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Job Ok

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GresonKwan
job-ok

Use when helping a Chinese job seeker, especially students, interns, or early-career users, prepare job applications with an agent without fabricating experience, auto-submitting applications, scraping hiring platforms, or promising offers.

Overview

PublisherGresonKwan
RepositoryJobOK
Skill namejob-ok
Stars
422
Forks
5
Bundled files
30
LicenseMIT
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 30 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by GresonKwan on GitHub. Read the source before you install it.

Installation

Install the Job Ok AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/GresonKwan/JobOK/tree/main
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/GresonKwan/JobOK.git \
  .claude/skills/job-ok
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Job Ok in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Job Ok on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Job Ok is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Job OK

Job OK 是一个面向中文求职者的本地求职 Skill。它帮助学生、实习生和早期职场人把真实经历整理成可追溯的求职证据，再用于岗位匹配、简历优化、投递跟踪和面试表达训练。

核心原则：先证据，后结论；先岗位匹配，后简历改写；用户手动确认任何外部投递动作。

使用边界

  • 默认使用中文，除非用户要求其他语言。
  • 默认把简历、联系方式、截图、聊天记录等敏感材料保留在本地。
  • 输出中区分 fact、assumption、inference、user_preference。
  • 不编造学历、实习、项目、奖项、指标、证书、技能或公司经历。
  • 不承诺面试、offer、薪资结果或平台曝光。
  • 不自动投递、不自动私信 HR、不绕过登录、不批量爬取招聘平台。
  • 不用于企业侧招聘、候选人排名或人事决策。

本地案例目录

每服务一个求职者，创建或复用：

text
job-search-cases/<yyyy-mm-dd-user-slug>/
├── brief.yaml
├── raw/
│   ├── resume/
│   └── job-posts/
├── profile.yaml
├── experience-assets.md
├── strengths.md
├── target-roles.csv
├── jobs.jsonl
├── job-matches.csv
├── resume-review.md
├── resume-versions/
├── interview-story-bank.md
├── interview-practice.md
├── application-tracker.csv
└── review-log.md

优先从 assets/templates/ 复制模板到案例目录，再开始分析。

工作流

  1. 先 Intake。 收集简历、目标城市、目标岗位、教育背景、项目/实习经历、限制条件、排除岗位、偏好行业和风险备注。核心信息不足时先追问，不急着推荐岗位或改简历。参考 references/intake-flow.md。
  2. 提取真实经历。 把简历和用户回答整理到 experience-assets.md。可用 scripts/extract_resume_text.py 提取 .pdf、.docx、.txt、.md 简历文本。
  3. 挖掘优势。 每个优势都必须走完 证据 -> 行为 -> 能力 -> 岗位信号。参考 references/strength-taxonomy.md，在 strengths.md 记录可信度和缺失证据。
  4. 生成岗位假设。 输出 3-5 个目标岗位簇到 target-roles.csv。参考 references/job-matching-rubric.md，写清匹配证据、差距、30 天补强动作和适合公司类型。
  5. 整理真实 JD。 只接受用户提供的岗位链接、截图、复制 JD、CSV 导出、Markdown 表格或浏览器可见页面。用 scripts/normalize_jobs.py 生成 jobs.jsonl。使用平台资料前先读 references/platform-boundaries.md。
  6. 评分和短名单。 用 scripts/score_job_matches.py 做确定性初筛。分数只用于 triage，不代表真实录取概率。低分岗位进入观察池，不进入投递列表。
  7. 优化简历。 参考 references/resume-rubric.md。每条建议必须能回到真实经历。输出 resume-review.md，并在 resume-versions/ 记录不同岗位版本。
  8. 训练面试表达。 参考 references/interview-training.md。一次只问一个问题，等待用户回答，再追问和复盘。首版只处理文本或语音转写稿。
  9. 跟踪和复盘。 每次投递、回复、面试、拒信或新增 JD 后，更新 application-tracker.csv 和 review-log.md。

辅助脚本

bash
python3 .agents/skills/job-ok/scripts/extract_resume_text.py \
  --input job-search-cases/<case>/raw/resume/resume.pdf \
  --output job-search-cases/<case>/raw/resume/resume.txt

python3 .agents/skills/job-ok/scripts/normalize_jobs.py \
  --input job-search-cases/<case>/raw/job-posts/jobs.md \
  --output job-search-cases/<case>/jobs.jsonl \
  --source-type user_paste

python3 .agents/skills/job-ok/scripts/score_job_matches.py \
  --profile job-search-cases/<case>/profile.yaml \
  --strengths job-search-cases/<case>/strengths.md \
  --jobs job-search-cases/<case>/jobs.jsonl \
  --output job-search-cases/<case>/job-matches.csv

输出标准

  • 优势挖掘：写明证据、可信度、岗位信号和缺失证据。
  • 岗位建议：同时写为什么投、为什么不投、简历重点和下一步用户动作。
  • 简历修改：输出建议，不输出不可验证的最终表述；缺证据内容标记为 needs_proof。
  • 面试训练：检查结构、具体性、证据、岗位相关性、风险表达和可追问性。
  • 外部动作：结尾使用“用户手动确认后再执行”，除非用户报告完成，否则不要写“已投递”。

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Job Ok AI skill do?

Use when helping a Chinese job seeker, especially students, interns, or early-career users, prepare job applications with an agent without fabricating experience, auto-submitting applications, scraping hiring platforms, or promising offers.

Why use Job Ok on TypingMind?

Because you install it once and use it with any model. Job Ok is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Job Ok in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/GresonKwan/JobOK/tree/main. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Job Ok?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Job Ok?

As many as you like. As long as a model supports skills, you can use Job Ok with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Job Ok AI skill free?

Yes. It is published on GitHub by GresonKwan under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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