A comprehensive AI-driven study assistant designed to optimize Gaokao preparation through data-driven tracking and strategic planning.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install gaokao
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 gaokao 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 Gaokao (高考) Exam Prep Assistant?
The Gaokao skill is a specialized framework designed for students preparing for China's national college entrance exam. By utilizing this implementation of Openclaw Skills, users turn their AI agent into a sophisticated academic coach that manages everything from daily scheduling to long-term university targeting. The skill focuses on maximizing score potential by applying data science principles to study habits.
This system moves beyond basic task management by incorporating province-specific curriculum rules and scoring structures. Within the ecosystem of Openclaw Skills, this tool stands out for its ability to handle complex subject combinations and provide actionable insights based on mock exam performance and spaced repetition data.
Gaokao (高考) Exam Prep Assistant Use Cases
- High school students requiring a structured daily schedule based on the Gaokao countdown.
- Parents looking to monitor progress and support their child's preparation through data-driven logs.
- Tutors managing multiple students and needing automated error pattern recognition and mock exam analysis.
- Retakers aiming to identify specific gaps in their previous year's performance for targeted improvement.
How Gaokao (高考) Exam Prep Assistant Works
- The agent initializes the user profile, collecting critical data such as province, subject combination, and target universities.
- Study sessions and mock exam results are logged into a structured local directory to maintain a persistent history.
- The system analyzes the data to identify weak areas with the highest points-per-hour potential, ensuring an ROI-first approach.
- Spaced repetition flashcards are automatically managed for subjects requiring heavy memorization like vocabulary and formulas.
- The agent generates predictive score reports and matches them against current university admission requirements.
Gaokao (高考) Exam Prep Assistant Setup
To begin using this skill, ensure your local directory structure is initialized for persistent storage. Run the following commands:
mkdir -p ~/gaokao/{subjects,sessions,mocks,flashcards}
touch ~/gaokao/profile.md ~/gaokao/feedback.md
Once the directories are ready, provide your province and subject combination (e.g., 3+1+2) to the agent to finalize the Openclaw Skills configuration.
Gaokao (高考) Exam Prep Assistant Data Schema & Taxonomy
The skill organizes data within the ~/gaokao/ directory using the following structure:
| Path | Purpose |
|---|---|
profile.md |
Stores goals, target scores, exam date, and province-specific settings. |
subjects/ |
Contains individual markdown files for each subject tracking mastery levels. |
sessions/ |
Daily logs detailing time spent, topics covered, and focus quality. |
mocks/ |
Records of mock exam performance, including error types and score trends. |
flashcards/ |
Markdown files formatted for spaced repetition review cycles. |
Gaokao (高考) Exam Prep Assistant Advanced Features
- Automated ROI-first topic prioritization based on point-per-hour potential analysis.
- Dynamic study plan adjustment that responds to mock exam performance and burnout monitoring.
- Province-specific curriculum adaptation for 3+1+2 and 3+3 exam formats.
- Integrated wellbeing tracking to prevent student burnout during the intense preparation cycle.
- Multi-user type adaptation providing different interfaces for students, parents, or professional tutors.
SKILL.md
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