Token导航 LogoToken导航

Agent Skill · 研究检索

jobautopilot-tailor(工作自动驾驶裁缝)

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。

jobautopilot-tailor 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为操作浏览器;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 17,731。

总安装 6,439周安装 271GitHub Stars 公开资料未说明下载 2,255核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.3.0

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

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

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。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install jobautopilot-tailor;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要操作浏览器,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

jobautopilot-tailor 怎么安装?

在终端执行 openclaw skills install jobautopilot-tailor。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

jobautopilot-tailor 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

jobautopilot-tailor 需要什么权限?

权限级别是操作浏览器。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。

jobautopilot-tailor 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

jobautopilot-tailor 用的人多吗?

公开数据是累计安装 6,439 次,近一周安装 271 次,下载 2,255 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

jobautopilot-tailor 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
jobautopilot-tailor

工作自动驾驶裁缝 · 本页

6,439—操作浏览器MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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

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。目前只有一个公开来源,建议同时核对源仓库。

继续浏览同类 Skills