Jobautopilot Tailor

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name: jobautopilot-tailor description: Tailors your resume and cover letter to a specific job description. Fetches the JD, rewrites bullet points to match keywords, and exports polished .docx files — 100% based on your real experience, nothing invented. Picks up shortlisted jobs from jobautopilot-search and hands resume_ready entries to jobautopilot-submitter. author: jerronl version: "1.3.0" homepage: https://github.com/jerronl/jobautopilot tags: - resume - cover-letter - docx - job-search - career requires: tools: - web_search - browser python_packages: - python-docx - lxml env: - RESUME_DIR - RESUME_OUTPUT_DIR - RESUME_TEMPLATE - MD_TO_DOCX_SCRIPT - JOB_SEARCH_TRACKER - USER_FIRST_NAME - USER_LAST_NAME - USER_EMAIL - USER_PHONE - USER_LINKEDIN bins: - python3 metadata: clawdbot: emoji: "📄" requires: env: - RESUME_DIR - RESUME_OUTPUT_DIR - RESUME_TEMPLATE - MD_TO_DOCX_SCRIPT - JOB_SEARCH_TRACKER - USER_FIRST_NAME - USER_LAST_NAME - USER_EMAIL - USER_PHONE - USER_LINKEDIN bins: - python3 pip: - python-docx - lxml files: - scripts/md_to_docx.py

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Job Autopilot — Resume Tailor

Produces a tailored resume and cover letter for each shortlist job in the tracker. Delivers .docx files ready to attach and send.

Core principles

1. 100% truthful — never invent experience, inflate metrics, or fabricate credentials. 2. Resume content comes from the user's original files first — always read $RESUME_DIR before writing anything. 3. md before docx — complete markdown drafts for ALL shortlisted jobs first, then convert to docx in batch. Do not interleave md writing and docx conversion. 4. One job at a time for reporting — finish and report each job's result before moving to the next. 5. No silent spinning — if a job cannot be reliably completed within 30 minutes, mark it error with a clear reason and move on.

Setup

Add to ~/.openclaw/workspace/job_search/config.sh:

export RESUME_DIR="$HOME/Documents/jobs/"           # your original resume files live here
export RESUME_OUTPUT_DIR="$HOME/Documents/jobs/tailored/"  # where tailored files are saved
export RESUME_TEMPLATE="$HOME/.openclaw/workspace/job_sub_agent/scripts/sample_placeholders.docx"
Download template: https://github.com/jerronl/jobautopilot/raw/main/jobautopilot-tailor/scripts/sample_placeholders.docx
export MD_TO_DOCX_SCRIPT="$HOME/.openclaw/workspace/job_sub_agent/scripts/md_to_docx.py"
export JOB_SEARCH_TRACKER="$HOME/.openclaw/workspace/job_search/job_application_tracker.md"
export USER_FIRST_NAME="Your"
export USER_LAST_NAME="Name"
export USER_EMAIL="your@email.com"
export USER_PHONE="+1-555-000-0000"
export USER_LINKEDIN="https://linkedin.com/in/yourprofile"
mkdir -p "$RESUME_OUTPUT_DIR"

Session start

Read in order: 1. $RESUME_DIR — understand the user's full experience and skills 2. $JOB_SEARCH_TRACKER — find all shortlist entries to process

JD fetch order

For each shortlist job: 1. Use the exact URL from the tracker 2. Try web_search first to extract job responsibilities, skills, keywords, asset classes 3. If web_search returns no useful JD, use browser to open the URL directly 4. If the URL is broken, a generic careers page, or wrong role → mark tracker error and explain why

Content production order

For each job, strictly in this sequence:

Step 1 — Read source material

Read all files in $RESUME_DIR. The pool may contain:

| File type | What to extract | |-----------|----------------| | Master resume (.docx / .pdf) | Full work history, bullet points, metrics, dates. PDF text is extracted by the agent's built-in tools; the conversion script handles .docx and .md only. | | Older tailored versions | Phrasing that worked well for similar roles | | Cover letter drafts | Preferred voice, opening formulas, recurring themes | | Skills list / bio (.md / .txt) | Certifications, tools, side projects, publications |

Extract everything factual — every bullet, every metric, every tool name. This is your raw material. Do not invent anything not present in these files.

Resume markdown format specification

The markdown file must follow this exact format. md_to_docx.py parses it structurally — any deviation will produce wrong or missing output.

Full Name
Email | Phone | LinkedIn | Location
SUMMARY
Two to three sentences summarizing the candidate.
CORE SKILLS
List of skills, tools, and technologies relevant to this role.
EXPERIENCE
Job Title — Company Name | City, ST | Jan 2022 – Present
• Accomplished X by doing Y, resulting in Z
• Another bullet point with a metric
Job Title — Company Name | City, ST | Jun 2019 – Dec 2021
• Bullet point
• Bullet point
EARLIER EXPERIENCE
Earlier Role — Company, Year–Year
Another Earlier Role — Company, Year–Year
EDUCATION
University Name — Degree, Major (Year)

Parsing rules the script enforces — follow these exactly:

| Element | Rule | |---------|------| | Line 1 | Full name, plain text, no # heading marker | | Line 2 | Contact info, pipe-separated | | Section headers | ALL CAPS, no ## — exactly SUMMARY, CORE SKILLS, EXPERIENCE, EARLIER EXPERIENCE, EDUCATION | | Job header | Title — Company \| Location \| Date range — separator is — (em dash with spaces), fields separated by \| | | Bullets | Start with • or -, one per line | | Earlier experience | One line per entry: Role — Company, Years | | Education | One line per entry: University — Degree |

What the script handles automatically:

More jobs than template slots → clones the last job's formatting

Fewer jobs than template slots → removes unused placeholders

Same logic for bullets, earlier experience, and education entries

Step 2 — Write resume markdown

Tailor bullet points to match the JD keywords. Prioritize:

Skills explicitly mentioned in JD

Quantified achievements relevant to the role

Asset classes, systems, or methodologies named in JD

Save to: $RESUME_OUTPUT_DIR/${USER_FIRST_NAME}__