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jobautopilot-tailor工作自动驾驶裁缝

Agent Skill

jobautopilot-tailor 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

6,439

周安装

271

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下载量

2,255
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:jobautopilot-tailor(工作自动驾驶裁缝)
来源仓库:https://github.com/jerronl/jobautopilot-tailor
安装命令:
openclaw skills install jobautopilot-tailor
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install jobautopilot-tailor

简介

工作自动驾驶裁缝根据职位描述定制简历和求职信,突出匹配关键词。

  • 适合针对不同岗位快速调整个人材料的场景。jobautopilot-tailor 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 输出格式精美的 .docx 文件,便于直接投递使用。
  • 使用前需提供原始简历和目标岗位 JD 文本。
  • 建议人工校验改写内容是否符合事实,防止夸大或失真。

SKILL.md

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
requires
tools
python_packages
env
bins
metadata
clawdbot
emoji
📄
requires
env
bins
pip
files

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 typeWhat 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 versionsPhrasing that worked well for similar roles
Cover letter draftsPreferred 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:

ElementRule
Line 1Full name, plain text, no # heading marker
Line 2Contact info, pipe-separated
Section headersALL 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 \`
BulletsStart with or -, one per line
Earlier experienceOne line per entry: Role — Company, Years
EducationOne 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:

# 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:

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):

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

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

${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

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

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

89.57%
按下载量换算2,020

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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