JobHound is a comprehensive AI-driven career companion designed to guide candidates through the entire recruitment lifecycle, from initial job discovery to final salary negotiation.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install jobhound
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 jobhound 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 JobHound?
JobHound is a sophisticated recruitment assistant within the Openclaw Skills ecosystem, specifically engineered to handle the complexities of the modern job market. It provides a seamless, end-to-end workflow that includes intelligent resume parsing, automated job discovery across major platforms like BOSS Zhipin and NiuKe, and data-driven evaluation of job fit. By maintaining a persistent memory of a candidate's profile and progress, it ensures that every interaction is contextually aware and strategically aligned with the user's career goals.
Beyond simple job matching, this skill excels in deep interview preparation. It leverages a STAR story bank methodology to help users prepare for behavioral and technical interviews, conducts realistic mock interviews with instant feedback, and assists in the delicate process of comparing multiple offers. Whether you are a student navigating campus recruitment or a professional looking for your next big move, JobHound acts as a dedicated coach to maximize your success rate.
JobHound Use Cases
- Automating job discovery across multiple recruitment platforms and official corporate sites.
- Analyzing and optimizing resumes to match specific Job Descriptions (JD) for higher conversion.
- Building a structured STAR (Situation, Task, Action, Result) story bank from personal experiences.
- Conducting realistic mock interviews with radar-chart performance analysis.
- Managing and tracking application statuses across different companies and interview rounds.
- Evaluating and negotiating salary packages by comparing multiple offers side-by-side.
How JobHound Works
- The agent initializes by reading the MEMORY.md file to understand the candidate's profile, core skills, and past application history.
- It parses the user's resume (PDF or Markdown) to extract structured data into a profile configuration.
- A 4-round search matrix is executed to find over 100 relevant job opportunities based on core keywords and city preferences.
- Each job is passed through an A-G evaluation framework (0-100 score) to assess role fit, skill match, and competitiveness.
- For high-scoring roles (A or B+), the agent generates customized resumes and tailored internal referral messages.
- During the interview phase, it pulls real interview experiences from external sources to create targeted 'question sets' and conduct mock sessions.
- Post-interview, the agent updates the memory with feedback, identifies weaknesses, and adjusts the STAR bank for future rounds.
- Finally, it provides a weighted comparison table for offer decision-making and negotiation scripts.
JobHound Setup
To integrate JobHound into your environment, ensure you have the necessary workspace structure and dependencies installed.
# Create the required directory structure
mkdir -p workspace/memory workspace/data workspace/reports workspace/output
# Ensure the core profile exists
touch workspace/memory/MEMORY.md
# If you are parsing PDF resumes, ensure the pdf skill is available in your Openclaw Skills library.
JobHound Data Schema & Taxonomy
JobHound utilizes a structured file system to maintain persistent state and organize recruitment data:
| File Path | Description |
|---|---|
memory/MEMORY.md |
Core persistent memory including candidate profile, weakness list, and STAR index. |
config/profile.yml |
Technical configuration of user preferences (city, salary, graduation year). |
data/scan-history.tsv |
Log of previously seen job postings to prevent duplicate recommendations. |
data/applications.md |
The master tracking sheet for application statuses (Pending, Interview, Offer). |
reports/{date}_eval.md |
Detailed A-G matrix evaluations for discovered job batches. |
output/STAR_Bank.md |
A structured library of the user's career stories mapped to interview competencies. |
JobHound Advanced Features
- Cross-session persistent memory ensuring the agent 'remembers' your interview weaknesses and progress over months.
- Automated JD deconstruction to identify 'hidden' core competencies required by employers.
- Dynamic search strategy that expands from core keywords to competitor lists and industry-specific long-tail threads.
- Comprehensive salary package breakdown including tax estimates, stock vesting cycles, and housing fund contributions.
- Multi-round interview simulation with adaptive difficulty and follow-up questioning based on user responses.
SKILL.md
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