # public, PII-free reference LaTeX template
├── .private/                     # git-ignored; maintainer's real resume source lives here
├── README.md
├── CONTRIBUTING.md
├── docs/                         # (this repo, today)
├── apps/
│   ├── extension/                # Chrome MV3 extension (TS)
│   ├── backend/                  # FastAPI (Python) + SQLite
│   └── web/                      # React review UI
├── packages/
│   ├── providers/                # provider adapter interface + impls
│   ├── tailoring/                # retrieval, rewrite, evidence-matrix builder
│   ├── latex/                    # renderer, validator, PDF pipeline
│   └── schema/                   # shared Zod/Pydantic schemas
└── tests/
    └── fixtures/                 # JD samples, expected evidence matrices

Only templates/resume.template.tex, README.md, CONTRIBUTING.md, and docs/ exist today in the tracked tree. .private/ is present in the maintainer's local checkout but is git-ignored and does not ship. The rest is the target of Phase M1 (see roadmap).

Assumptions on record

These are the reasonable assumptions this design makes without asking the user. Change them by opening an ADR.

1. Local-first, single-user MVP. Cloud/multi-user is a v2 concern. See docs/adr/001-local-first-provider-agnostic.md.

2. BYO provider account. We do not front an LLM as a service. The user picks OpenAI, Anthropic, or Ollama; we call it with their credentials.

3. LaTeX is the source of truth for the tailored artifact. PDF is a build product. The user's base template (templates/resume.template.tex, optionally overlaid by a private file such as .private/alan-resume-source.tex) defines the layout envelope.

4. Python backend, TypeScript extension + web UI. Matches the ML/LaTeX ecosystem on the backend and the browser platform on the frontend.

5. Chrome first. Firefox/Safari can follow via MV3 compatibility once the surface is stable.

6. The bank is user-owned. The user can export the full bank as YAML at any time and delete the local DB with one command.

7. Every generated bullet carries provenance. No bullet reaches the PDF without at least one evidence_id reference in the tailored-resume artifact.

Open questions

Tracked in docs/roadmap.md#open-questions.