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:

bash

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 typeWhat to extractMaster 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 versionsPhrasing that worked well for similar rolesCover letter draftsPreferred voice, opening formulas, recurring themesSkills 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.

markdown

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:

ElementRuleLine 1Full name, plain text, no # heading markerLine 2Contact info, pipe-separatedSection headersALL CAPS, no ## — exactly SUMMARY, CORE SKILLS, EXPERIENCE, EARLIER EXPERIENCE, EDUCATIONJob headerTitle — Company | Location | Date range — separator is — (em dash with spaces), fields separated by |BulletsStart with • or -, one per lineEarlier experienceOne line per entry: Role — Company, YearsEducationOne 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}_<Company>_<Title>_Resume_2026.md

Self-check before converting to docx#

Before running md_to_docx.py, verify the markdown against these rules:

bash

# Line 1 must be plain name (no # prefix)
head -1 resume.md

# Line 2 must contain pipes (contact info)
sed -n '2p' resume.md | grep '|'

# Section headers must be ALL CAPS with no ## prefix
grep -E '^[A-Z ]+$' resume.md

# Job headers must match: Title — Company | Location | Date
grep -E '^.+ — .+ \| .+ \| .+$' resume.md

# Bullets must start with • or -
grep -E '^[•\-]' resume.md

If any check fails, fix the markdown before proceeding — a malformed file will silently produce an incomplete docx.

Step 3 — Write cover letter markdown#

Three paragraphs max:

1. Why this role + company
2. Most relevant experience match (2–3 specific points)
3. Brief close

Save to: $RESUME_OUTPUT_DIR/${USER_FIRST_NAME}_<Company>_<Title>_Cover_Letter_2026.md

Step 4 — Update tracker#

Change status to md_ready. Record md file paths.

Step 5 — Generate docx files#

Resume — use md_to_docx.py with the template:

bash

python3 "$MD_TO_DOCX_SCRIPT" \
  --input "$RESUME_OUTPUT_DIR/${USER_FIRST_NAME}_<Company>_<Title>_Resume_2026.md" \
  --template "$RESUME_TEMPLATE" \
  --output "$RESUME_OUTPUT_DIR/${USER_FIRST_NAME}_<Company>_<Title>_Resume_2026.docx"

Cover letter — use python-docx directly (plain text, no template):

python

from docx import Document
from docx.shared import Pt
from docx.enum.text import WD_ALIGN_PARAGRAPH

doc = Document()
style = doc.styles['Normal']
style.font.name = 'Calibri'
style.font.size = Pt(11)

# Add paragraphs from cover letter md content
for para in cover_letter_paragraphs:
    p = doc.add_paragraph(para)

doc.save(f"{output_dir}/{os.environ['USER_FIRST_NAME']}_<Company>_<Title>_Cover_Letter_2026.docx")

Step 6 — Verify docx#

"Text looks right" is not the same as "file is deliverable." Both conditions must pass:

1. Content check — open the docx and compare section by section against the md:

   • No missing sections
   • No leftover {{PLACEHOLDER}} strings anywhere in the document
   • Company name and job title are correct throughout

2. File check — the docx must open without errors, have non-zero file size, and be saved to $RESUME_OUTPUT_DIR

3. URL check — validate the job URL from the tracker is still reachable

Do not call partial verification "good enough." If any check fails, fix and re-verify before updating the tracker.

Step 7 — Update tracker#

• Success → resume_ready, record docx paths
• Cannot reliably complete → error, write reason

Step 8 — Report#

After each job, report: company, title, files produced, any issues.

File naming convention#

text

${USER_FIRST_NAME}_<CompanyName>_<JobTitle>_Resume_2026.docx
${USER_FIRST_NAME}_<CompanyName>_<JobTitle>_Cover_Letter_2026.docx

Spaces → underscores. Keep company and title short (≤ 20 chars each if possible).

Tracker status flow#

text

shortlist → md_ready → resume_ready
                    ↘ error

Known failure modes to avoid#

• Do not call partial verification "good enough"
• Do not treat "text looks right" as equivalent to "docx is deliverable"
• Do not spend more than 30 minutes on a single job without reporting status
• Do not write a generic script to handle all cases; get the md layer working first

Scope#

Resume tailoring only. Do not submit applications. Hand off resume_ready entries to the jobautopilot-submitter skill.

Support#

If Job Autopilot saved you time: paypal.me/ZLiu308