resume-kit 0.5.0

Resume Intelligence Toolkit — evaluate, match, align, validate, and generate resume materials against jobs.

Resume Intelligence Toolkit (resume-kit)

A reusable, trustworthy system for evaluating, comparing, aligning, generating, and validating resume materials against specific jobs. It answers five core questions:

• Can an ATS reliably parse this resume?
• How closely does this resume match a specific job?
• Which relevant qualifications are missing or poorly represented?
• What truthful changes would improve alignment?
• Did the revised resume actually improve?

Resume-kit is a shared core engine exposed through an agent plugin, an MCP server, a CLI (resume-tool), and a REST API. All interfaces are thin adapters over the same core — no business rule lives only in a route, command, handler, or skill.

Installation

resume-kit ships as a single self-contained wheel that vendors every internal import package. Choose extras for the surface(s) you want:

pip install resume-kit          # core engine + export (base)
pip install "resume-kit[cli]"   # + the resume-tool CLI
pip install "resume-kit[mcp]"   # + the MCP server
pip install "resume-kit[api]"   # + the FastAPI/uvicorn REST API
pip install "resume-kit[all]"   # everything

The base install carries the engine and export third-party runtime dependencies (pydantic, markitdown, pdfminer.six, python-docx, reportlab). Extras add typer (cli), mcp (mcp), and fastapi + uvicorn (api).

The resume-tool command

Installing the cli extra exposes the console script:

pip install "resume-kit[cli]"
resume-tool --help

Deterministic ingest pipeline

Turning a resume/job file into structured JSON is split into deterministic rails around one confined interpretation step, so only the text→schema mapping needs an agent — extraction and validation are mechanical:

resume-tool init                                   # scaffold resume-kit/ + config.json (idempotent)
resume-tool extract-text resume.docx               # deterministic text (docx/pdf/md/txt), no LLM, no network
#  → agent maps the extracted text onto the ResumeDocument schema (the one agentic step)
resume-tool validate-faithfulness \                # HARD GATE: exits non-zero on drift
  --source resume.docx --json resume-kit/resumes/resume-original.json
resume-tool set-active \                            # record active pointer + originating source path
  --resume resumes/resume-original.json --source resume.docx

Text extraction (markitdown, pdfminer.six, python-docx) is bundled in the base install — docx/pdf/md/txt all extract deterministically with no optional extra. validate-faithfulness is a machine gate: it diffs the produced JSON against the source (bullet/section parity, dropped/added tokens, altered high-signal fields, non-ASCII scan) and exits non-zero when the conversion is not faithful, so an unfaithful conversion never silently reaches disk. The resume-kit/ working directory and its config.json (active resume/job pointers, their source paths, and alias_file) are owned by code via init / set-active — not hand-authored.

Building & publishing

Build the umbrella wheel and sdist locally with uv:

uv build            # produces dist/resume_kit-*.whl and dist/resume_kit-*.tar.gz

The wheel vendors all import packages (schemas, core, document-parser, job-parser, ats, matching, policy, evidence, alignment, export, facade, cli, mcp, api, and the job-hunter bridge) via Hatch force-include, so the shipped metadata declares only third-party dependencies — no internal resume-kit-* requirements. The per-package pyproject.toml files remain only for local uv workspace development.

Publishing to PyPI uses Trusted Publishing (OIDC, no long-lived API tokens). The GitHub Actions workflow in .github/workflows/publish.yml builds and publishes on a v* version tag; it is not triggered by ordinary pushes. To cut a release, push a tag:

git tag v0.1.0
git push origin v0.1.0

As a manual fallback (also Trusted-Publishing-friendly), you can upload from a local build with twine:

uv build
python -m twine upload dist/*

Note: resume-kit has not been published to PyPI yet. The commands above describe how a release would be cut once the project is ready.

Status

Early development. Built by selectively porting proven behavior from Resume-Matcher (Apache 2.0) into a clean, modular architecture. Resume-Matcher is a donor codebase and upstream reference, not the product architecture. See references/ for the upstream audit, reuse inventory, and attribution.

Language & distribution

Implemented in Python (the donor codebase and all extractable subsystems are Python: Pydantic models, MarkItDown extraction, LiteLLM providers). Distribution targets PyPI, not npm — see ADR-0001.

Principles

• Deterministic parsing, checks, diffs, and scoring before any LLM reasoning; LLM usage is optional, explicit, and replaceable by local/no-LLM modes.
• Never fabricate employers, titles, dates, accomplishments, metrics, certifications, or experience. The user is the final authority over truth.
• Original resumes are preserved by default; every change ships with a structured diff, claim provenance, and a truth-validation report.

Repository layout

packages/      core, schemas, document-parser, job-parser, matching, alignment,
               evidence, policy, ats, llm, export, cli, mcp, api
plugins/       resume-intelligence agent plugin
integrations/  job-hunter bridge
references/    upstream-audit.md, reuse-inventory.md, attribution.md, ADRs
tests/         fixtures, characterization, unit, integration, evals

License

Apache 2.0. See LICENSE and NOTICE.