claw-apply/SKILL.md

name, description

name

description

claw-apply

Automated job search and application for LinkedIn and Wellfound. Searches for matching roles hourly, applies automatically every 6 hours using Playwright + Kernel stealth browsers. Handles LinkedIn Easy Apply and Wellfound applications. Asks you via Telegram when it hits a question it can't answer, saves your answer, and never asks again. Use when you want to automate your job search and application process.

claw-apply

Automated job search and application. Finds matching roles on LinkedIn and Wellfound, applies automatically, and learns from every unknown question.

Requirements

• Kernel.sh account (for stealth browsers + bot detection bypass)
• Kernel CLI: npm install -g @onkernel/cli
• Kernel Managed Auth sessions for LinkedIn and Wellfound
• Kernel residential proxy (US recommended)
• Telegram bot for notifications

Setup

1. Install dependencies

cd claw-apply
npm install

2. Create Kernel Managed Auth sessions

# Create residential proxy
kernel proxies create --type residential --country US --name "claw-apply-proxy"

# Create authenticated browser profiles
kernel auth create --name "LinkedIn-YourName"     # Follow prompts to log in
kernel auth create --name "WellFound-YourName"    # Follow prompts to log in

3. Configure

Edit these files in config/:

• profile.json — your personal info, resume path, cover letter
• search_config.json — what jobs to search for (titles, keywords, filters)
• settings.json — Telegram bot token, Kernel profile names, proxy ID, mode A/B

4. Run setup

KERNEL_API_KEY=your_key node setup.mjs

Verifies config, tests logins, sends a test Telegram message.

5. Register cron jobs (via OpenClaw)

Search: 0 * * * *       (hourly)
Apply:  0 */6 * * *     (every 6 hours)

Running manually

KERNEL_API_KEY=your_key node job_searcher.mjs   # search now
KERNEL_API_KEY=your_key node job_applier.mjs    # apply now

How it works

JobSearcher (hourly):

1. Searches LinkedIn + Wellfound with your configured keywords
2. Filters out excluded roles/companies
3. Adds new jobs to data/jobs_queue.json
4. Sends Telegram: "Found X new jobs"

JobApplier (every 6 hours):

1. Reads queue for new + needs_answer jobs
2. LinkedIn: navigates two-panel search view, clicks Easy Apply, fills form, submits
3. Wellfound: navigates to job, fills profile, submits
4. On unknown question → Telegrams you → saves answer → retries next run
5. Sends summary when done

Mode A vs Mode B

Set in config/settings.json → "mode": "A" or "B"

• A: Fully automatic. No intervention needed.
• B: Applier sends you the queue 30 min before running. You can flag jobs to skip before it fires.

File structure

claw-apply/
├── job_searcher.mjs        search agent
├── job_applier.mjs         apply agent
├── setup.mjs               setup wizard
├── lib/
│   ├── browser.mjs         Kernel/Playwright factory
│   ├── form_filler.mjs     generic form filling
│   ├── linkedin.mjs        LinkedIn search + apply
│   ├── wellfound.mjs       Wellfound search + apply
│   ├── queue.mjs           queue management
│   └── notify.mjs          Telegram notifications
├── config/
│   ├── profile.json        ← fill this in
│   ├── search_config.json  ← fill this in
│   ├── answers.json        ← auto-grows over time
│   └── settings.json       ← fill this in
└── data/
    ├── jobs_queue.json     auto-managed
    └── applications_log.json  auto-managed

answers.json — self-learning Q&A bank

When the applier hits a question it can't answer, it messages you on Telegram. You reply. The answer is saved to config/answers.json and used forever after.

Pattern matching is regex-friendly:

[
  { "pattern": "quota attainment", "answer": "1.12" },
  { "pattern": "years.*enterprise", "answer": "5" },
  { "pattern": "1.*10.*scale", "answer": "9" }
]