LuckyBall v3.0 merges Chinese Bazi metaphysics and Pointwise Random Forest models with AR(1) mean-reversion to analyze and forecast Shuangseqiu lottery numbers.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install luckyball
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 luckyball 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 LuckyBall (Shuangseqiu Lucky Number Predictor)?
LuckyBall v3.0 is a highly sophisticated, multi-paradigm analysis and prediction tool designed to run seamlessly as part of your Openclaw Skills library. The tool uniquely bridges ancient Chinese cosmological frameworks—such as Bazi (Eight Characters), Heavenly Stems, Earthly Branches, Nayin Five Elements, and Solar Terms (Jieqi)—with modern machine learning architectures. By converting metaphysics into structured features, the system attempts to uncover latent statistical regularities in historically randomized lottery draws.
While designed as a cultural data science experiment and entertainment utility, the engineering underlying this skill is remarkably robust. It features an ensemble model utilizing a Pointwise Ranking Random Forest classifier for red ball estimation alongside an Auto-Regressive AR(1) Mean Reversion model for blue balls. Supported by rolling backtests and automated result parsing, it offers a deep demonstration of how to map highly qualitative ancient algorithms into a rigorous, modern machine learning feature space.
LuckyBall (Shuangseqiu Lucky Number Predictor) Use Cases
- Astrological Feature Engineering Study: Explore how traditional Ganzhi, Wuxing, and Shishen concepts can be transformed into a 37-dimensional numerical feature matrix for modeling.
- Hybrid Model Experimentation: Benchmark the performance of standard Pointwise ranking algorithms against a combination of Random Forest and AR(1) mean-reversion modeling.
- Automated Prediction Review: Utilize local file access within Openclaw Skills to automatically parse historical logs, execute WebSearch hooks to grab live lottery draws, and compare prediction accuracy.
- Stateful Memory Audits: Maintain persistent logs of multi-agent execution results, tracking hit rates and model accuracy over long historical horizons inside centralized markdown indexes.
How LuckyBall (Shuangseqiu Lucky Number Predictor) Works
- Temporal-Astro Conversion: Upon receiving a user date/time query, the system uses a modified Zeller's congruence algorithm to calculate the Julian Day Number, mapping Gregorian dates to exact Heavenly Stems, Earthly Branches, and Bazi calendar pillars.
- Feature Extraction Pipeline: The converted Bazi details are processed into 37 distinct feature dimensions, encompassing chronological splits, solar terms, Hidden Stems, Nayin properties, Shishen relationships, and element frequencies.
- Red Ball Pointwise Ranking: Rather than predicting static slot distributions, a Pointwise Random Forest classifier (300 trees, depth 10) scores each individual red ball (1 to 33) based on its astrological and chronological features, selecting the top 6 scoring candidates.
- Blue Ball Auto-Regressive Ensemble: For the blue ball prediction, the skill balances a Random Forest model with an Auto-Regressive AR(1) Mean Reversion model ($blue_final = 0.4 \times RF + 0.6 \times AR$) built around a calculated historical negative autocorrelation coefficient ($r \approx -0.72$).
- WebSearch Verification Hook: Before outputting new predictions, the engine uses Openclaw Skills web search modules to retrieve the actual winning numbers of the latest draw, verifying the accuracy of the preceding prediction cycle.
- State Persistence: The verification results are evaluated, and the new five sets of predictions are logged directly into
luckyball-predictions.mdand indexed insideMEMORY.mdto ensure continuous data tracking.
LuckyBall (Shuangseqiu Lucky Number Predictor) Setup
To deploy and operate the LuckyBall predictive analytics engine, configure your environment using the following steps:
- Ensure your AI agent workspace has the necessary libraries installed for astrological and statistical computing:
pip install pandas scikit-learn numpy requests beautifulsoup4
- Initialize the file structure for persistence and history verification under your workspace memory path:
mkdir -p "C:\\Users\\a\\.claude\\projects\\C--Users-a-luspace\\memory\\"
touch "C:\\Users\\a\\.claude\\projects\\C--Users-a-luspace\\memory\\luckyball-predictions.md"
- Launch the prediction execution cycle directly inside your agent console using the designated date/time format:
/luckyball 2026-04-30 20:00
LuckyBall (Shuangseqiu Lucky Number Predictor) Data Schema & Taxonomy
The LuckyBall skill models its predictions across a 37-dimensional mathematical schema, integrating standard chronological features with metaphysical indexes:
37-Dimension Feature Matrix Schema
| Feature Group | Specific Fields | Dimensions | Data Type & Range |
|---|---|---|---|
| Chronological | year, month, day, weekday, day_of_year |
5 | Integer representations of date splits |
| Solar Term Offset | Distance to the nearest solar term node | 1 | Float representing absolute days to next Jieqi |
| Stems & Branches | Ganzhi values for Year, Month, Day, Hour pillars | 8 | Heavenly Stems (0-9), Earthly Branches (0-11) |
| Wuxing Main Qi | Wood, Fire, Earth, Metal, and Water counts | 5 | Frequency counts of primary elements |
| Hidden Stems | Hidden Wuxing frequencies within Branches | 5 | Frequency counts of Canggan elements |
| Nayin | Sound-based elemental representations of pillars | 4 | Integer-mapped values (0-4) |
| Shishen | Astrological Pillar Stems relative to Day Master | 3 | Integer-mapped relationships (0-9) |
| Yinyang Polarities | Polarities of Year and Day Master stems | 2 | Binary flag (0 = Yin, 1 = Yang) |
| Candidate Attributes | Numerical value, Ganzhi property, Wuxing, Parity | 4 | Integer and categorical flags mapping candidates 1-33 |
Persistence Schema (luckyball-predictions.md)
Every prediction is tracked using a markdown database format with the following headers:
- Draw Period: The specific lottery draw code identifier.
- Predictions: Five discrete groups containing 6 Red Balls and 1 Blue Ball.
- Verification State: Stamped as
PendingorComparedwith matched ball counts (e.g.,3/6 Red,✓ Blue).
LuckyBall (Shuangseqiu Lucky Number Predictor) Advanced Features
- Automated Walk-Forward Backtesting: Implements progressive rolling validation routines starting from historic period 60 to verify algorithm hit rates against standard random expectation limits.
- Automated Web-Parsing Loop: Hooks into search engines via Openclaw Skills web features to pull latest winning numbers and compute immediate accuracy verification matrices.
- Dual-Model Ensemble Framework: Integrates physical auto-regressive filters ($r \approx -0.72$) with non-linear machine learning classifiers to balance predictions.
- Bazi Calendrical Compiler: Features a lightweight algorithm to parse and map Gregorian timestamps directly into highly complex astronomical Ganzhi components.
- Stateful Memory Vault Integration: Synchronizes predictive execution records directly with local markdown databases, allowing multi-session history tracking and automatic hit reporting.
SKILL.md
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