| name | career-job-hunting |
| category | career |
| description | Complete job hunting workflow using Hermes Agent — resume setup via visiky-resume-setup, multi-platform job search via mcp-jobs, application tracking in Notion. |
| tags | ["career","job-hunting","mcp","resume","internship"] |
| trigger | Use this skill when the user wants to:
- Find job/internship opportunities on Chinese platforms (BOSS直聘, 猎聘, 智联, 51job)
- Set up or update their online resume
- Track job applications and interview progress
- Get recommendations on job hunting tools and workflow
- Evaluate which tools (mcp-jobs, get_jobs, JobClaw) fit their current stage
|
Career Job Hunting with Hermes Agent
Overview
This skill covers the end-to-end workflow for using Hermes Agent to find, track, and apply for jobs/internships — optimized for the Chinese job market (AI/tech roles). The user (冯周杰 / Publieople) is a 大二 AI 专业学生 at 上海中侨职业技术大学, targeting AI toolchain development / Agent application / MLOps roles.
Components
1. Resume Setup (visiky-resume-setup)
The user's resume data lives in publieople/publieople/resume.json on GitHub. The online editor preview is at https://visiky.github.io/resume?user=publieople&branch=master.
See the visiky-resume-setup skill for full schema, deployment, and debugging.
Key fields to update (last updated 2026-06-04):
profile.positionTitle — "AI 工具链开发与智能体应用 · 自动化效率工程师"educationList — 上海中侨职业技术大学, 人工智能（主修：机器学习、深度学习、计算机视觉、自然语言处理、数据结构与算法）, 2024-至今workExpList — 4 bullet points with quantification (GPU集群管理 + AI服务栈 + 培训 + 知识传承)projectList — 4 projects (AI Agent生态/产研社/技术博客/AI学习小组)，均补充量化数据awardList — 4 awards, ordered by actual prestige: 数维杯>中青杯>五一建模>工行杯aboutme — 精简为3行，无MBTI，突出AI Agent闭环经验
2. Job Search (mcp-jobs MCP Integration)
Tool: mergedao/mcp-jobs v1.4.0 — aggregates jobs from 猎聘, BOSS直聘, 智联, 51job via Playwright crawling.
Registration in ~/.hermes/config.yaml:
mcp_servers:
jobs:
command: "node"
args: ["/home/po/.npm-global/lib/node_modules/mcp-jobs/dist/mcp.js"]
Available MCP tools (after Hermes restart):
mcp_jobs_mcp_search_job — keyword-based job search across platformsmcp_jobs_mcp_job_detail — fetch details from a specific job URL
Usage patterns (directly in conversation):
- "搜上海AI实习岗" → search_job(keyword="AI", city="上海")
- "搜北京Python后端薪资15k以上" → search_job with filters
- "看看这个职位的详情" → job_detail with URL from search results
See references/mcp-jobs-deployment.md for install and troubleshooting details.
3. Tool Deployment — Detailed Installation & Integration
For each tool's detailed reference see references/tool-details.md.
3a. mcp-jobs — Zero-Config Job Aggregator
Type: Aggregator (search only) — crawl BOSS, 猎聘, 智联, 51job
Tech: Node.js + Playwright + MCP | Risk: Low
Prerequisites: Node.js v18+, npm, Playwright Chromium (npx playwright install chromium)
Standalone: npx -y mcp-jobs
Hermes Integration (see references/mcp-jobs-deployment.md):
mcp_servers:
jobs:
command: "npx"
args: ["-y", "mcp-jobs"]
timeout: 180
connect_timeout: 60
Available tools (after Hermes restart): mcp_jobs_mcp_search_job, mcp_jobs_mcp_job_detail
Known pitfalls:
npx -y mcp-jobs has no bin field — must use the working config above; the node/direct dist/mcp.js path is a fallback- v1.4.0 is ~10 months old — platform DOM may have changed
- Proxy: MCP subprocess inherits filtered env from Hermes config, not shell env. If behind
http://127.0.0.1:7890, add env: block to the server config
- First run downloads Playwright browser — may take 30-60s
3b. JobClaw — LLM-Powered Smart Apply
Type: Auto-applier (precision, LLM-matched) — BOSS + LinkedIn
Tech: Python + Playwright + LLM scoring | Risk: Medium
Prerequisites: Python 3.11+, Playwright (playwright install)
git clone https://github.com/slothsheepking/jobclaw.git
cd jobclaw
pip install -e .
playwright install
jobclaw login
jobclaw run
LLM auth (priority order): Claude OAuth (free) > Anthropic API key > OpenAI API key
Notification: Telegram bot / Discord webhook
Anti-detection: Randomized apply delays, HR inactivity filter (7d default)
Proxy: HTTP_PROXY / HTTPS_PROXY env vars
Known pitfalls:
- Cookie management: cookies in
~/.jobclaw/cookies/ expire; run jobclaw login --check to verify
- LinkedIn requires separate cookie login
- LLM auth priority means no API key needed if Claude Code is set up — uses subscription token
3c. get_jobs — Mass Batch Apply
Type: Auto-applier (volume) — BOSS (150/day), 猎聘, 前程无忧, 智联
Tech: Java + Gradle + ChromeDriver | Risk: Medium-High (account restrictions)
Prerequisites: JDK 21, Maven/Gradle, ChromeDriver matching Chrome version
git clone https://github.com/loks666/get_jobs.git
cd get_jobs
./gradlew build
Platform limits:
- BOSS: 150 hellos/day hard cap; auto-blacklists "not suitable" companies
- 猎聘: unlimited hellos, must bind WeChat
- 前程无忧: degraded, limited applies
- 智联招聘: ~100 applies, broken, not recommended
Known pitfalls:
- Windows-oriented: hardcoded
chromedriver.exe paths; ChromeDriver must match Chrome version exactly
- WSL complication: Chrome must run headless; ChromeDriver path must be Linux-adjusted
- Account risk: BOSS auto-apply patterns can trigger account restrictions — use a secondary account
- .env config: Requires API key for LLM greeting generation (
OPENAI_API_KEY or proxy)
- Cost: ~$0.06/day with gpt-5-nano
3d. GeekGeekRun (牛人快跑) — Desktop GUI
Type: Auto-applier (desktop UI) — BOSS only
Tech: Puppeteer + Electron | Risk: Low
Packages: Windows (.exe), Linux (.deb), macOS (.dmg)
Features: Auto-chat, read-without-reply retry, zombie job cleanup, LLM template editor, config templates
Repo: github.com/geekgeekrun/geekgeekrun (857 commits, 27 releases)
4. Deployment Decision Tree
Want to just browse the market?
└─ mcp-jobs (integrate into Hermes, search by conversation)
Want precision-targeted apply (high match rate)?
└─ JobClaw (Python, LLM scoring, anti-detection)
Want mass batch apply (volume over precision)?
└─ get_jobs (Java, multi-platform, cap-aware)
Want a simple GUI without command line?
└─ GeekGeekRun (Desktop app, BOSS only)
5. Environment Notes (This User — Verified May 2026)
- Shell: Arch Linux via WSL2
- Node.js: v26.1.0 ✅ | npm: 11.14.1 ✅
- Playwright: 1.59.0 (chromium-1217, chromium_headless_shell-1217) ✅
- Python: 3.11.14 ✅
- Java: NOT installed ❌ (needed for get_jobs)
- Proxy:
http://127.0.0.1:7890 (for HTTP/HTTPS traffic)
- HF Mirror:
HF_ENDPOINT=https://hf-mirror.com
- Resume repo:
publieople/publieople with resume.json (visiky/resume format)
6. Application Tracking (Notion)
Set up a Notion database with columns:
- Company, Position, Platform, URL
- Status (已投递 / 已读 / 约面 / 一面 / 二面 / Offer / 感谢信)
- Date applied, Last follow-up, Notes
- Resume version used
Use Hermes' notion skill to interact with this database.
Quick Start for New Sessions
- Resume: Ensure
publieople/publieople/resume.json is up to date → push to GitHub
- Search: Use mcp-jobs tools (requires Hermes restart after config change) — "帮我搜上海AI实习岗"
- Track: Log interesting matches in Notion
- Apply: For high-match roles, draft custom greeting and submit manually or via JobClaw
Job Hunting Timeline (2026)
| Period | Focus | Tool |
|---|
| 大二暑假 (now, 2026.06-08) | Resume polish, portfolio building, market research. Target 上海日常实习 for 大三上 (Sep start). | web_search (实习僧/Boss直聘/官网) |
| 大三上 (2026.09-12) | Start regular internship in Shanghai (3-4 days/week, 3-6 months preferred). Daily internships hire year-round, less competition than summer. | mcp-jobs + manual apply |
| 大三寒假 | Continue or upgrade internship | — |
| 大三暑假 | Summer internship push for return offer / full-time pipeline | get_jobs for volume |
Key strategy: 福州暑假无大厂AI机会 → 7-8月打磨简历 + 线上调研 → 9月上海日常实习入职。 日常实习全年可投、竞争小于暑期实习、可拉长3-6个月含金量更高。
Pitfalls
Phase 0: Reconcile User Intent (Before Any Planning)
Always do this before writing a plan, picking companies, or editing the resume. A user saying "let's discuss my internship plan" is NOT permission to start drafting — it's permission to discover what the plan should be.
Pull signals from every available source and check them against each other:
| Source | What to extract | How |
|---|
publieople/publieople/resume.json | Stated positionTitle, project list, skill tags | gh api or curl raw.githubusercontent.com |
| GitHub repos + stars | Actual shipped projects, language depth, what they spend time on | gh api /users/{name}/repos |
Blog (blog.for-people.cn) | Topics they actually write about vs what they claim to be good at | curl /feed (NotionNext ships /feed) |
Notion 求职意向 data_source | The single highest-signal artifact — user's actual ranked company list with reasons | /data_sources/{id}/query |
Notion 大学, 阅历, 编程, AI, 大二上 课程 | Course context, hobby/identity tags | search → list data_sources |
| mem0_search + session_search | Past stated preferences and decisions | before drafting anything |
Notion API 2025-09 pitfalls when doing this:
POST /v1/search filter.value MUST be "data_source", not "database" (returns 400 otherwise — older tutorials are wrong)- Sort + no query = 400. Omit sort if you have no query.
- 404 on
/data_sources/{id}/query does NOT mean "doesn't exist" — it means "integration not shared with this database". Try a different data_source.
Then surface the contradictions directly. In this user's case, the Notion 求职意向 table contained 28 WLB-first foreign-tech companies (VMware/AWS/NVIDIA/Microsoft/Apple/...) while resume.json said "AI 工具链开发与智能体应用 · 自动化效率工程师". Both are real, but they don't match — and HR will read whichever is on the resume. Don't paper over this with a "best of both worlds" recommendation; ask the user to pick.
Use the Reconnaissance Report format when reporting back (see references/reconnaissance-report-template.md):
- 已确认画像 (identity, skills, projects, honors) — concrete facts only
- 求职意向表的真正信号 — what the company list + reasons reveal
- 4 个尖锐问题 — explicit mismatches between stated position and inferred direction
- 5 个事实问题 (A-E) — what the user must answer before planning resumes
Only after the user answers the A-E questions does planning begin. Never write a resume, company list, or timeline until intent is aligned.
Resume Optimization Rules (from 2026-06-04 session)
When helping the user optimize their resume:
- Never guess competition prestige — verify via web_search before ranking. 校级 awards go last.
- Don't add projects the user hasn't vouched for — ask first. The user knows which projects are solid vs. "不能细看".
- University courses may not matter — the user considers self-taught skills more important than formal coursework. Ask before adding.
- Quantify everything possible — 子弹点 = 动词 + 事项 + 数据. "100+ Skills模块" beats "沉淀百余个".
- Personal evaluation stays short — 3 lines max, no MBTI, no filler.
- Section title "更多信息" should be renamed to something concrete like "竞赛获奖" if that's what it contains.
Related Skills
visiky-resume-setup — online resume JSON schema, deployment, and debuggingnative-mcp — general MCP server configuration for Hermes (includes the no-bin-field fix for mcp-jobs)notion — application tracking database management
References
references/mcp-jobs-deployment.md — mcp-jobs install, troubleshooting, and MCP integration detailsreferences/tool-details.md — detailed tool comparisons: mcp-jobs v1.4.0 deps, get_jobs platform limits, JobClaw LLM auth, GeekGeekRun features, environment verificationreferences/job-tools-landscape.md — broader auto-apply tool landscape (NEAR agent market, OpenOutreach, MoneyPrinterTurbo, etc.)references/reconnaissance-report-template.md — Phase 0 user-intent alignment: 4-source cross-check, 4-section report format, A-E gate questions
