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Found 105 Skills
Pipeline state management for Goldsky Turbo — pause, resume, restart, and delete commands with their rules and safety behavior. Use this skill when the user asks: will deleting my pipeline lose the data already in my postgres/clickhouse table, how do I pause a pipeline while doing database maintenance, how do I restart from block zero to reprocess all historical data, can I update a running streaming pipeline in place or do I have to delete and redeploy, will resuming a paused pipeline pick up from where it left off (checkpoint), how do I re-run a completed job pipeline from the beginning, can I pause or restart a job-mode pipeline. Also covers what happens to checkpoint state on delete, and job auto-deletion 1 hour after termination. For actively diagnosing why a pipeline is broken or erroring, use /turbo-doctor instead.
Use this skill when building real-time data pipelines, stream processing jobs, or change data capture systems. Triggers on tasks involving Apache Kafka (producers, consumers, topics, partitions, consumer groups, Connect, Streams), Apache Flink (DataStream API, windowing, checkpointing, stateful processing), event sourcing implementations, CDC with Debezium, stream processing patterns (windowing, watermarks, exactly-once semantics), and any pipeline that processes unbounded data in motion rather than data at rest.
Track progress across sessions using SESSION.md with git checkpoints and concrete next actions. Converts IMPLEMENTATION_PHASES.md into trackable session state. Use when: resuming work after context clears, managing multi-phase implementations, or troubleshooting lost context.
Anti-footgun protocol for AI-assisted coding. Always active during coding tasks to enforce simplicity-first thinking, surface assumptions, and prevent scope creep. Explicit checkpoints available via "cg pre", "cg post", "cg simplify". Triggers on: any coding task, code review requests, refactoring, or when user says "cg" or "check".
Refactor PyTorch code to improve maintainability, readability, and adherence to best practices. Identifies and fixes DRY violations, long functions, deep nesting, SRP violations, and opportunities for modular components. Applies PyTorch 2.x patterns including torch.compile optimization, Automatic Mixed Precision (AMP), optimized DataLoader configuration, modular nn.Module design, gradient checkpointing, CUDA memory management, PyTorch Lightning integration, custom Dataset classes, model factory patterns, weight initialization, and reproducibility patterns.
Train custom TTS voices for Piper (ONNX format) using fine-tuning or from-scratch approaches. Use when creating new synthetic voices, fine-tuning existing Piper checkpoints, preparing audio datasets for TTS training, or deploying voice models to devices like Raspberry Pi or Home Assistant. Covers dataset preparation, Whisper-based validation, training configuration, and ONNX export.
Create a context handoff file, pausing work mid-phase, stopping work temporarily, or creating a checkpoint for session resumption. Triggers include "pause work", "stop work", "create handoff", "save progress", and "pause session".
LangGraph checkpointing and persistence. Use when implementing fault-tolerant workflows, resuming interrupted executions, debugging with state history, or avoiding re-running expensive operations.
Execute implementation plans with checkpoint validation, progress tracking, and quality gates. Use for task implementation, plan execution, progress tracking. Skip if no plan exists.
This skill should be used at natural checkpoints (after completing complex tasks, at session end, or when friction occurs) to reflect on skill and process execution and identify targeted improvements. Use when experiencing confusion, repeated failures, or discovering new patterns that should be codified into skills for smoother future operation.
Manages session state and context handoffs for multi-session projects using the Session Handoff Protocol. Creates and maintains SESSION.md to track phase progress, git checkpoints, and next actions across context clears. Integrates with project-planning skill to convert IMPLEMENTATION_PHASES.md into trackable session state. Use when starting new projects after planning, resuming work after context clear, or managing complex multi-phase implementations. Keywords: session management, SESSION.md, session handoff protocol, context handoff, multi-session projects, phase tracking, git checkpoints, session state tracking, resume work, context clear, phase progress tracking, implementation phases, verification stage, debugging stage, next action tracking, work continuity, session recovery, context management, phased implementation tracking
When a user asks how long a task will take, requests a time estimate, or before starting any non-trivial coding task, immediately run scripts/estimate_task.py to analyze the codebase scope and provide a data-driven time estimate. Show the estimate breakdown, risk factors, and checkpoint recommendations without asking.