feat: claw-apply v0.1 — full implementation
- job_searcher.mjs: LinkedIn + Wellfound search, queue population - job_applier.mjs: Easy Apply + Wellfound apply, Mode A/B - lib/form_filler.mjs: config-driven form filling, custom answers.json - lib/linkedin.mjs: two-panel Easy Apply flow - lib/wellfound.mjs: Wellfound search + apply - lib/browser.mjs: Kernel stealth browser factory with local fallback - lib/queue.mjs: jobs_queue.json management - lib/notify.mjs: Telegram notifications - setup.mjs: setup wizard with login verification - Config templates: profile, search_config, answers, settings - SKILL.md: OpenClaw skill definition
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SKILL.md
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SKILL.md
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---
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name: claw-apply
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description: 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.
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---
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# claw-apply
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Automated job search and application. Finds matching roles on LinkedIn and Wellfound, applies automatically, and learns from every unknown question.
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## Requirements
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- [Kernel.sh](https://kernel.sh) account (for stealth browsers + bot detection bypass)
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- Kernel CLI: `npm install -g @onkernel/cli`
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- Kernel Managed Auth sessions for LinkedIn and Wellfound
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- Kernel residential proxy (US recommended)
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- Telegram bot for notifications
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## Setup
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### 1. Install dependencies
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```bash
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cd claw-apply
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npm install
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```
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### 2. Create Kernel Managed Auth sessions
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```bash
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# Create residential proxy
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kernel proxies create --type residential --country US --name "claw-apply-proxy"
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# Create authenticated browser profiles
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kernel auth create --name "LinkedIn-YourName" # Follow prompts to log in
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kernel auth create --name "WellFound-YourName" # Follow prompts to log in
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```
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### 3. Configure
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Edit these files in `config/`:
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- **`profile.json`** — your personal info, resume path, cover letter
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- **`search_config.json`** — what jobs to search for (titles, keywords, filters)
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- **`settings.json`** — Telegram bot token, Kernel profile names, proxy ID, mode A/B
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### 4. Run setup
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```bash
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KERNEL_API_KEY=your_key node setup.mjs
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```
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Verifies config, tests logins, sends a test Telegram message.
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### 5. Register cron jobs (via OpenClaw)
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```
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Search: 0 * * * * (hourly)
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Apply: 0 */6 * * * (every 6 hours)
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```
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## Running manually
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```bash
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KERNEL_API_KEY=your_key node job_searcher.mjs # search now
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KERNEL_API_KEY=your_key node job_applier.mjs # apply now
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```
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## How it works
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**JobSearcher** (hourly):
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1. Searches LinkedIn + Wellfound with your configured keywords
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2. Filters out excluded roles/companies
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3. Adds new jobs to `data/jobs_queue.json`
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4. Sends Telegram: "Found X new jobs"
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**JobApplier** (every 6 hours):
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1. Reads queue for `new` + `needs_answer` jobs
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2. LinkedIn: navigates two-panel search view, clicks Easy Apply, fills form, submits
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3. Wellfound: navigates to job, fills profile, submits
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4. On unknown question → Telegrams you → saves answer → retries next run
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5. Sends summary when done
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## Mode A vs Mode B
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Set in `config/settings.json` → `"mode": "A"` or `"B"`
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- **A**: Fully automatic. No intervention needed.
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- **B**: Applier sends you the queue 30 min before running. You can flag jobs to skip before it fires.
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## File structure
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```
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claw-apply/
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├── job_searcher.mjs search agent
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├── job_applier.mjs apply agent
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├── setup.mjs setup wizard
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├── lib/
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│ ├── browser.mjs Kernel/Playwright factory
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│ ├── form_filler.mjs generic form filling
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│ ├── linkedin.mjs LinkedIn search + apply
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│ ├── wellfound.mjs Wellfound search + apply
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│ ├── queue.mjs queue management
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│ └── notify.mjs Telegram notifications
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├── config/
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│ ├── profile.json ← fill this in
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│ ├── search_config.json ← fill this in
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│ ├── answers.json ← auto-grows over time
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│ └── settings.json ← fill this in
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└── data/
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├── jobs_queue.json auto-managed
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└── applications_log.json auto-managed
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```
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## answers.json — self-learning Q&A bank
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When the applier hits a question it can't answer, it messages you on Telegram.
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You reply. The answer is saved to `config/answers.json` and used forever after.
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Pattern matching is regex-friendly:
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```json
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[
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{ "pattern": "quota attainment", "answer": "1.12" },
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{ "pattern": "years.*enterprise", "answer": "5" },
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{ "pattern": "1.*10.*scale", "answer": "9" }
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]
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```
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