Job Application

Create tailored application documents from structured profile data

Create truthful, role-specific application documents from the bundled structured profile or a profile supplied by the user.

Privacy and authorization

• Treat all profile and application data as private.
• Never invent qualifications, employment, education, or contact details.
• The bundled profile is fictional demonstration data and may be replaced by the user.
• Do not submit, email, upload, or message an application without explicit authorization for that specific destination.

Persistent data and inputs

• Profile data: $JOB_APPLICATION_PROFILE_DIR/*.json when configured, otherwise the fictional demonstration data in assets/profile/*.json
• Application output: $JOB_APPLICATION_OUTPUT_DIR when configured, otherwise applications/ in the user's working directory
• Job data: a job.json shaped like examples/job.json
• Templates: assets/templates/

Keep real profiles, photographs, generated documents, and application history outside the installed skill directory. ClawHub updates may replace that directory. Resolve bundled resources relative to this skill directory, but honor the two environment variables for private persistent data. Do not assume a username, home directory, workspace layout, messaging channel, or recipient ID.

Workflow

1. Create the application folder with python3 scripts/prepare_application.py --slug <yyyy-mm-company-role>. Use the printed absolute path for all following files. Never overwrite an existing application directory unless the user explicitly requests reuse.

2. Save normalized job details as job.json.

3. Run scripts/generate_cv_selection.py --job <job.json> --output <cv_selection.json>. Review its deterministic suggestion and adjust only with facts supported by the profile.

4. Create cover_letter.json and indeed_text.json using the example schemas. Keep claims grounded in selected profile records.

5. Generate source documents:

   bash

   python3 scripts/generate_cv.py --selection <cv_selection.json> --output <CV.tex>
   python3 scripts/generate_cover_letter.py --content <cover_letter.json> --output <cover_letter.tex>
   python3 scripts/generate_plain_text.py --content <indeed_text.json> --output <indeed_text.txt>
   

6. If pdflatex is available and PDFs are requested, run the cross-platform renderer:

   bash

   python3 scripts/render_application.py <application-dir>
   

   On Windows, py -3 can be used instead of python3. Linux and macOS users may alternatively use the scripts/render_application.sh wrapper.

7. Return the generated paths. Deliver files externally only after destination-specific approval.

Installation requirements and platform-specific commands are documented in README.md. Generated sample PDFs using only fictional data are in examples/generated/.

Content policy

• Default to a one-page CV.
• Use at most five skills, three experience entries, two education entries, one project, and three languages.
• Prefer evidence relevant to the posting over generic claims.
• Keep each experience entry to at most two concise achievement sentences.
• Preserve optional project attribution fields (organization, context, and context_label) so generated CVs can distinguish employer projects from work performed through the candidate's own company.
• Omit date of birth, signature image, and full street address by default. Include a profile photograph only when the user requests one or the supplied profile explicitly declares profile_picture; the bundled photo is fictional demonstration material.
• Match the job's language unless the user requests another language.

JSON contains content, Python performs deterministic transformation, and LaTeX handles layout. Do not hand-edit generated LaTeX when the source JSON can be corrected instead.