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Build Jobscan

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Saved on this device only. Tick prerequisites first, then work the phases in order · do not start one until the checks above it pass.

Build a transparent resume-to-job comparison with evidence-based edits.

estimated effort multi-daythe files for this build are in the project pack

RuntimePython 3.12+ and FlaskStorageSQLite through Python sqlite3InterfaceJinja HTML templates, CSS and vanilla JavaScriptVerificationPython unittest and browser acceptance fixtures

Before step 1

Everything below is assumed from the first step. Tick each one when you actually have it, not when you plan to.

  1. installPython is free; this local prototype requires no hosting subscription.

    Why The local backend, tests and storage use Python.

    Get it Install Python from python.org. Open a terminal in a new empty project folder. On Windows, use py -3 where this guide says python if python is not on PATH; on macOS/Linux use python3 if needed. Create .venv and activate it before installing packages. open ↗

    Verify python --version reports 3.12+ and python -c "import sqlite3" succeeds.

  2. installNo paid service required for this local step.

    Why You need to view the local interface and edit generated project files.

    Get it Use an installed Chrome, Firefox or Edge browser. Open the empty project folder in your editor; open the terminal there, not in this catalog repository.

    Verify You can create a text file in the project folder and open browser developer tools.

  3. have readyNo paid service required for this local step.

    Why This fixture makes the first end-to-end check reproducible.

    Get it Create synthetic sample data before implementation: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Save it under fixtures/ with the expected results in fixtures/README.md; use no real credentials or customer records.

    Verify The fixture files and expected outcomes exist and can be inspected without a vendor account.

  4. decideNo paid service required for this local step.

    Why The full commercial product is larger than this guide.

    Get it The deliverable is a transparent resume-to-job comparison with evidence-based edits. Read every exclusion below. Indie mode runs locally; any public or shared use requires the product access and deployment gates first.

    Verify You can name the primary workflow and the vendor features this version deliberately leaves out.

Data model

Create these before the first phase that stores anything. Changing a table later is the expensive kind of change.

- resumes(id,title,text)
- jobs(id,title,text)
- terms(id,job_id,term,weight,source_span)
- matches(resume_id,term_id,spans_json)
- suggestions(id,resume_id,claim,evidence_span,status)
- Use stable ids and explicit relationships; add auxiliary tables named by the phases before those phases use them.
- Acceptance fixture: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence.

Environment variables

These go in a .env file the app reads at startup. The pack's .env.example is this table as a file · copy it, never commit the filled-in version.

VariableNeededExampleWhere the value comes from
DATA_DIRrequired./data/jobscanA new private folder this application creates on first run; never serve it as static files.
SECRET_KEYsecretrequiredreplace-with-a-random-secretGenerate with python -c "import secrets; print(secrets.token_hex(32))" and save only in .env.
APP_MODErequiredindieUse indie for loopback-only personal work. Product mode must refuse to start until owner login and the deployment gates are configured.
SITE_URLrequiredhttp://127.0.0.1:5000The origin used for local forms and links; replace with the chosen HTTPS origin in product mode.

The build, in order

  1. Local foundation and data contract

    Create the runnable local foundation for a transparent resume-to-job comparison with evidence-based edits, including the real schema and acceptance fixture.

    1. Run the venv command first, then stop and activate it before running the remaining commands. Use your OS instructions: Windows .venv\Scripts\activate.bat in cmd, or .venv\Scripts\Activate.ps1 in PowerShell; macOS/Linux source .venv/bin/activate. Then install the listed packages. Create app.py exposing app, load .env with python-dotenv, and add a /healthz route. Freeze tested dependency versions to requirements.txt. Create .env manually from .env.example; replace SECRET_KEY before launch.

      Files app.pyrequirements.txt.env.example.gitignore

      terminal
      python -m venv .venv
python -m pip install Flask Flask-WTF python-dotenv waitress tzdata
python -m pip freeze > requirements.txt
python -m flask --app app run --host 127.0.0.1

    2. For database-backed state create schema.sql and db.py using this model: resumes(id,title,text); jobs(id,title,text); terms(id,job_id,term,weight,source_span); matches(resume_id,term_id,spans_json); suggestions(id,resume_id,claim,evidence_span,status). Add the auxiliary relationships explicitly used in later phases; use stable ids, foreign keys, parameterized values and transactions. For canonical files use atomic replacement and hashes instead. Implement python manage.py init and python manage.py seed to create only synthetic data: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Re-running init must not erase data.

      Files schema.sqldb.pymanage.pyfixtures/README.md

      terminal
      python manage.py init
python manage.py seed

    3. Install Flask-WTF CSRF protection for forms and same-origin fetches, validate Host and Origin against SITE_URL, bound inputs and escape output. Deliberately public intake endpoints get bounded validation and rate limits; signed provider webhooks use signature checks instead of browser CSRF tokens. All other admin routes stay local until product authentication exists. When a later phase tests viewer/member permissions, implement the users, hashed passwords, expiring server sessions and role checks in this foundation first; product mode must never use a fabricated identity from request input. Add error, empty, keyboard-focus and retry states now.

      Files app.pytemplates/base.htmltest/test_foundation.py

    done when · tick each as it passes
    watch out
    • The Flask development server is for local development only. Loopback binding alone does not replace CSRF and origin validation.
    next phase

  2. Text and requirements

    Text and requirements for Jobscan: Accept pasted plain text for a resume and a job; preserve original files/text and normalize a working copy.

    1. Accept pasted plain text for a resume and a job; preserve original files/text and normalize a working copy.

      Files features/text_and_requirements.pytemplates/text_and_requirements.html

    2. Extract visible terms with source spans and allow the user to include, exclude or reweight each term.

      Files features/text_and_requirements.py

    3. Use A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Check: Every scored term links to the exact job sentence that introduced it. Then check: An empty or image-only input receives a readable error before scoring. Add a focused regression for the failure case under test/ using a temporary database or folder and fake external services; also perform the visible workflow in a browser.

      Files test/test_text_and_requirements.py

      terminal
      python -m unittest discover -s test
    done when · tick each as it passes
    watch out
    • A keyword overlap score is not an ATS vendor score or a hiring probability.
    next phase

  3. Explainable comparison

    Explainable comparison for Jobscan: Calculate weighted coverage and show matched resume spans plus missing terms; cap repeat occurrences at one match.

    1. Calculate weighted coverage and show matched resume spans plus missing terms; cap repeat occurrences at one match.

      Files features/explainable_comparison.pytemplates/explainable_comparison.html

    2. Offer structure checks for headings and dates without inferring personal characteristics or inventing qualifications.

      Files features/explainable_comparison.py

    3. Use A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Check: Repeating Python 100 times does not improve coverage beyond one occurrence. Then check: SQL remains missing when no evidence exists in the resume. Add a focused regression for the failure case under test/ using a temporary database or folder and fake external services; also perform the visible workflow in a browser.

      Files test/test_explainable_comparison.py

      terminal
      python -m unittest discover -s test
    done when · tick each as it passes
    watch out
    • Substring matching must not treat Java as JavaScript experience.
    next phase

  4. Truthful revision workspace

    Truthful revision workspace for Jobscan: Let the user attach evidence to a suggested bullet and view a before/after diff before accepting.

    1. Let the user attach evidence to a suggested bullet and view a before/after diff before accepting.

      Files features/truthful_revision_workspace.pytemplates/truthful_revision_workspace.html

    2. Export the revised text and a JSON explanation report with weights, excluded terms and evidence mappings.

      Files features/truthful_revision_workspace.py

    3. Use A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Check: A proposed SQL-experience bullet cannot be accepted without user-supplied evidence. Then check: Rejecting a suggestion leaves the original resume byte-for-byte unchanged. Add a focused regression for the failure case under test/ using a temporary database or folder and fake external services; also perform the visible workflow in a browser.

      Files test/test_truthful_revision_workspace.py

      terminal
      python -m unittest discover -s test
    done when · tick each as it passes
    watch out
    • Never fabricate numbers, degrees, titles or experience to raise a score.
    next phase

  5. Portable delivery and clean-start proof

    Finish the Jobscan replacement with a documented first-run path and a recoverable export.

    1. Document prerequisites, every configuration variable, data locations and the three domain workflows: Text and requirements; Explainable comparison; Truthful revision workspace. Include the exclusions, actual costs and source check date 2026-09-05. Commands shown earlier that reference project files are files the builder must implement before running them.

      Files README.md.env.example

    2. Use sqlite3.Connection.backup for the database in DATA_DIR and pause writers while taking the matching attachment snapshot. Keep schema version, hashes and a manifest for jobscan. Restore into a separate empty directory or isolated deployment, with all outbound actions and schedules disabled. Recreate this proof: A job requesting Python and SQL; a resume showing Python experience but no SQL evidence. Compare ids/counts and open the result before recording the restore date. Never test a restore over the only live copy.

      Files OPERATIONS.mdscripts/backup.py

    3. Use a new folder and only README instructions to reach the fixture outcome. Install from requirements.txt, initialize storage, run the tests and exercise the UI at 375px and 1280px; use keyboard-only controls and inspect browser errors. Record exactly what passed; do not mark unrun checks complete.

      Files README.mdverification.md

      terminal
      python -m pip install -r requirements.txt
python -m unittest discover -s test
    done when · tick each as it passes
    watch out
    • Export without an import or restore rehearsal is not verified portability.
what this build does not replace
after v1, if you want it

Need the files? The project pack on the verdict page hands your agent the whole brief · more career.