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Specific description with trigger conditions
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Specific description with trigger conditions
When editing KINTSUGI notebook modules (Kdecon, Kstitch, Kreg, etc.), always edit the main repo first then sync to project folders
Statistical analysis for selecting trading symbols compatible with mean-reversion/momentum strategies, GARCH risk models, and Markov regime detection
Save backtest results to SQLite database for comparison. Trigger when: (1) tracking backtest history, (2) comparing model performance, (3) querying best backtests.
Fix HOLD bias in RL reward function. Trigger when: (1) model learns to always HOLD, (2) trade rate is too low (
Fix PyPI package name collisions when local package name exists on PyPI
Analysis of crypto-to-crypto vs crypto-to-fiat (USD) trading on Alpaca for fee optimization and risk considerations
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.
SUPERSEDED by persistent-cache-gap-filling (v2.8.0). Cache data during symbol selection for instant repeat runs.
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
Converting backtest visualizations from bar indices/timesteps to actual datetime axes for clearer time context
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.
Pattern for connecting Jupyter notebooks in a pipeline via checkpoint files. Trigger: connecting notebooks, saving outputs, multi-notebook pipelines
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...
Pattern filter normalization and multi-gate order entry system
Critical fix for backtest capital accounting when equity curves are inflated or drawdown metrics are meaningless
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.
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...
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.
Post-training model validation workflow: gating, backtesting, walk-forward validation, deployment decisions. Trigger after GPU training completes.
Python package reorganization with pyproject.toml inside package directory
Python dataclass best practices: slots, frozen, validation. Trigger when optimizing dataclasses or creating config classes.
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...
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.
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.