Automate job searches and application submissions across multiple platforms like LinkedIn and Indeed using Clawdbot.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install job-auto-apply
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install job-auto-apply using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Job Auto-Apply Skill?
This skill provides a robust framework for automating the end-to-end job application process. By leveraging Openclaw Skills, developers and job seekers can programmatically scan major platforms, analyze job descriptions for compatibility, and generate AI-driven tailored cover letters. It bridges the gap between manual searching and high-volume, high-quality outreach.
Built specifically for integration with Clawdbot, this tool handles complex form automation and screening questions. Whether you are looking for remote backend roles or local software engineering positions, these Openclaw Skills ensure your profile reaches the right recruiters with minimal manual effort while maintaining high standards of application quality.
Job Auto-Apply Skill Use Cases
- Automating high-volume job applications across LinkedIn, Indeed, and Glassdoor.
- Scaling job searches for specific roles like Senior Developer or Backend Engineer.
- Generating tailored cover letters and resumes using AI to match job descriptions.
- Monitoring job markets and applying to new listings as soon as they appear via Openclaw Skills.
- Tracking application status and success rates programmatically.
How Job Auto-Apply Skill Works
- Configure the user profile by filling out the JSON template with personal details, experience, and resume paths.
- Define search parameters such as job title, location, salary requirements, and preferred platforms.
- Execute the search workflow where the agent fetches listings and filters them based on a match score.
- Generate customized application assets, including AI-written cover letters specific to the company's needs.
- Submit the application through automated form filling or via official APIs, supported by Openclaw Skills.
- Log the results into a tracking system for follow-up and status monitoring.
Job Auto-Apply Skill Setup
The setup for these Openclaw Skills requires a Python environment and a configured profile.
# Step 1: Initialize your profile
cp profile_template.json ~/job_profile.json
# Step 2: Configure search and apply (Dry Run suggested first)
python job_search_apply.py --title "Software Engineer" --location "Remote" --dry-run
# Step 3: Enable production mode for actual submissions
python job_search_apply.py --profile ~/job_profile.json --auto-apply --no-dry-run
Job Auto-Apply Skill Data Schema & Taxonomy
The system organizes data using a structured JSON schema for profiles and a logging system for applications. Openclaw Skills utilize these formats to ensure consistency across different job platforms.
| Component | Description |
|---|---|
job_profile.json |
Contains full name, email, resume path, and skills taxonomy. |
application_logs.json |
Stores timestamps, job URLs, match scores, and submission status. |
match_score |
A float (0.0 - 1.0) representing the compatibility of the job description to the user profile. |
custom_paragraph |
AI-generated text inserted into cover letter templates. |
Job Auto-Apply Skill Advanced Features
- Multi-platform support for LinkedIn (Easy Apply), Indeed, Glassdoor, and Wellfound.
- AI-powered screening question handling to bypass complex application forms automatically.
- Configurable rate limiting and manual confirmation modes to stay within platform terms of service.
- Custom cover letter templating with dynamic placeholders for company-specific personalization using Openclaw Skills.
- Advanced filtering using minimum match thresholds (e.g., 0.75) to ensure high-quality applications.
SKILL.md
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