GitHub
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Databases AI Skills
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GitHub
repo-project-sync-workflow
When editing KINTSUGI notebook modules (Kdecon, Kstitch, Kreg, etc.), always edit the main repo first then sync to project folders
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GitHub
symbol-selection-statistical
Statistical analysis for selecting trading symbols compatible with mean-reversion/momentum strategies, GARCH risk models, and Markov regime detection
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GitHub
backtest-persistence
Save backtest results to SQLite database for comparison. Trigger when: (1) tracking backtest history, (2) comparing model performance, (3) querying best backtests.
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GitHub
reward-function-hold-bias
Fix HOLD bias in RL reward function. Trigger when: (1) model learns to always HOLD, (2) trade rate is too low (
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GitHub
pypi-collision-fix
Fix PyPI package name collisions when local package name exists on PyPI
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GitHub
crypto-pair-fee-optimization
Analysis of crypto-to-crypto vs crypto-to-fiat (USD) trading on Alpaca for fee optimization and risk considerations
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GitHub
risk-sizing-not-blocking
Risk managers should SIZE trades, not BLOCK them. Trigger when: (1) capital manager blocks trades, (2) risk manager rejects orders, (3) trades fail due to 'exceeds limit', (4) components don't work in harmony.
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GitHub
selection-data-caching
SUPERSEDED by persistent-cache-gap-filling (v2.8.0). Cache data during symbol selection for instant repeat runs.
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GitHub
hpc-dev-testing-workflow
Efficient development testing workflow for HPC environments with VS Code tunneling. Trigger: testing code changes on HPC, development workflow with external data directories, VS Code Remote SSH development
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GitHub
backtest-datetime-visualization
Converting backtest visualizations from bar indices/timesteps to actual datetime axes for clearer time context
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GitHub
data-source-priority
Ensure Alpaca API is used for quality data, not yfinance fallback. Trigger when: (1) crypto volume filter fails unexpectedly, (2) zero-volume bars in data, (3) API key configuration issues.
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GitHub
notebook-checkpoint-pattern
Pattern for connecting Jupyter notebooks in a pipeline via checkpoint files. Trigger: connecting notebooks, saving outputs, multi-notebook pipelines
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GitHub
model-version-protocol
Model-trader version compatibility protocol: Embed version metadata in checkpoints, validate at load time. Trigger when: (1) training and live trading versions diverge, (2) models fail to load, (3) action inter...
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GitHub
trading-gates-pattern-filter
Pattern filter normalization and multi-gate order entry system
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GitHub
backtest-capital-accounting
Critical fix for backtest capital accounting when equity curves are inflated or drawdown metrics are meaningless
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GitHub
training-improvements-v245
Training improvements: LR warmup, validation intervals, reward weights. Trigger when: (1) training unstable in early epochs, (2) need more validation visibility, (3) model too conservative.
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GitHub
private-function-shim-export
Fix compatibility shims that fail to export private functions. Trigger when: (1) import errors for _prefixed functions after module reorganization, (2) 'cannot import name' errors from shim files, (3) creating...
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GitHub
dashboard-parallel-lists
Dashboard symbol_signals uses parallel lists (symbols[], signal_values[], gate_statuses[]) not dict keyed by symbol. Trigger when: (1) 'list' object has no attribute 'get', (2) .items() on symbol_signals fails.
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GitHub
post-training-workflow
Post-training model validation workflow: gating, backtesting, walk-forward validation, deployment decisions. Trigger after GPU training completes.
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GitHub
repo-reorganization
Python package reorganization with pyproject.toml inside package directory
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GitHub
dataclass-optimization
Python dataclass best practices: slots, frozen, validation. Trigger when optimizing dataclasses or creating config classes.
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GitHub
multi-agent-integration
Integrate Claude agents into training and live trading (v3.0). Trigger when: (1) setting up multi-agent training, (2) adding agent consultation to live trading, (3) configuring orchestrator, (4) understanding a...
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GitHub
network-architecture-sizing
PPO network architecture sizing for trading models. Trigger: (1) model files are unexpectedly small/large, (2) choosing hidden_dims for training, (3) balancing model capacity vs inference speed.
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