Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add kayzaa/k.i.t.-bot --skill ai-predictorgit clone --depth 1 https://github.com/kayzaa/k.i.t.-botWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/kayzaa/k.i.t.-bot/ai-predictor)<a href="https://agentmods.dev/skills/kayzaa/k.i.t.-bot/ai-predictor"><img src="https://agentmods.dev/badge/skills/kayzaa/k.i.t.-bot/ai-predictor/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/kayzaa/k.i.t.-bot/ai-predictor"><img src="https://agentmods.dev/badge/skills/kayzaa/k.i.t.-bot/ai-predictor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.00580 |
| Opus 5 | $0.00000 | $0.00290 |
| Sonnet 5 | $0.00000 | $0.00116 |
| Haiku 4.5 | $0.00000 | $0.00058 |
Grade A, and why
ai-predictor scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to ai-predictor — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Market Predictor
K.I.T.'s brain for price predictions - Machine Learning that WORKS!
Features
🔮 LSTM Neural Networks
- Time Series Prediction mit Deep Learning
- Multi-Step Forecasting (1h, 4h, 24h, 7d)
- Attention Mechanisms für wichtige Patterns
📊 Feature Engineering
- Technical Indicators (RSI, MACD, Bollinger, 50+ mehr)
- Volume Profile Analysis
- Order Flow Imbalance
- Funding Rates (Perps)
- Open Interest Changes
🎯 Confidence Scoring
- Monte Carlo Dropout für Uncertainty Estimation
- Ensemble Models für robustere Predictions
- Dynamische Confidence basierend auf Volatilität
🏆 Model Performance
- Rolling Backtests
- Walk-Forward Optimization
- Real-time Model Retraining
Usage
from ai_predictor import MarketPredictor
predictor = MarketPredictor()
# Single prediction
prediction = await predictor.predict(
symbol="BTC/USDT",
timeframe="1h",
horizon=24 # hours ahead
)
print(f"Price: ${prediction.price:.2f}")
print(f"Direction: {prediction.direction}") # UP/DOWN/NEUTRAL
print(f"Confidence: {prediction.confidence:.1%}")
print(f"Range: ${prediction.low:.2f} - ${prediction.high:.2f}")
# Batch predictions
predictions = await predictor.predict_batch(
symbols=["BTC/USDT", "ETH/USDT", "SOL/USDT"],
timeframe="4h",
horizon=168 # 1 week
)
Models
| Model | Use Case | Accuracy |
|---|---|---|
| LSTM-Attention | Short-term (1-24h) | ~65% direction |
| Transformer | Medium-term (1-7d) | ~58% direction |
| XGBoost Ensemble | Volatility Prediction | MAE < 2% |
| CNN-LSTM | Pattern Recognition | ~62% breakouts |
Configuration
ai_predictor:
models:
lstm:
layers: [128, 64, 32]
dropout: 0.2
attention: true
ensemble_size: 5
features:
technical: true
orderflow: true
sentiment: true # requires sentiment-analyzer
training:
lookback: 168 # hours
retrain_interval: 24h
min_samples: 1000
Dependencies
- tensorflow>=2.15.0
- scikit-learn>=1.3.0
- ta-lib (technical analysis)
- numpy, pandas
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 92 lines · 0 tokens per session scan A 47a451716722
ai-predictor is a skill published in the GitHub repository kayzaa/k.i.t.-bot (5 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 580 tokens. A static security scan graded it A with 0 findings. It is 100% identical to ai-predictor, differing in 0 lines, and is treated as a copy.
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